Download The Neuroscientist

Thehttp://nro.sagepub.com/
Neuroscientist
Real-Time fMRI: A Tool for Local Brain Regulation
Andrea Caria, Ranganatha Sitaram and Niels Birbaumer
Neuroscientist published online 7 June 2011
DOI: 10.1177/1073858411407205
The online version of this article can be found at:
http://nro.sagepub.com/content/early/2011/06/03/1073858411407205
Published by:
http://www.sagepublications.com
Additional services and information for The Neuroscientist can be found at:
Email Alