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Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks


Abstract and Figures

Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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Conn: A Functional Connectivity Toolbox for Correlated
and Anticorrelated Brain Networks
Susan Whitfield-Gabrieli and Alfonso Nieto-Castanon
Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional archi-
tecture of the human brain. However, valid statistical analysis used to identify such networks must address sour-
ces of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources.
We have developed a functional connectivity toolbox Conn ( that implements
the component-based noise correction method (CompCor) strategy for physiological and other noise source re-
duction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the re-
sidual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard
functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis
for resting state as well as task-related data. Compared to methods that rely on global signal regression, the
CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the
global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest
(ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression
analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional con-
nectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with
examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate
that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree
of interscan reliability for many fcMRI measures.
Key words: brain connectivity; CompCor functional connectivity; intrinsic connectivity; noise; resting state
Functional connectivity has been broadly defined to
be the statistical association (or temporal correlation)
among two or more anatomically distinct regions (or remote
neurophysiological events) (Friston et al., 1994; Horwitz,
2003; Salvador et al., 2005). Numerous methods have been
used to investigate these temporal correlations, including in-
dependent component analysis (ICA) (e.g., Beckmann et al.,
2005; Calhoun et al., 2001, 2004), seed-driven functional con-
nectivity magnetic resonance imaging (fcMRI) (e.g., Biswal
et al., 1995; Fox et al., 2005; Greicius et al., 2003), and psycho-
physiological interactions used to characterize activation in a
particular brain region in terms of the interaction between
the influence of another area and an experimental parameter
(Friston et al., 1997; Gitelman et al., 2003). Functional connec-
tivity has been investigated in block (e.g., Hampson et al.,
2002; Koshino et al., 2005) and event-related (Aizenstein
et al., 2009; Fox et al., 2006; Rissman et al., 2004; Siegle et al.,
2007) fMRI activation designs. Further, functional connectivity
is evident during rest in the absence of task-related activation
(Biswal et al., 1995).
Low-frequency resting state networks ( <0.1 Hz) reveal co-
herent, spontaneous fluctuations that delineate the functional
architecture of the human brain (Biswal et al., 1995, 2010;
Buckner et al., 2008; Fox et al., 2005; Fox and Raichle, 2007).
Such networks were initially discovered for the motor system
(Biswal et al., 1995), but have also been discovered for both
task-positive and task-negative (i.e., default, Raichle et al.,
2001) neural systems (Fox et al., 2005; Fransson, 2005; Kelly
et al., 2008; Uddin et al., 2009). Resting state networks have
been shown to be robust and reliable (Chen et al., 2008; Dam-
oiseaux et al., 2006; Shehzad et al., 2009; Zuo et al., 2010a,
2010b), and to exist in infants (Fransson et al., 2007), during
sleep (Fukunaga et al., 2006; Horovitz et al., 2008), under
light sedation (Greicius et al., 2008) and under anesthesia in
primates (Vincent et al., 2007). Such networks have been asso-
ciated with individual differences in healthy people (Mennes
Department of Brain and Cognitive Sciences, Martinos Imaging Center at McGovern Institute for Brain Research, and Poitras Center for
Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Volume 2, Number 3, 2012
ªMary Ann Liebert, Inc.
DOI: 10.1089/brain.2012.0073
et al., 2010). Because rest has no behavioral demands, resting
state connectivity is particularly useful for characterizing
functional brain network differences in pediatric and clinical
populations, such as schizophrenia (Whitfield-Gabrieli et al.,
2009; Whitfield-Gabrieli and Ford 2012), ADHD (Castellanos
et al., 2008), autism (Weng et al., 2010), depression (Greicius
et al., 2007; Hamilton et al., 2011), bipolar disorder (Chai
et al., 2012), and Alzheimer’s disease (Buckner et al., 2009;
Greicius et al., 2004; Wang et al., 2007).
The two most common analytical approaches toward ana-
lyzing resting state functional connectivity (RSFC) data are
ICA (e.g., Beckmann et al., 2005, 2009; Greicius et al., 2007;
Stevens et al., 2009) and seed-driven RSFC (e.g., Biswal
et al., 1995; Castellanos et al., 2008; Greicius et al., 2003; Fox
et al., 2005). In seed-driven RSFC analysis, Pearson’s correla-
tion coefficients are calculated between the seed time course
and the time course of all other voxels, after which correlation
coefficients are typically converted to normally distributed
scores using Fisher’s transform to allow for second-level Gen-
eral Linear Model analysis. Correlation maps often depend
on the specific location of the seed, so that seed-driven
RSFC has been used to dissociate functionally and anatomi-
cally heterogeneous regions of interest (Di Martino et al.,
2008; Margulies et al., 2007; Roy et al., 2009; Uddin et al.,
2010), and to delineate functional topography in the brain
by sharp transitions in correlation patterns that signal func-
tional boundaries across cortex (Cohen et al., 2008).
In functional connectivity analysis, it is critical to appropri-
ately address noise in order to avoid possible confounding ef-
fects (spurious correlations based on non-neuronal sources).
Standard methods dealing with blood oxygen level-depend-
ent (BOLD) contrast signal noise sources that may be appro-
priate in the context of the estimation of task- or condition-
dependent BOLD signal responses (e.g., regression of subject
movement parameters in standard functional analysis) may
not suffice in the context of the estimation of functional con-
nectivity measures. For activation studies, the risk of only
partially removing BOLD signal noise sources is typically a
potential decrease of sensitivity (increasing type II errors),
whereas for resting connectivity studies, the risk is a potential
decrease of validity (increasing type I errors). Therefore, a
more conservative approach to controlling the effects of
BOLD signal noise sources is warranted in the context of
functional connectivity analysis compared with that of stan-
dard functional analysis. In Chai et al. (2012) we showed
how a method for reducing spurious sources of variance in
BOLD and perfusion-based fMRI, the anatomical compo-
nent-based noise correction method (aCompCor) (Behzadi
et al., 2007), can be particularly useful in the context of
fcMRI analysis, increasing not only the validity, but also the
sensitivity and specificity of these analyses. Compared to
methods that subtract global signals from noise regions of
interest (ROIs), the CompCor method is more flexible in its
characterization of noise. It models the influence of noise as
a voxel-specific linear combination of multiple empirically es-
timated noise sources, which are estimated from the variabil-
ity in BOLD responses within noise ROIs. This is particularly
appropriate for fMRI noise sources as cardiac and respiratory
effects do not have a common spatial distribution in their ef-
fects (e.g., cardiac effects are particularly visible near vessels
and respiratory effects appear more globally and stronger
near edges in the image). Removal of this richer characteriza-
tion of the range of voxel-specific noise effects and additional
movement and possible task-related covariates, together with
temporal filtering and windowing of the resulting BOLD sig-
nal at each voxel, provides increased protection against pos-
sible confounding effects in RSFC without introducing
artifactual biases in the estimated connectivity measures.
In addition to physiological artifacts, head motion artifacts
have been shown to significantly influence intrinsic func-
tional connectivity measurements (Satterthwaite et al., 2012;
Van Dijk et al., 2012). Moreover, it has been recently demon-
strated that artifacts in the functional time series may result in
substantial changes in RSFC data despite standard compen-
satory regression of motion estimates from the data (Power
et al., 2012). These findings suggest that rigorous artifact re-
jection in addition to motion regression is especially prudent
for valid interpretation of RSFC. Conn is seamlessly interoper-
able with quality assurance and artifact rejection software, art
(, such that a ma-
trix of outlier, artifactual time points saved by art may be eas-
ily entered as first-level covariates in Conn. The combination
of the Conn’s implementation of the CompCor method of
noise reduction along with the efficient rejection of motion
and artifactual time points allows for better interpretation
of functional connectivity results for both correlated and anti-
correlated networks.
With the increase in popularity of linear functional connec-
tivity analysis, there is still a large degree of variability in the
exact methods used for the analysis of fcMRI data, with dif-
ferences in noise-preprocessing steps as well as differences
in the characterization of fcMRI measures across labs,
which can complicate the interpretation and comparison of
fcMRI results across different studies. To provide a common
framework for the analysis of fcMRI data, we have developed
and made publicly available the Conn toolbox (www.nitrc
.org/projects/conn). The toolbox is compatible with most
data formats—including nifty (nii) and analyze images
(img)—and implements all the processing steps necessary
to perform fcMRI analysis—including spatial preprocessing
of BOLD signal and anatomical volumes, CompCor removal
of noise sources, first-level estimation of fcMRI measures, and
second-level random effect analysis—while maintaining the
flexibility to define and estimate different forms of fcMRI
analysis. The implementation includes standard fcMRI analy-
sis, such as estimation of seed-to-voxel and ROI-to-ROI func-
tional correlations, as well as other forms of fcMRI analysis,
such as bivariate regression analysis, semipartial correlation
and multivariate regression analysis of multiple ROI sources,
graph theoretical analysis of brain networks, and novel voxel-
to-voxel analysis of functional connectivity. In the following
sections we first describe in detail the methods used by the
toolbox to compute different functional connectivity mea-
sures (Functional Connectivity Metrics in Conn section), and
then we illustrate examples of use and reliability estimates
of all of these fcMRI measures indicating their validity as po-
tentially useful neuromarkers (Illustration of Functional Con-
nectivity Analysis in Conn section).
Functional Connectivity Metrics Implemented in Conn
The analysis steps involved in the computation of fcMRI
measures, as implemented in the Conn toolbox, are illustrated
in Figure 1. The following sections explain these steps in
Spatial preprocessing
Spatial preprocessing steps for functional connectivity
analysis do not typically differ from those used in the context
of functional activation analysis. Most fcMRI studies include
slice-timing correction, realignment, coregistration and/or
normalization, and spatial smoothing. In addition to these
steps, the toolbox employs segmentation of gray matter,
white matter, and cerebrospinal fluid (CSF) areas for optional
use during removal of temporal confounding factors. All spa-
tial preprocessing steps are implemented using SPM8 (Well-
come Department of Imaging Neuroscience, London, UK; www; however, users can choose to omit
this step and use their own spatial preprocessing pipeline.
Temporal processing (treatment of temporal
confounding factors)
Several studies have emphasized the importance of additional
preprocessing steps in fcMRI studies (e.g., Birn et al., 2006; Fox
et al., 2005, 2009; Power et al., 2012; Van Dijk et al., 2010; Weis-
senbacher et al., 2009), including—but not limited to—band-
pass filtering and the inclusion of estimated subject motion
parameters, artifacts, respiratory and cardiac signals, global
BOLD signal, and BOLD signals in white matter and CSF
areas as additional covariates. The main concern is that move-
ment and physiological noise sources can potentially induce spu-
rious correlations among distant voxels, increasing the chance of
false positives and confounding the interpretation of fcMRI
results. These additional preprocessing steps are designed to
help mitigate the impact of motion and physiological noise fac-
tors, increasing the validity and the robustness of fcMRI analysis.
The toolbox allows the specification of an arbitrary set of
possible temporal confounding factors, which can be defined
from indirect sources, as subject- and session-specific time se-
ries (e.g., estimated subject movement parameters and arti-
facts, cardiac or respiratory rates, and possible task effects;
these are indicated in Figure 1 as Design matrix), as well as
BOLD signals obtained from subject-specific noise ROIs
(white matter and CSF masks, as well as optionally additional
user-defined ROIs). The toolbox implements an anatomical
aCompCor strategy (Behzadi et al., 2007) in which a user-
defined number of orthogonal time series are estimated
using principal component analysis (PCA) of the multivariate
BOLD signal within each of these noise ROIs. This strategy
generalizes the common practice of extracting the average
BOLD time series from one or several seeds located within
the white matter and/or CSF areas. In addition, and for
each original temporal confounding factor, first- and higher-
order derivatives of the associated time series can also be de-
fined by the user as additional confounding factors (e.g., Fox
et al., 2005). Each of the defined temporal confounding factors
is then regressed from the BOLD time series at each voxel (sep-
arately for each session), and the resulting residual time series
are band-pass filtered. In particular, the removal of temporal
confounding factors, from an observed signal BOLD*(v,t)at
voxel vand time t, takes the following form:
bkn()dkn (t)
FIG. 1. Schematic representation of fcMRI analysis steps. BOLD, blood oxygen level-dependent; CSF, cerebrospinal fluid;
fcMRI, functional connectivity magnetic resonance imaging; ROI, region of interest.
where c
(t) represents Ntemporal confounds defined
explicitly through subject- and session-specific time series or
implicitly as temporal derivatives of these signals (e.g., sub-
ject motion parameters); d
(t) represents those confounds de-
fined implicitly from Knoise ROIs, each characterized by M
component time series from each noise ROI (e.g., average sig-
nal, principal components, or temporal derivatives of these
signals within white matter and CSF areas); and a
(v) and
(v) represent voxel-specific weights for each of these fac-
tors estimated using linear regression (see Appendix A.1 for
further details of these preprocessing steps).
The toolbox graphical user interface (GUI) encourages
users to explore the effect of these additional preprocessing
steps by displaying the histogram of voxel-to-voxel func-
tional connectivity values (correlation coefficients between
the BOLD time series of a random subset of voxels) before
and after regression of the selected temporal confounding fac-
tors. This display typically shows a heavily skewed distribu-
tion of connectivity values in the presence of motion and/or
physiological noise sources, which is approximately centered
and normalized by the regression process (Fig. 2). This explo-
ration can help users optimize the choice of preprocessing
steps as well as help detect anomalous subjects/sessions.
ROI time series
Functional connectivity measures are typically computed
either between every pair of voxels (voxel-to-voxel analysis),
between a seed voxel or area and every other voxel (seed-to-
voxel analysis), or between each pair of seed areas (ROI-to-
ROI analysis). The toolbox allows the definition of seed
areas using standard practices, including individual mask
image volumes [where an ROI is defined by all voxels with
values above zero, e.g., WFU pickatlas files (Maldjian et al.,
2003) or functional mask files defined using SPM save func-
tionality], text files (listing Montreal Neurological Institute
(MNI) coordinates of ROI voxels), and atlas image volumes
where multiple ROIs can be jointly defined using a single
image volume. Each ROI is characterized by voxels sharing
the same identifier number, for example, talairach atlas (Lan-
caster et al., 2000). ROIs can be defined separately for each
subject (subject-specific ROIs) or jointly across all subjects
(e.g., MNI space).
The average BOLD time series is computed across all the
voxels within each ROI. In addition, the toolbox allows the
extraction of additional temporal components from each
area resulting from a principal component decomposition of
the temporal covariance matrix (as for the noise ROIs
above), as well as the estimation of higher-order temporal de-
rivatives of these original BOLD signals. In general the fol-
lowing ROI time series can be computed from each seed area:
qtnBOLD(,t) (2)
BOLD(m,t): BOLD timeseries at voxel mand time t
: voxels in seed area
m: order of PCA component (0 for straight average)
n: order of temporal derivative (0 for original signal)
In combination with the average BOLD signal within an
ROI, PCA component signals allow multivariate analysis of
functional connectivity patterns. In addition, temporal deriva-
tive BOLD signals when used in combination with multivariate
measures of connectivity (e.g., multivariate regression or semi-
partial correlation measures) allow the exploration of temporal
lags or more complex linear dynamics between two areas.
Linear fcMRI measures
The toolbox focuses on linear measures of functional
connectivity between two sources: zero-lagged bivariate-
correlation and bivariate-regression coefficients, and their
associated multivariate measures, semipartial-correlation and
multivariate-regression coefficients (Table 1). Bivariate correla-
tion and regression coefficients measure the level of linear as-
sociation of the BOLD time series between each pair of
sources when considered in isolation. In contrast, semipartial
correlation and multivariate regression coefficients consider
multiple sources simultaneously and estimate the unique
contribution of each source using a general linear model. In
bivariate and semipartial correlation analyses, effect sizes
represent correlation coefficients (their values squared can
be interpreted as the percentage of the target BOLD signal
variance explained by each source BOLD signal). In bivariate
and multivariate regression analyses, effect sizes represent %
FIG. 2. Effect of temporal preprocessing steps on the distri-
bution of voxel-to-voxel BOLD signal correlation values. The
average distribution (across subjects and sessions) is shown
as thick lines, and its 5% and 95% percentiles are shown as
filled areas. After temporal preprocessing, voxel-to-voxel
functional connectivity estimates show a reduction in bias
and an associated increase in reliability across subjects and
sessions (see text for details).
Table 1. Definition of Linear Measures
of Functional Connectivity
Bivariate regression b=(xtx)1(xty)
Bivariate correlation r=(xtx)1=2b(yty)1=2
Multivariate regression B =(XtX)1(XtY)
Semipartial correlation R =(XtX)1
x and y represent two BOLD time series vectors (centered), X and Y
represent matrices created by concatenating horizontally one or sev-
eral x and y vectors, and the brackets [] represent the operation of
zeroing all the nondiagonal elements in a matrix.
changes in BOLD activity at each target associated with a 1%
change of BOLD activity at each source ROI. Before being en-
tered into second-level between-subjects analysis, a Fisher
transformation (inverse hyperbolic tangent function) is ap-
plied to all bivariate and semipartial correlation measures
in order to improve the normality assumptions of standard
second-level general linear models.
Voxel-to-voxel measures
The toolbox also computes a complete voxel-to-voxel func-
tional correlation matrix for each subject. From the residual
BOLD time series at every voxel within an a priori gray mat-
ter mask (isotropic 2-mm voxels), the matrix of voxel-to-voxel
bivariate correlation coefficients is computed. To minimize
storage and computation requirements (explicit storage of
this matrix could occupy above 300 Gb for each subject),
this matrix is instead characterized without loss of precision
by its eigenvectors and associated eigenvalues (see Appendix
A.2). In addition to downsampling the voxel-to-voxel correla-
tion matrices to any desired target resolution, the toolbox also
computes several voxel-level measures of functional connec-
tivity directly from the original voxel-to-voxel correlation
matrix (see Table 2). Integrated local correlation (ILC) (Desh-
pande et al. 2007) characterizes the average local connectivity
between each voxel and its neighbors. Radial correlation con-
trast (RCC) (Goelman, 2004) characterizes the spatial asym-
metry of the local connectivity pattern between each voxel
and its neighbors. Intrinsic connectivity contrast (ICC) (Mar-
tuzzi et al. 2011) and radial similarity contrast (RSC) (Kim
et al., 2010) are novel measures similar to ILC and RCC
measures, but characterizing the global connectivity pattern
between each voxel and the rest of the brain (instead of the
local connectivity pattern around each voxel). In particular,
ICC characterizes the strength of the global connectivity pat-
tern between each voxel and the rest of the brain, while RSC
characterizes the global similarity between the connectivity
patterns of neighboring voxels. In addition to these measures
the toolbox allows simple and fast implementation of other
user-defined voxel-level fcMRI measures, as long as these
measures can also be characterized as a function of the eigen-
vectors/eigenvalues of the voxel-to-voxel correlation matrix.
The Illustration of Functional Connectivity Analysis in Conn
section [Illustration of voxel-to-voxel analysis (optimal placement
of fcMRI seeds) subsection] illustrates the application of one
such user-defined measure to investigate between-session
similarity of functional connectivity patterns, and Appendix
A.2 describes the characterization of this measure as a func-
tion of the eigenvectors/eigenvalues of the voxel-to-voxel
connectivity matrix.
Task-related and resting state fcMRI
The previous sections characterize the steps necessary to
perform first-level (within-subjects) connectivity analysis of
resting state BOLD time series, as well as time series derived
from the residuals of BOLD time series in block- and event-
related designs after removing modeled task or condition
effects (Fair et al., 2007) (e.g., simply by including these mod-
eled condition effects as additional temporal confounding
factors). The toolbox also allows condition-dependent func-
tional connectivity analysis of block design studies, such as
fcMRI analysis of interleaved resting periods or analysis of
functional connectivity within task blocks. In these cases
and after the session-specific treatment of temporal con-
founds, the BOLD time series is divided into scans associated
with each blocked presentation. To take into account the he-
modynamic delay, block regressors for each condition are
convolved with a canonical hemodynamic response function,
a combination of two gamma functions, and rectified {filtered
to keep the positive part of the original time series; y[n]=
max(0,x[n])}. All of the scans with nonzero effects in the
resulting time series are concatenated for each condition
and across all sessions, weighting each scan by the value of
these time series. Alternatively it is also possible to use a
Hann function (e.g., a ‘‘Hann function’’ window, shaped as
a half cycle of a sine-squared function) instead of the rectified
hrf function that more heavily de-weights the scans at the be-
ginning and end of each block, as well as to omit any form of
within-block weighting. In the case of block design studies, it
is also recommended to include standard task regressors
(block regressors convolved with a canonical hemodynamic
response function) and their first-derivative terms as addi-
tional covariates in the temporal preprocessing step. This
step helps avoid possible between-condition main effects
from affecting within-condition connectivity estimates in
the presence of possible voxel-specific differences in hemody-
namic delay. Resting state analysis is treated like a special
case of task-specific analysis where only one condition span-
ning the entire scanner acquisition length is considered.
Second-level analysis
Following the computation for each subject of seed-to-
voxel connectivity maps, ROI-to-ROI connectivity matrices,
and voxel-level fcMRI measures from voxel-to-voxel analysis,
each one of these measures can then be entered into a second-
level general linear model to obtain population-level estimates
and inferences. Specific hypotheses can then be tested using
between-subjects contrasts (e.g., comparing functional con-
nectivity patterns between two groups of subjects), between-
condition contrasts (e.g., comparing task- or condition-specific
connectivity patterns between two conditions), between-
source contrasts (e.g., comparing functional connectivity
Table 2. Voxel-Level Functional Connectivity
MRI Measures Derived from the Voxel-to-Voxel
Connectivity Matrix r(x,y)
Integrated local correlation P
hr(x y) r(x, y)
Radial correlation contrast P
hr(x y) q
r(x, y)
Global correlation strength 1
jr(x, y)j2
Radial similarity contrast 1
r(x, y)
Integrated local correlation and radial correlation contrast charac-
terize properties of the local pattern of connectivity (between each
voxel and its neighbors). Global correlation strength and radial sim-
ilarity contrast characterize properties of the global pattern of connec-
tivity (between each voxel and the entire brain).
x and y represent the spatial locations of two arbitrary voxels, h
represents a Gaussian convolution kernel of width r, and Orepre-
sents the set of all brain voxels.
patterns between two seeds), and combinations of these con-
trasts (e.g., testing group by condition interactions). False pos-
itive control in ROI-to-ROI analysis is implemented using
uncorrected or false discovery rate (FDR)-corrected p-values.
Uncorrected p-values are appropriate when the researcher’s
original hypotheses involve only the connectivity between
two a priori ROIs and FDR-corrected p-values are appropriate
when the researcher’s original hypotheses involve the connec-
tivity between larger sets of ROIs and do not specify a priori
which ROIs are expected to show an effect. False positive
control in voxel-level analysis is implemented through a com-
bination of a voxel-level height threshold (defined by uncor-
rected or FDR-corrected voxel-level p-values) and a cluster-
level extent threshold (defined by uncorrected, family wise
error [FWE]-corrected, or FDR-corrected cluster-level p-values).
Graph theoretical analysis
The toolbox also computes several graph theoretical mea-
sures (Achard and Bullmore, 2007; Bullmore and Sporns,
2009; Latora and Marchiori, 2001; Watts and Strogatz, 1998)
characterizing structural properties of the estimated ROI-to-
ROI functional connectivity networks, and allows users
to perform group-level analysis of these measures. Each
subject-specific ROI-to-ROI connectivity matrix is thresh-
olded at a fixed level. This threshold can be based on raw con-
nectivity values, normalized z-scores, or percentile scores
(resulting in graphs with fixed network-level cost). Supra-
threshold connectivity values define an adjacency matrix
characterizing a graph with nodes associated with ROIs,
and edges associated with the strength of functional connec-
tivity among these ROIs. For each node nin a graph G,cost
is defined as the proportion of connected neighbors, global
efficiency is defined as the average inverse shortest path dis-
tance from node nto all other nodes in the graph, and local ef-
ficiency is defined as the average global efficiency across all
nodes in the local subgraph of node n(the subgraph consist-
ing only of nodes neighboring node n). In addition, equiva-
lent network-level summary measures can be defined by
averaging across all nodes of the network (Table 3). Popula-
tion-level inferences on these graph theoretical measures are
obtained using a second-level general linear model as in the
fcMRI analysis above.
Illustration of Functional Connectivity Analysis in Conn
In this section several examples of fcMRI analysis per-
formed with Conn are illustrated. These examples are chosen
to illustrate some of the standard approaches available for
RSFC analysis, as well as to demonstrate the reliability of
the functional connectivity measures computed by the Conn
toolbox. The analyses were based on a publically available
resting state dataset (NYU CSC TestRetest dataset, www, which has been previously ana-
lyzed in detail demonstrating the reliability of functional con-
nectivity measures (Shehzad et al., 2009). This dataset consists
of echo planar imaging (EPI) images of 25 participants col-
lected on three occasions: (1) the first resting state scan in a
scan session, (2) 5–11 months after the first resting state
scan, and (3) about 30 ( <45) min after the second resting
state scan. Resting state scans consist of 197 continuous EPI
functional volumes (TR =2000 ms; TE =25 ms; flip angle =90;
39 slices, matrix =64 ·64, FOV =192 mm; isotropic 3-mm
acquisition voxel size).
Preprocessing of BOLD time courses
Spatial preprocessing of functional volumes included re-
alignment, normalization, and smoothing (8-mm FWHM
Gaussian filter), using SPM8 default parameter choices. Ana-
tomical volumes were segmented into gray matter, white
matter, and CSF areas, and the resulting masks were eroded
(one voxel erosion, isotropic 2-mm voxel size) to minimize
partial volume effects. The temporal time series characterizing
the estimated subject motion (three-rotation and three-
translation parameters, plus another six parameters repre-
senting their first-order temporal derivatives), as well as
the BOLD time series within the subject-specific white matter
mask (three PCA parameters) and CSF mask (three PCA
parameters), were used as temporal covariates and removed
from the BOLD functional data using linear regression, and
the resulting residual BOLD time series were band-pass fil-
tered (0.01 Hz <f<0.10 Hz). Figure 2 illustrates the effect of
removing temporal covariates on the distribution of voxel-
to-voxel BOLD signal correlation values. A random subset
of 256 voxels (the same voxels across subjects and sessions)
was used to compute the sample distribution of voxel-to-
voxel BOLD signal correlation values separately for each
subject and session, before and after removal of the defined
temporal covariates.
Estimated voxel-to-voxel correlations using the raw BOLD
signals typically show distributions with some degree of pos-
itive bias, and with large differences between sessions and
subjects. In contrast, after temporal preprocessing, the esti-
mated voxel-to-voxel correlations appear more centered and
with very similar distributions across sessions and subjects.
To quantify this observation, we computed measures of inter-
session reliability of the voxel-to-voxel connectivity measures
from the raw BOLD signal, and compared them with the
Table 3. Definition of Graph Theoretical Measures Characterizing Structural Properties
of Functional Connectivity Networks
ROI-level measures Network-level measures
Cost Cn(G)=1
Global efficiency Eglobal
nm (G)Eglobal(G)=1
Local efficiency Elocal
n(G)=Eglobal(Gn)Elocal (G)=1
(G) represents the shortest path distance between nodes nand min graph G, and jGjrepresents the number of nodes in graph G.
ROI, region of interest.
same measures after temporal preprocessing. The reliability
(average intersession correlation) of the resulting group-
level voxel-to-voxel connectivity estimates between ran-
domly selected voxels was r=0.52 from the raw BOLD signal,
r=0.62 when using subject motion covariates, and r=0.70
when additionally using CompCor method of white matter
and CSF noise covariates, and their corresponding intraclass
correlation coefficients (one-way random effects, Shehzad
et al. 2009) were 0.22, 0.55, and 0.71, respectively. These re-
sults highlight that reliability of group-level voxel-to-voxel
connectivity measures increases dramatically with the addi-
tional methods of noise reduction implemented in the tempo-
ral preprocessing steps of the Conn toolbox, potentially due to
their effect reducing physiological and other noise-dependent
biases on functional connectivity estimates.
Illustration of seed-to-voxel bivariate correlation analysis
A posterior cingulate cortex (PCC) region [a spherical ROI
with MNI coordinates (6,52,40) and radius 10 mm (Fox
et al., 2005)] was used as the seed. The PCC seed shows pos-
itive functional connectivity with a network of default areas
(shown in red in Fig. 3 top) and negative functional connec-
tivity (shown in blue) with task-related regions. In addition,
three separate within-session estimates of the connectivity
strength between the PCC seed and each voxel were com-
puted. These session-specific estimates represent the Fisher-
transformed correlation coefficients for each voxel averaged
across all subjects and converted back to raw correlation coef-
ficient values. Within-session estimates (Fig. 3) show a high
degree of reliability (intersession correlation r=0.95, mean ab-
solute error 0.03) when comparing group-level estimates
of functional connectivity strength across repeated runs or
sessions. Similarly, high interscan reliability (r=0.97) was
found when repeating these analyses using bivariate regres-
sion measures instead of bivariate correlation measures.
Illustration of seed-to-voxel semipartial
correlation analysis
Multivariate seed-to-voxel analysis was also performed to
explore the unique connectivity with the PCC area that is not
mediated by other default network areas. The average BOLD
time series within the PCC area were used as sources of the
FIG. 3. Seed-to-voxel functional connectivity with PCC seed area. Top: Spatial patterns of group-level seed-to-voxel connectivity
measures (bivariate correlation) collapsed across the three sessions available from each subject. Red: positive connectivity, blue:
negative connectivity. Results are thresholded at FWE-corrected cluster-level p<0.05 (with FDR-corrected two-sided p<0.05
height threshold). Bottom: Intersession reliability. Correlations between session-specific estimates ofgroup-level seed-to-voxel con-
nectivity measures, between session 2 and session 1 (5–11-month difference between the sessions), and between session 2 and ses-
sion 3 (30-min difference between the sessions). FWE, family wise error; FDR, falsediscoveryrate; PCC, posterior cingulate cortex.
seed-to-voxel analysis. A multivariate representation of
the activation within three control ROIs—medial prefrontal
cortex (MPFC), left lateral parietal, and right lateral parie-
tal—characterizing, for each ROI, the average BOLD activa-
tion plus four orthogonal components derived from a
principal component decomposition of the within-ROI
BOLD time series were used as control variables. Semipartial
correlation values with the PCC seed were estimated for each
voxel. PCC shows unique positive and negative functional
connectivity with a large network of areas that are not medi-
ated by other default network regions (Fig. 4). In addition,
three separate within-session estimates of the semipartial cor-
relation coefficients between the PCC seed and each voxel
were computed. These session-specific estimates represent
the Fisher-transformed semipartial correlation coefficients
for each voxel averaged across all subjects and converted
back to raw correlation coefficient values. Within-session es-
timates show a high degree of reliability (intersession correla-
tion r=0.82, mean absolute error 0.03) when comparing
group-level estimates of unique functional connectivity
strength across repeated runs or sessions (Fig. 4). Similar
interscan reliability (r=0.88) was found when repeating
these analyses using multivariate regression measures in-
stead of semipartial correlation measures.
Illustration of ROI-to-ROI analysis
This analysis uses the same PCC seed area as the previous
seed-to-voxel analysis, and estimates the ROI-to-ROI func-
tional connectivity (bivariate correlation measure) between
this seed and a set of 84 ROIs defining the Brodmann areas
(talairach atlas; Lancaster et al., 2000). Group-level estimates
of ROI-to-ROI connectivity show a high degree of reliability
(Fig. 5; intersession correlation r=0.99, mean absolute error
0.01). Similar interscan reliability (r=0.98) was found when
repeating these analyses using bivariate regression measures
instead of bivariate correlation measures.
Illustration of graph metrics analysis
The entire matrix of ROI-to-ROI functional connectivity
values (bivariate correlation measure) was computed for
each subject using the Brodmann area ROIs, and thresholded
at a fixed network-level cost value to define an undirected
graph characterizing the entire network of functional connec-
tions between these ROIs. Negative functional connectivity
values were disregarded in these analyses. The network
global and local efficiency was computed for a range of pos-
sible cost value (K) thresholds and compared to a random
graph and to a lattice graph with the same network size
FIG. 4. Seed-to-voxel analysis of unique connectivity with PCC seed area (controlled by MPFC, left and right LP). Top: Spatial
pattern of group-level effects of the semipartial correlation coefficients collapsed across the three sessions available from each sub-
ject. Results are thresholded at FWE-corrected cluster-level p<0.05 (with FDR-corrected two-sided p<0.05 height threshold). Bot-
tom: Intersession reliability of semipartial correlation measures with PCC seed. MPFC, medial prefrontal cortex; LP, lateral parietal.
and cost (Fig. 6). Small world properties were observed in the
range of costs 0.05 <K<0.25, where global efficiency is greater
than that of a lattice graph and local efficiency is greater than
that of a random graph (Achard and Bullmore, 2007). Using
an intermediate K=0.15 cost threshold level, the global effi-
ciency of each ROI, a measure of the centrality of each ROI
within the network, was computed and averaged across all
subjects. This measure showed a high degree of reliability
when comparing session-specific estimates of global effi-
ciency across repeated runs or sessions (Fig. 6; intersession
correlation r=0.95, mean absolute error 0.01). Similar inter-
scan reliability was found for other graph theoretical mea-
sures (local efficiency r=0.90; cost r=0.95).
Illustration of voxel-to-voxel analysis (RSC)
This analysis investigates the similarity, at each voxel, be-
tween the global functional connectivity patterns of this
voxel and those of its neighbors. The voxel-to-voxel func-
tional connectivity matrix was computed separately for
each session using an isotropic 2-mm voxels within an a priori
gray matter mask (SPM apriori/grey.nii mask thresholded at
FIG. 5. ROI-to-ROI functional
connectivity with PCC seed area. Top:
ROIs defined from talairach atlas
Brodmann areas that show positive
(red) and negative (blue) functional
connectivity with PCC are shown (for
display clarity each ROI is identified
by its centroid positions). Results are
thresholded at FDR-corrected p<0.05.
Bottom: Intersession reliability of
ROI-to-ROI group-level functional
connectivity measures.
FIG. 6. ROI-level analysis of global efficiency. Top: Global efficiency of each ROI (a measure of ROI centrality, and shown
proportional to circle sizes in the left display) in the network defined by positively associated ROIs (ROIs defined from talair-
ach atlas Brodmann areas). Small world properties, where global efficiency is greater than that of a lattice graph and local ef-
ficiency is greater than that of a random graph, are observed at the chosen cost threshold level (K=0.15). Bottom: Intersession
reliability of the estimated group-level measures of global efficiency for each ROI.
p>0.25; N=212,792 voxels). The RSC measure computes the
norm of the difference between the functional connectivity
patterns (rows of the voxel-to-voxel matrix) of neighboring
voxels. Average group-level RSC across all sessions is
shown in Figure 7 (top). Within-session estimates (Fig. 7 bot-
tom) show a high degree of reliability (intersession correla-
tion r=0.98, mean absolute error 0.002) when comparing
group-level estimates of RSC across repeated runs or ses-
sions. Similar interscan reliability was found for other
voxel-level fcMRI measures derived from voxel-to-voxel
analysis (ILC r=0.98; RCC r=0.99; GCS r=0.97).
Illustration of voxel-to-voxel analysis (optimal placement
of fcMRI seeds)
This analysis investigates the reliability of seed-to-voxel
functional connectivity estimates across all possible seed loca-
tions. They were implemented as voxel-to-voxel analysis
using a user-defined measure characterizing the between-
session similarity of functional connectivity patterns at each
voxel. Isotropic 2-mm voxels within an a priori gray matter
mask (SPM apriori/grey.nii mask thresholded at p>0.25)
were used for this analysis (N=212,792 voxels). The matrix
of voxel-to-voxel functional connectivity values (bivariate
correlation matrix, with size N·N) was parameterized sepa-
rately for each subject and for each session (Appendix A.2).
Separately for each row of this matrix (subject-specific func-
tional connectivity estimates between a given seed voxel
and all of the gray matter voxels) the intersession correlation
was computed and averaged across each pair of sessions and
across all subjects. The resulting measures (for each voxel)
characterize the average subject-level intersession reliability
of seed-to-voxel analysis when using each voxel as a possible
seed location (c.f. group-level reliability measure used in the
previous sections) (Fig. 8). Intersession reliability values of
subject-level connectivity estimates ranged between r=0.01
and r=0.62 (average r=0.29) across all possible seed loca-
tions. Local peaks in this map characterize optimal seed loca-
tions (they result in seed-to-voxel functional connectivity
patterns that are more robust across sessions than those pat-
terns resulting when using neighboring seed locations). Peak
values with intersession reliability above r=0.50 are show in
Figure 8 (bottom). Robust seed locations were identified in
default network areas—PCC, MPFC, and lateral parietal—
in close agreement with standard seed locations for these
areas (Fox et al., 2005). In addition, other robust locations in-
cluded superior temporal gyrus (one anterior temporal
source, and a different posterior source close to supramargi-
nal gyrus), superior frontal gyrus, and cingulate gyrus. The
results showed high degree of hemispheric symmetry, with
all of the peaks (except medial peaks: MPFC, PCC, and cingu-
late gyrus) having a corresponding peak with similar location
and reliability in the opposite hemisphere. Since the seed with
highest reliability (0,56,28) was close but slightly inferior to
the a priori PCC seed location used in the previous sections
(6,52,40), we defined for comparison a new seed location
using a spherical ROI of 10 mm centered at the new coordina-
tes (0,56,28). The group-level and subject-level intersession
reliability of the seed-to-voxel functional connectivity estima-
tes when using this new seed definition was r=0.97 and
FIG. 7. Voxel-to-voxel analysis of radial similarity contrast measure. Top: Average group-level radial similarity contrast at
each voxel. Darker shades for a voxel indicate higher similarity between the global functional connectivity patterns of this
voxel and those of its neighbors. Bottom: Intersession reliability of the group-level radial similarity contrast measure.
r=0.64, respectively (compared with r=0.95 and r=0.55, re-
spectively, when using the original PCC definition).
RSFC analysis offers an important characterization of func-
tional brain connectivity for both normal and patient popula-
tions. This article describes the methods used to compute a
variety of functional connectivity measures in the Conn tool-
box and illustrates the interscan reliability of these measures.
The Conn toolbox offers a large suite of connectivity analyses
packaged in a user-friendly GUI. The toolbox can be best used
in conjunction with SPM but it is compatible with other anal-
ysis packages. The output of most processing and analysis
procedures are stored as NIFTI volumes (e.g., the time series
post noise reduction and correlation and Z-maps from seed
voxel analysis) that may be used for further interrogation. For
example, researchers may enter the subject-level Z-maps
(Fisher-transformed subject-level correlation coefficients
when performing bivariate-correlation analysis) in their anal-
ysis package of choice for additional second-level analysis.
The toolbox also offers a complete batch processing environ-
ment facilitating the implementation of scalable and robust
functional connectivity analysis using a simple common
framework. In addition, the toolbox encourages users to
explore their data at intermediate steps of the analyses (e.g.,
distribution of voxel-to-voxel connectivity estimates, spatial
patterns of potential confounder effects, and individual sub-
ject-level connectivity maps), which can aid in detecting
and correcting potential anomalies in the data as well as iden-
tifying sources of variability that might go unnoticed when
focusing on group-level summary results alone.
There has been a debate as to whether observed anticorre-
lations are valid neurophysiological findings or analytic
artifacts introduced by global signal regression, a common
technique in removing confounds due to physiological and
other noise sources in the BOLD time series (Buckner et al.,
FIG. 8. Voxel-to-voxel analysis studying robustness of seed locations. Top: Intersession reliability maps. This display shows
the intersession correlation between functional connectivity patterns for all possible seed locations. Darker shades for a voxel
indicate that this voxel, when used as seed for standard seed-to-voxel functional connectivity analysis, results in connectivity
patterns that are better replicated across sessions (higher intersession correlations). Bottom: Optimal seed locations, as esti-
mated from the local peaks of the reliability maps above. Seed locations that show local maxima in reliability when comparing
subject-level estimates of functional connectivity strength across repeated runs or sessions (rvalue represents the subject-
specific intersession correlation averaged across all subjects). All seeds with average r>0.50 are shown. Peak locations are
reported as (x,y,z) Montreal Neurological Institute coordinates. LLP, left lateral parietal; RLP, right lateral parietal.
2008; Fox et al., 2009; Murphy et al., 2009; Weissenbacher
et al., 2009). There is general agreement, however, as may
be illustrated with mathematical proof, that a seed voxel anal-
ysis using global signal regression will necessarily show anti-
correlations even if none were truly present in the data
because after global regression the distribution of the correla-
tion coefficients between a voxel and every other voxel in the
brain is shifted such that the sum £0 (Fox et al., 2009; Murphy
et al., 2009). Because of this mathematical consequence of the
shift in correlation distribution and because the global signal
may contain important neural signals as well as noise, it has
been recommended to refrain from interpreting anticorrela-
tions when using global signal regression (Chang and Glover,
2009; Murphy et al., 2009).
However, the CompCor method of noise reduction, which
does not rely on global signal regression or physiological
monitoring, also results in anticorrelations, further support-
ing a biological basis for their existence, and the positive cor-
relations have higher sensitivity and specificity than global
signal regression (Chai et al., 2012). We believe that the
CompCorr method, as implemented in Conn, yields valid
anticorrelations between large-scale brain networks.
Example illustrations
This article also describes the methods used to compute a
variety of functional connectivity measures in the Conn tool-
box and illustrates the interscan reliability of these mea-
sures. Seed-to-voxel and ROI-to-ROI measures of functional
connectivity show high reliability for well-characterized
seed locations in RSFC, both when using correlation- and
regression-based measures to characterize functional connec-
tivity. Similarly, graph theoretical measures characterizing
structural properties of functional connectivity networks, as
well as voxel-level measures characterizing the local connec-
tivity patterns (between each voxel and its neighbors) and
global connectivity patterns (between each voxel and the
rest of the brain) also show high levels of interscan reliability.
The Conn toolbox offers a common framework to define
and perform a large suite of connectivity analyses, including
bivariate/semipartial correlations, bivariate/multivariate re-
gression, seed-to-voxel connectivity, ROI-ROI connectivity,
novel voxel-to-voxel connectivity, and graph theoretical mea-
sures for both resting state and task fMRI data. The analyses
in this article show high levels of interscan reliability for a
variety of fcMRI measures corroborating their potential appli-
cation as useful neuromarkers. In addition, the Conn imple-
mentation of the anatomical CompCor method of noise
reduction increases sensitivity and specificity of functional
connectivity and allows for better interpretability of anticor-
relations as it does not rely on global signal regression. We
hope that the imaging community will benefit from the con-
tribution of the tools.
The Poitras Center for Affective Disorders Research at the
McGovern Institute for Brain Research at MIT supported this
work. The authors thank Shay Mozes for initial programming
support and John Gabrieli for comments on the article.
Author Disclosure Statement
The authors of the study have no conflict of interest to
Achard S, Bullmore E. 2007. Efficiency and cost of economical
brain functional networks. PLoS Comput Biol 3:e17.
Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E. 2006. A
resilient, low-frequency, small-world human brain functional
network with highly connected association cortical hubs.
J Neurosci 26:63–72.
Aizenstein HJ, Butters MA, Wu M, Mazurkewicz LM, Stenger VA,
Gianaros PJ, Becker TJ, Reynolds CF, Carter CS. 2009. Altered
functioning of the executive control circuit in late-life depres-
sion: episodic and persistent phenomena. Am J Geriatr Psy-
chiatry: Off J Am Assoc Geriatr Psychiatry 170:30–42.
Behzadi Y, Restom K, Liau J, Liu TT. 2007. A component based
noise correction method (CompCor) for BOLD and perfusion
based fMRI. NeuroImage 37:90–101.
Beckmann CF, DeLuca M, Devlin JT, Smith SM. 2005. Investiga-
tions into resting-state connectivity using independent com-
ponent analysis. Philos Trans R Soc Lond B Biol Sci 360:
Beckmann CF, Mackay CE, Filippini N, Smith SM. 2009. Group
comparison of resting-state FMRI data using multi-subject
ICA and dual regression. Neuroimage 47:(Suppl. 1), S148.
Birn RM, Diamon JB, Smith MA, Bandettini PA. 2006. Separating
respiratory-variation-related fluctuations from neuronal-
activity-related fluctuations in fMRI. NeuroImage, 31:1536–
Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional
connectivity in the motor cortex of resting human brain
using echo-planar MRI. Magn Reson Med 34:537–541.
Biswal BB, et al. 2010. Towards discovery science of human brain
function. Proc Natl Acad Sci 107:4734–4739.
Buckner RL, Andrews-Hanna JR, Schacter DL. 2008. The brain’s
default network: anatomy, function, and relevance to disease.
Ann N Y Acad Sci 1124:1–38.
Buckner RL, et al. 2009. Cortical hubs revealed by intrinsic func-
tional connectivity: mapping, assessment of stability, and re-
lation to Alzheimer’s disease. J Neurosci 29:1860–1873.
Bullmore E, Sporns O. 2009. Complex brain networks: graph the-
oretical analysis of structural and functional systems. Nat Rev
Neurosci 10:186–198.
Calhoun VD, Adali T, Pearlson GD, Pekar JJ. 2001. A method for
making group inferences from functional MRI data using in-
dependent component analysis. Hum Brain Mapp 14:140–151.
Calhoun VD, Adali T, Pekar JJ. 2004. A method for comparing
group fMRI data using independent component analysis: ap-
plication to visual, motor and visuomotor tasks. Magn Reson
Imaging 22:1181–1191.
Castellanos FX, Margulies DS, Kelly C, Uddin LQ, Ghaffari M,
Kirsch A, Shaw D, Shehzad Z, Di Martino A, Biswal BB,
Sonuga-Barke EJ, Rotrosen J, Adler LA, Milham MP. 2008.
Cingulate-precuneus interactions: a new locus of dysfunction
in adult attention-deficit/hyperactivity disorder. Biol Psy-
chiatry 63:332–337.
Chai XJ, Nieto-Castan
¨r D, Whitfield-Gabrieli S. 2012.
Anticorrelations in resting state networks without global sig-
nal regression. Neuroimage 59:1420–1428.
Chang C, Glover GH. 2009. Effects of model-based physiological
noise correction on default mode network anti-correlations
and correlations. NeuroImage 47:1448–1459.
Chen S, Ross TJ, Zhan W, Myers CS, Chuang KS, Heishman SJ,
Stein EA, Yang Y. 2008. Group independent component anal-
ysis reveals consistent resting-state networks across multiple
sessions. Brain Res 1239:141–151.
Cohen AL, Fair DA, Dosenbach NU, Miezin FM, Dierker D, Van
Essen DC, Schlaggar BL, Petersen SE. 2008. Defining func-
tional areas in individual human brains using resting func-
tional connectivity MRI. NeuroImage 41:45–57.
Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ,
Smith SM, Beckmann CF. 2006. Consistent resting-state
networks across healthy subjects. Proc Natl Acad Sci U S A
Deshpande G, LaConte S, Peltier S, Hu X. 2007. Integrated local
correlation: A new measure of local coherence in fMRI data.
Hum Brain Mapp 30:13–23.
Di Martino A, Scheres A, Margulies DS, Kelly AM, Uddin LQ,
Shehzad Z, Biswal B, Walters JR, Castellanos FX, Milham
MP. 2008. Functional connectivity of human striatum: a rest-
ing state FMRI study. Cereb Cortex 18:2735–2747.
Fair DA, Schlaggar BL, Cohen AL, Miezin FM, Dosenbach NU,
Wenger KK, Fox MD, Snyder AZ, Raichle ME, Petersen SE.
2007. A method for using blocked and event-related fMRI
data to study ‘‘resting state’’ functional connectivity. Neuro-
Image 35:396–405.
Fox MD, Raichle ME. 2007. Spontaneous fluctuations in brain ac-
tivity observed with functional magnetic resonance imaging.
Nat Rev Neurosci 8:700–711.
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC,
Raichle ME. 2005. The human brain is intrinsically organized
into dynamic, anticorrelated functional networks. Proc Natl
Acad Sci U S A 102:9673–9678.
Fox MD, Snyder AZ, Zacks JM, Raichle ME. 2006. Coherent spon-
taneous activity accounts for trial-to-trial variability in human
evoked brain responses. Nat.Neurosci. 90:23–25.
Fox MD, Zhang D, Snyder AZ, Raichle ME. 2009. The global sig-
nal and observed anticorrelated resting state brain networks.
J Neurophysiol 101:3270–3283.
Fransson. 2005. Spontaneous low-frequency BOLD signal fluc-
tuations: an fMRI investigation of the resting-state default
mode of brain function hypothesis. Hum Brain Mapp 260:
Fransson P, Skiold B, Horsch S, Nordell A, Blennow M, Lager-
crantz H, Aden U. 2007. Resting-state networks in the infant
brain. Proc Natl Acad Sci U S A 104:15531–15536.
Friston KJ. 1994. Functional and effective connectivity in neuroi-
maging: a synthesis. Hum Brain Mapp 20:56–78.
Friston KJ, Buechel C, Fink GR, Morris J, Rolls E, Dolan RJ. 1997.
Psychophysiological and modulatory interactions in neuroi-
maging. NeuroImage 60:218–229.
Friston KJ, Worsley KJ, Frackowiak RSJ, Mazziotta JC, Evans AC.
1994. Assessing the significance of focal activations using their
spatial extent. Hum Brain Mapp 1:210–220.
Fukunaga M, Horovitz SG, van Gelderen P, de Zwart JA, Jansma
JM, Ikonomidou VN, Chu R, Deckers RH, Leopold DA, Duyn
JH. 2006. Large-amplitude, spatially correlated fluctuations in
BOLD fMRI signals during extended rest and early sleep
stages. Magn Reson Imaging 24:979–992.
Gitelman DR, Penny WD, Ashburner J, Friston KJ. 2003. Model-
ing regional and psychophysiologic interactions in fMRI: the
importance of hemodynamic deconvolution. NeuroImage
Goelman G. 2004. Radial correlation contrast—a functional con-
nectivity MRI contrast to map changes in local neuronal com-
munication. Neuroimage 23:1432–1439.
Greicius MD, Flores BH, Menon V, Glover GH, Solvason HB,
Kenna H, Reiss AL, Schatzberg AF. 2007. Resting-state func-
tional connectivity in major depression: abnormally increased
contributions from subgenual cingulate cortex and thalamus.
Biol Psychiatry 62:429–437.
Greicius MD, Kiviniemi V, Tervonen O, Vainionpaa V, Alahuhta
S, Reiss AL, Menon V. 2008. Persistent default-mode network
connectivity during light sedation. Hum Brain Mapp 29:839–
Greicius MD, Krasnow B, Reiss AL, Menon V. 2003. Functional
connectivity in the resting brain: a network analysis of the
default mode hypothesis. Proc Natl Acad Sci U S A 100:
Greicius MD, Srivastava G, Reiss AL, Menon V. 2004. Default-
mode network activity distinguishes Alzheimer’s disease
from healthy aging: evidence from functional MRI. Proc
Natl Acad Sci U S A 101:4637–4642.
Hamilton JP, Furman DJ, Chang C, Thomason ME, Dennis E,
Gotlib IH. 2011. Default-mode and task positive network
activity in major depressive disorder: implications for adap-
tive and maladaptive rumination. Biol Pschiatry 70:327–333.
Hampson M, Peterson BS, Skudlarski P, Gatenby JC, Gore JC.
2002. Detection of functional connectivity using temporal cor-
relations in MR images. Hum. Brain Mapp 150:247–262.
Horwitz B. 2003. The elusive concept of brain connectivity. Neu-
roImage 190:466–470.
Horovitz SG, Fukunaga M, de Zwart JA, van Gelderen P, Ful-
ton SC, Balkin TJ, Duyn JH. 2008. Low frequency BOLD
fluctuations during resting wakefulness and light sleep: a
simultaneous EEG-fMRI study. Hum Brain Mapp 29:
Kelly AM, Uddin LQ, Biswal BB, Castellanos FX, Milham MP.
2008. Competition between functional brain networks medi-
ates behavioral variability. Neuroimage 39:527–537.
Kim JH, Lee JM, Jo HJ, Kim SH, Lee JH, Kim ST, Seo SW, Cox RW,
Na DL, Kim SI, Saad ZS. 2010. Defining functional SMA and
pre-SMA subregions in human MFC using resting state fMRI:
functional connectivity-based parcellation method. Neuro-
image 49:2375–2386.
Koshino H, Carpenter PA, Minshew NJ, Cherkassky VL, Keller
TA, Just MA. 2005. Functional connectivity in an fMRI work-
ing memory task in high-functioning autism. NeuroImage
Lancaster JL, et al. 2000. Automated Talairach atlas labels for
functional brain mapping. Hum Brain Mapp 10:120–131.
Latora V, Marchiori M. 2001. Efficient behavior of small-world
networks. Phys Rev Lett 87:198701–198704.
Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. 2003. An auto-
mated method for neuroanatomic and cytoarchitectonic
atlas-based interrogation of fMRI data sets. NeuroImage 19:
Margulies DS, Kelly AM, Uddin LQ, Biswal BB, Castellanos FX,
Milham MP. 2007. Mapping the functional connectivity of an-
terior cingulate cortex. NeuroImage 37:579–588.
Mennes M, Kelly C, Zuo XN, Di Martino A, Biswal BB, Castella-
nos FX, Milham MP. 2010. Inter-individual differences in rest-
ing-state functional connectivity predict task-induced BOLD
activity. Neuroimage 50:1690–1701.
Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA.
2009. The impact of global signal regression on resting state
correlations: are anti-correlated networks introduced? Neuro-
image 44:893–905.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE.
2012. Spurious but systematic correlations in functional
connectivity MRI networks arise from subject motion. Neuro-
image 59:2142–2154.
Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA,
Shulman GL. 2001. A default mode of brain function. Proc
Natl Acad Sci U S A 98:676–682.
Rissman J, Gazzaley A, D’Esposito M. 2004. Measuring func-
tional connectivity during distinct stages of a cognitive task.
NeuroImage 230:752–763.
Roy AK, Shehzad Z, Margulies DS, Kelly AM, Uddin LQ,
Gotimer K, Biswal BB, Castellanos FX, Milham MP. 2009.
Functional connectivity of the human amygdala using resting
state fMRI. Neuroimage 45:614–626.
Salvador R, Suckling J, Schwarzbauer C, Bullmore E. 2005.
Undirected graphs of frequency-dependent functional con-
nectivity in whole brain networks. Philos Trans R Soc Lond
B Biol Sci 360:937–946.
Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA,
Hakonarson H, Gur RC, Gur RE. 2012. Impact of in-scanner
head motion on multiple measures of functional connectivity:
Relevance for studies of neurodevelopment in youth. Neuro-
image 60:623–632.
Siegle GJ, Thompson W, Carter CS, Steinhauer SF, Thase, ME.
2007. Increased amygdala and decreased dorsolateral pre-
frontal BOLD responses in unipolar depression: related and
independent features. Biol Psychiatry 610:198–209.
Shehzad Z, Kelly AM, Reiss PT, Gee DG, Gotimer K, Uddin LQ,
Lee SH, Margulies DS, Roy AK, Biswal BB, Petkova E, Castel-
lanos FX, Milham MP. 2009. The resting brain: unconstrained
yet reliable. Cereb Cortex 19:2209–2229.
Stevens M, Pearlson GD, Calhoun VD. 2009. Changes in the inter-
action of resting state neural networks from adolescence to
adulthood. Hum Brain Mapp 30:2356–2366.
Uddin LQ, Kelly AM, Biswal BB, Castellanos FX, Milham MP.
2009. Functional connectivity of default mode network com-
ponents: correlation, anticorrelation, and causality. Hum
Brain Mapp 30:625–637.
Uddin LZ, Supekar K, Amin H, Rykhlevskaia E, Nguyen DA,
Greicius MD, Menon V. 2010. Dissociable connectivity within
human angular gyrus and intraparietal sulcus: evidence from
functional and structural connectivity. Cereb Cortex 20:2636–2646.
Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW,
Buckner RL. 2010. Intrinsic functional connectivity as a tool
for human connectomics: theory, properties, and optimiza-
tion. J Neurophysiol 103:297–321.
Van Dijk KR, Sabuncu MR, Buckner RL. 2012. The influence of
head motion on intrinsic functional connectivity MRI. Neuro-
image 59:431–438.
Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen
DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME. 2007.
Intrinsic functional architecture in the anaesthetized monkey
brain. Nature 447:83–86.
Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T. 2007.
Altered functional connectivity in early Alzheimer’s disease: a
resting-state fMRI study. Hum Brain Mapp 28:967–978.
Watts DJ, Strogatz SH. 1998. Collective dynamics of ‘‘small-
world’’ networks. Nature 393:440–442.
Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E,
Windischberger C. 2009. Correlations and anticorrelations in
resting-state functional connectivity MRI: a quantitative com-
parison of preprocessing strategies. Neuroimage 47:1408–
Weng SJ, Wiggins JL, Peltier SJ, Carrasco M, Risi S, Lord C, Monk
CS. 2010. Alterations of resting state functional connectivity in
the default network in adolescents with autism spectrum dis-
order. Brain Res 1313:202–214.
Whitfield-Gabrieli S, Ford J. 2012. Assessment of default mode
network activity and connectivity in psychopathology.
Annu Rev Clin Psychol 8:49–76.
Whitfield-Gabrieli S, Thermenos HW, Milanovic S, Tsuang MT,
Faraone SV, McCarley RW, Shenton ME, Green, AI, Nieto-
Castanon A, LaViolette P, Wojcik J, Gabrieli J, Seidman LJ.
2009. Hyperactivity and hyperconnectivity of the default
network in schizophrenia and in first-degree relatives of
persons with schizophrenia. Proc Natl Acad Sci U S A
Zuo XN, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF,
Castellanos FX, Biswal BB, Milham MP. 2010a. The oscillating
brain: complex and reliable. Neuroimage 49:1432–1445.
Zuo XN, Kelly C, Adelstein JS, Klein DF, Castellanos FX, Milham
MP. 2010b. Reliable intrinsic connectivity networks: Test-
retest evaluation using ICA and dual regression approach.
Neuroimage 49:2163–2177.
Address correspondence to:
Susan Whitfield-Gabrieli
Department of Brain and Cognitive Sciences
Martinos Imaging Center at McGovern Institute
for Brain Research
Poitras Center for Affective Disorders Research
Massachusetts Institute of Technology
Cambridge, MA 02139
Appendix A.1: Treatment of Temporal
Confounding Factors
The observed raw blood oxygen level-dependent (BOLD)
contrast signal s(x,t) at voxel xand time tis characterized as
a linear combination of (1) Ntemporal confounds defined ex-
plicitly through subject- and session-specific time series c
(e.g., representing subject motion effects); (2) those confounds
defined implicitly from Knoise region of interests (ROIs),
each characterized by M
principal component time series
(t) (e.g., representing physiological effects observable in
white matter and CSF areas); and (3) an underlying BOLD
time series of interest e(x,t):
bkn(x)dkn (t)þe(x,t)
where a
(x) and b
(x) represent voxel-specific weights for
each of the confounding factors. The factors a
(x) and b
are estimated using ordinary least squares, and the BOLD sig-
nal of interest e(x,t) is approximated as the residuals in the lin-
ear model fit. The noise ROI time series d
(t) for each ROI k
are estimated using principal component analysis of the
time series s(x,t) limited to within ROI voxels [and after an ini-
tial orthogonalization with respect to the known c
(t) factors].
(e(x,u)dk1(u)) (e(x,)dk1())
dkn(u)dkn ()
dkn(t)dkm (t)=081<n<m
Where d
(t) represents the average residual BOLD time se-
ries within the ROI voxels W
, and d
(t) (for 1 <n£M
) repre-
sents the first M
–1 principal components of the temporal
covariance matrix within the same voxels. The initial orthog-
onalization in e*(x, t) guarantees that the noise ROI time se-
ries d
(t) are in turn orthogonal to the confounding factor
time series c
(t), making the resulting model (1) maximally
This approach assumes that the BOLD signal of interest e(x,t)
is orthogonal to each confounding factors c
While this can lead to decreased sensitivity in those cases
where the signal of interest is in fact correlated with some of
the confounding factors, we believe that the increased robust-
ness and validity of the resulting functional connectivity mea-
sures compensates the decreased sensitivity in these cases.
Appendix A.2: Computing Voxel-Wise Linear
Functional Connectivity Measures Singular
Value Decomposition
We represent the raw BOLD signal at voxel xand time tas
s(x,t). We are interested in computing the functional connec-
tivity matrix R, characterizing the temporal correlation be-
tween the BOLD signal at two arbitrary voxels x
and x
R: r(x1,x2)X
where ~
s(x,t) represents the normalized BOLD time series
(after subtracting its mean, and dividing by its standard devi-
ation). Typically the number of voxels is considerably larger
than the number of time points (scans). Because of this it is
more efficient to compute the crosscovariance matrix (time-
by-time covariance matrix, aggregated across all brain voxels
W), and to perform a singular value decomposition as follows:
C: c(t1,t2)X
From this decomposition we can define a new set of maps
This decomposition allows a simple reconstruction of the
voxel-to-voxel BOLD signal temporal correlation matrix Ras:
R: r(x1,x2)=X
In this way the b
maps (eigenvectors) and associated d
values (eigenvalues) implicitly characterize the correlation
matrix R. In the presence of band-pass filtering the number
of independent eigenvectors nis significantly smaller than
the number of independent time points (scans), so storing
the b
(x) maps requires always less storage than a copy of
the original functional data s(x,t). In addition, many measures
derived from the matrix Rcan easily be computed without
ever requiring to explicitly estimate the elements of this ma-
trix as the following section will illustrate.
Derived Measures
It can be shown that the b
maps form an orthogonal basis
for the entire set of possible connectivity maps (the row or col-
umn space of R). For example, an entire voxel-to-voxel con-
nectivity map with voxel x
as seed (one row of R) can be
computed as follows:
And the average of several connectivity maps (averaging sev-
eral rows of R, e.g., those corresponding to voxels within a
given seed ROI X) can be computed as follows:
bn(X) represents the average value of the b
map at the
voxels within the ROI X.
The norm of these connectivity maps, a measure of the over-
all strength of each connectivity map averaged across all target
voxels (corresponding to the norm of one row of R), can be com-
puted, respectively, and with minimal computation as follows:
The average voxel-to-voxel connectivity between two
ROIs x
and x
(averaging the values within a submatrix
of R) can also be computed with minimal computation
Not orthonormal, as the squared-norm of each b
map equals d
For example, we can consider the costs associated with computing
the average connectivity between any two arbitrary pairs of ROIs in
one atlas encompassing the entire set of brain voxels. Using (1) we
would need to compute all voxel-to-voxel connectivity values first
and then average across the desired ROIs. This computation scales
quadratically with the number of voxels, which is usually
prohibitive both in terms of time and required memory storage.
Using (2) instead, this computation scales linearly with the number
of voxels, as it only requires computing the average values of the b
maps within each ROI (no voxel-wise cross-products involved).
In addition, several complex measures derived from voxel-
wise connectivity measures can also be computed with
reduced computational cost. For example, the map of differ-
ential connectivity between two voxels x
and x
can be
computed as follows:
(bn(x1)bn(x2)) bn
The overall strength of this differential connectivity map, a
measure of the difference between the two individual connec-
tivity maps (similar to g
measure in Cohen et al., 2008), can
be computed as follows:
Similarly, the functional similarity measure between voxels
and x
(Kim et al., 2010), another way to characterize the
difference between two individual connectivity maps, can
be computed as follows:
The integrated local correlation measure (Deshpande et al.,
2007), characterizing the average connectivity between a
voxel and its neighbors (where the neighborhood is defined
by a spatial convolution kernel h), can be computed as
Similarly, the radial correlation contrast vector measure
(Goelman, 2004) can also be computed using multiple convo-
lution kernels (one for each spatial dimension), jointly defin-
ing the difference vector for each neighboring voxel.
Last, the norm of the local spatial gradient of a connectivity
map (a measure of the similarity between the global connec-
tivity patterns of neighboring voxels) can be computed as
Comparing Connectivity Patterns Across Conditions
In an experimental design with multiple conditions
(e.g., block design) we might wish to compute the task- or
condition-specific connectivity matrices.
where Aand Brepresent the time points associated with
two conditions of interest. For any given seed voxel x
, the
between-conditions correlation, a measure of the similarity
between the connectivity patterns during two different condi-
tions, separately for each seed voxel and for each subject, can
be computed as follows:
corr(rA(x1), rB(x1))
where Nis the total number of voxels,
nrepresents the aver-
age (across all voxels) of the bA
ncomponent map, and the
matrix D
represents the between-conditions covariance
n(x)note :dA,A
One possible application of this between-conditions corre-
lation measure is exemplified in this article in the voxel-to-
voxel analysis subsection of the results.
Although inspiratory muscle training (IMT) is an effective intervention for improving breath perception, brain mechanisms have not been studied yet. To examine the effects of IMT on insula and default mode network (DMN) using resting‐state functional MRI (RS‐fMRI). Prospective. A total of 26 healthy participants were randomly assigned to two groups as IMT group (n = 14) and sham IMT groups (n = 12). A 3‐T, three‐dimensional T2* gradient‐echo echo planar imaging sequence for RS‐fMRI was obtained. The intervention group received IMT at 60% and sham group received at 15% of maximal inspiratory pressure (MIP) for 8 weeks. Pulmonary and respiratory muscle function, and breathing patterns were measured. Groups underwent RS‐fMRI before and after the treatment. Statistical tests were two‐tailed P < 0.05 was considered statistically significant. Student's t test was used to compare the groups. One‐sample t‐test for each group was used to reveal pattern of functional connectivity. A statistical threshold of P < 0.001 uncorrected value was set at voxel level. We used False discovery rate (FDR)‐corrected P < 0.05 cluster level. The IMT group showed more prominent alterations in insula and DMN connectivity than sham group. The MIP was significantly different after IMT. Respiratory rate (P = 0.344), inspiratory time (P = 0.222), expiratory time (P = 1.000), and inspiratory time/total breath time (P = 0.572) of respiratory patterns showed no significant change after IMT. All DMN components showed decreased, while insula showed increased activation significantly. Differences in brain activity and connectivity may reflect improved ventilatory perception with IMT with a possible role in regulating breathing pattern by processing interoceptive signals. 2 Stage 4
Aims The addiction neurocircuitry model describes the role of several brain circuits (drug reward, negative emotionality and craving/executive control) in alcohol use and subsequent development of alcohol use disorder (AUD). Human studies examining longitudinal change using resting-state functional magnetic resonance imaging (rs-fMRI) are needed to understand how functional changes to these circuits are caused by or contribute to continued AUD. Methods In order to characterize how intrinsic functional connectivity changes with sustained AUD, we analyzed rs-fMRI data from individuals with (n = 18; treatment seeking and non-treatment seeking) and without (n = 21) AUD collected on multiple visits as part of various research studies at the NIAAA intramural program from 2012 to 2020. Results Results of the seed correlation analysis showed that individuals with AUD had an increase in functional connectivity over time between emotionality and craving neurocircuits, and a decrease between executive control and reward networks. Post hoc investigations of AUD severity and alcohol consumption between scans revealed an additive effect of these AUD features in many of the circuits, such that more alcohol consumption or more severe AUD was associated with more pronounced changes to synchronicity. Conclusions These findings suggest an increased concordance of networks underlying emotionality and compulsions toward drinking while also a reduction in control network connectivity, consistent with the addiction neurocircuitry model. Further, they suggest a compounding effect of continued heavy drinking on these vulnerabilities in neurocircuitry. More longitudinal research is necessary to understand the trajectories of individuals with AUD not adequately represented in this study, as well as whether this can inform effective harm reduction strategies.
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Objective To investigate the changes in the cerebellar-cerebral language network in temporal lobe epilepsy (TLE) patients from the cerebellar perspective, the research analyzes the changes of language and cognitive network in terms of functional connectivity (FC), as well as their efficiency of the reorganization were evaluated basing on relationship between the network metrics and neuropsychological scale scores.Methods30 TLE patients and 30 healthy controls were recruited. Brain activity was evaluated by voxel-mirrored homotopic connectivity analysis (VMHC). Two groups were analyzed and compared in terms of language FC using the following methods: Seed-to-Voxel analysis, pairwise correlations [region of interest(ROI)-to-ROI] and graph theory. Correlation analysis was performed between network properties and neuropsychological score.ResultsCompared with healthy participants, VMHC values in the Cerebellum Anterior Lobe, Frontal Lobe, Frontal_Sup_R/L, Cingulum_Ant_R/L, and Cingulum_Mid_R/L were decreased in TLE patients. Decreased FC was observed from the Cerebelum_10_R to the left inferior frontal gyrus, from the Cerebelum_6_R to the left Lingual Gyrus, from the Cerebelum_4_5_R to left Lingual Gyrus, left Cuneal Cortex and Precuneous Cortex, from the Cerebelum_3_R to Brain-Stem, and from the Cerebelum_Crus1_L to Cerebelum_6_R in TLE patients. The FC was enhanced between bilateral Cingulum_Mid and angular gyrus and frontoparietal insular cranium, between Frontal_Sup_Med L and left/right superior temporal gyrus (pSTG l/r), while it was decreased between left middle temporal gyrus and pSTG l/r. Compared with controls, the Betweenness Centrality (BC) of the right superior marginal gyrus (SMG), Temporal_Pole_Mid_R and Temporal_Mid_L as well as the Degree Centrality (DC) and Nodal Efficiency (NE) of the right SMG were lower in TLE patients. Further analysis showed that decreased VMHC in bilateral Cerebellum Anterior Lobe was positively correlated with the Boston Naming Test score in TLE patients, but it was negatively correlated with the Verbal Fluency Test score. The NE and DC of SMG_R were both negatively correlated with visual perception score in Montreal Cognitive Assessment.Conclusion Our results suggest that presence of abnormalities in the static functional connectivity and the language and cognitive network of TLE patients. Cerebellum potentially represents an intervention target for delaying or improving language and cognitive deficits in patients with TLE.
Purpose In this exploratory, open-label study, we used behavioral and brain imaging measures to assess the effectiveness of a smartphone application (ReSound Relief app), which aims to help reduce tinnitus-related distress. Method Fourteen participants with a wide range of tinnitus-related symptoms and who were not currently undergoing any external treatment participated. They completed the 6-month study and reported different levels of engagement with the app. Results Across a range of tinnitus questionnaires, most participants showed either no change or decrease in tinnitus handicap. Resting-state and task-based functional magnetic resonance imaging (fMRI) data were collected at baseline and the end of the study. Resting-state fMRI of 12 participants revealed alterations in interregional connectivity of default mode, salience, emotion, auditory, and visual processing networks at the end of the intervention period compared to baseline. Ratings of affective sounds (as pleasant, neutral, or unpleasant) were assessed using fMRI, and comparison after 6 months of app usage revealed reduced activity in the left superior temporal gyrus (secondary auditory cortex), right superior occipital gyrus, and left posterior cingulate cortex. Our findings were not significant at a false discovery rate level of p < .05. Conclusions The reported changes were not significant, possibly due to the small sample size, heterogeneity of the tinnitus handicap among subjects at the start of the project, and the length of the intervention period. Nevertheless, this study underscores the ease of usage of the app and the potential use of brain imaging to assess changes due to a passive, self-administered intervention for individuals with varying levels of tinnitus severity.
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Non-invasive electrical stimulation methods, such as transcranial alternating current stimulation (tACS), are increasingly used in human neuroscience research and offer potential new avenues to treat neurological and psychiatric disorders. However, their often variable effects have also raised concerns in the scientific and clinical communities. This study aims to investigate the influence of subject-specific factors on the alpha tACS-induced aftereffect on the alpha amplitude (measured with electroencephalography, EEG) as well as on the connectivity strength between nodes of the default mode network (DMN) [measured with functional magnetic resonance imaging (fMRI)]. As subject-specific factors we considered the individual electrical field (EFIELD) strength at target regions in the brain, the frequency mismatch between applied stimulation and individual alpha frequency (IAF) and as a covariate, subject’s changes in mental state, i.e., sleepiness. Eighteen subjects participated in a tACS and a sham session conducted on different days. Each session consisted of three runs (pre/stimulation/). tACS was applied during the second run at each subject’s individual alpha frequency (IAF), applying 1 mA peak-to-peak intensity for 7 min, using an occipital bihemispheric montage. In every run, subjects watched a video designed to increase in-scanner compliance. To investigate the aftereffect of tACS on EEG alpha amplitude and on DMN connectivity strength, EEG data were recorded simultaneously with fMRI data. Self-rated sleepiness was documented using a questionnaire. Conventional statistics (ANOVA) did not show a significant aftereffect of tACS on the alpha amplitude compared to sham stimulation. Including individual EFIELD strengths and self-rated sleepiness scores in a multiple linear regression model, significant tACS-induced aftereffects were observed. However, the subject-wise mismatch between tACS frequency and IAF had no contribution to our model. Neither standard nor extended statistical methods confirmed a tACS-induced aftereffect on DMN functional connectivity. Our results show that it is possible and necessary to disentangle alpha amplitude changes due to intrinsic mechanisms and to external manipulation using tACS on the alpha amplitude that might otherwise be overlooked. Our results suggest that EFIELD is really the most significant factor that explains the alpha amplitude modulation during a tACS session. This knowledge helps to understand the variability of the tACS-induced aftereffects.
Background Childhood maltreatment (CM) represents a potent risk factor for major depressive disorder (MDD), including poorer treatment response. Altered resting-state connectivity in the fronto-limbic system has been reported in maltreated individuals. However, previous results in smaller samples differ largely regarding localization and direction of effects. Methods We included healthy and depressed samples [ n = 624 participants with MDD; n = 701 healthy control (HC) participants] that underwent resting-state functional MRI measurements and provided retrospective self-reports of maltreatment using the Childhood Trauma Questionnaire. A-priori defined regions of interest [ROI; amygdala, hippocampus, anterior cingulate cortex (ACC)] were used to calculate seed-to-voxel connectivities. Results No significant associations between maltreatment and resting-state connectivity of any ROI were found across MDD and HC participants and no interaction effect with diagnosis became significant. Investigating MDD patients only yielded maltreatment-associated increased connectivity between the amygdala and dorsolateral frontal areas [ p FDR < 0.001; η ² partial = 0.050; 95%-CI (0.023–0.085)]. This effect was robust across various sensitivity analyses and was associated with concurrent and previous symptom severity. Particularly strong amygdala-frontal associations with maltreatment were observed in acutely depressed individuals [ n = 264; p FDR < 0.001; η ² partial = 0.091; 95%-CI (0.038–0.166)). Weaker evidence – not surviving correction for multiple ROI analyses – was found for altered supracallosal ACC connectivity in HC individuals associated with maltreatment. Conclusions The majority of previous resting-state connectivity correlates of CM could not be replicated in this large-scale study. The strongest evidence was found for clinically relevant maltreatment associations with altered adult amygdala-dorsolateral frontal connectivity in depression. Future studies should explore the relevance of this pathway for a maltreated subgroup of MDD patients.
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Subjectively arousing experiences tend to be better remembered than neutral ones. While numerous task-related neuroimaging studies have revealed the neural mechanisms associated with this phenomenon, it remains unclear how variability in the extent to which individuals show superior memory for subjectively arousing stimuli is associated with the intrinsic functional organization of their brains. Here, we addressed this issue using functional magnetic resonance imaging data collected at rest from a sample drawn from the Cambridge Centre for Ageing and Neuroscience cohort (N = 269, 18–86 years). Specifically, we performed multi-voxel pattern analysis of intrinsic functional connectivity, an unbiased, data-driven approach to examine whole-brain voxel-wise connectivity patterns. This technique allowed us to reveal the most important features from the high-dimensional, whole-brain connectivity structure without a priori hypotheses about the topography and direction of functional connectivity differences. Behaviorally, both item and associative memory accuracy were enhanced for trials with affectively arousing (positive or negative) stimuli than those with neutral ones. Whole-brain multi-voxel pattern analysis of functional connectivity revealed that the affective enhancement of memory was associated with intrinsic connectivity patterns of spatially distributed brain regions belonging to several functional networks in the cerebral cortex. Post hoc seed-based brain-behavior regression analysis and principal component analysis of the resulting correlation maps showed that these connectivity patterns were in turn primarily characterized by the involvement of heteromodal association and paralimbic (dorsal attention, salience, and default mode) networks of the cerebral cortex as well as select subcortical structures (striatum, thalamus, and cerebellum). Collectively, these findings suggest that the affective enhancement of episodic memory may be characterized as a whole-brain phenomenon, possibly supported by intrinsic functional interactions across several networks and structures in the brain.
Introduction: Pain in Parkinson's disease is poorly understood, and most patients with pain do not respond to dopaminergic drugs. We aimed to explore the mechanisms of dopa-responsive and -unresponsive pain by comparing such patients against patients without pain in terms of neural activity and functional connectivity in the brain. Methods: We prospectively examined 31 Parkinson's patients with dopa-responsive pain, 51 with dopa-unresponsive pain and 93 without pain using resting-state functional magnetic resonance imaging. Neural activity was assessed in terms of the amplitude of low-frequency fluctuation, while functional connectivity was assessed based on analysis of regions of interest. Results: Patients with dopa-unresponsive pain showed significantly higher amplitude of low-frequency fluctuation in the right parahippocampal/lingual region than patients with no pain. However, there was no amplitude difference between the dopa-responsive pain group and the no pain group. Patients with dopa-unresponsive pain also differed significantly from patients with no pain in their functional connections between the superior temporal gyrus and other areas of cerebral cortex, between amygdala and thalamus and between the amygdala and putamen. Patients with dopa-responsive pain differed significantly from patients with no pain in their functional connections between temporal fusiform cortex and cerebellum, between precentral gyrus and temporal fusiform cortex and between precentral gyrus and cerebellum. Conclusions: Regional neural activity and functional connectivity in the brain differ substantially among Parkinson's patients with dopa-unresponsive pain, dopa-responsive pain or no pain. Our results suggest that dopa-responsive and -unresponsive pain may arise through different mechanisms, which may help guide the development of targeted therapies.
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We explored the resting state functional connectivity correlates of apathy assessed as a multidimensional construct, using behavioral metrics, in behavioral variant frontotemporal dementia (bvFTD). We recorded the behavior of 20 bvFTD patients and 16 healthy controls in a close-to-real-life situation including a free phase (FP—in which actions were self-initiated) and a guided phase (GP—in which initiation of actions was facilitated by external guidance). We investigated the activity time and walking episode features as quantifiers of apathy. We used the means ((FP + GP)/2) and the differences (FP-GP) calculated for these metrics as well as measures by questionnaires to extract apathy dimensions by factor analysis. We assessed two types of fMRI-based resting state connectivity measures (local activity and seed-based connectivity) and explored their relationship with extracted apathy dimensions. Apathy in bvFTD was associated with lower time spent in activity combined with walking episodes of higher frequency, lower acceleration and higher duration. Using these behavioral metrics and apathy measures by questionnaires, we disentangled two dimensions: the global reduction of goal-directed behaviors and the specific deficit of self-initiation. Global apathy was associated with lower resting state activity within prefrontal cortex and lower connectivity of salience network hubs while the decrease in self-initiation was related to increased connectivity of parietal default-mode network hubs. Through a novel dimensional approach, we dissociated the functional connectivity correlates of global apathy and self-initiation deficit. We discussed in particular the role of the modified connectivity of lateral parietal cortex in the volitional process.
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Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.
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Small-world properties have been demonstrated for many complex networks. Here, we applied the discrete wavelet transform to functional magnetic resonance imaging (fMRI) time series, acquired from healthy volunteers in the resting state, to estimate frequency-dependent correlation matrices characterizing functional connectivity between 90 cortical and subcortical regions. After thresholding the wavelet correlation matrices to create undirected graphs of brain functional networks, we found a small-world topology of sparse connections most salient in the low-frequency interval 0.03– 0.06 Hz. Global mean path length (2.49) was approximately equivalent to a comparable random network, whereas clustering (0.53) was two times greater; similar parameters have been reported for the network of anatomical connections in the macaque cortex. The human functional network was dominated by a neocortical core of highly connected hubs and had an exponentially truncated power law degree distribution. Hubs included recently evolved regions of the heteromodal association cortex, with long-distance connections to other regions, and more cliquishly connected regions of the unimodal association and primary cortices; paralimbic and limbic regions were topologically more peripheral. The network was more resilient to targeted attack on its hubs than a comparable scale-free network, but about equally resilient to random error. We conclude that correlated, low-frequency oscillations in human fMRI data have a small-world architecture that probably reflects underlying anatomical connectiv-ity of the cortex. Because the major hubs of this network are critical for cognition, its slow dynamics could provide a physiological substrate for segregated and distributed information processing.
An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced [Lancaster et al., 1997]. In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation foci. TD system labels were compared with author-designated labels of activation coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD system compared well with authors' labels, with a 70% or greater label match averaged over all locations. Author-label matching improved to greater than 90% within a search range of +/-5 mm for most sites. An adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1 mouth region (30 subjects, 52 sites). It provided an 87% label match to Brodmann area labels (BA 4 & BA 6) within a search range of +/-5 mm. Using the adaptive GM range search, the TD system's overall match with authors' labels (90%) was better than that of the expert group (80%). When used in concert with authors' deeper knowledge of an experiment, the TD system provides consistent and comprehensive labels for brain activation foci. Additional suggested applications of the TD system include interactive labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education. (C) 2000 Wiley-Liss, Inc.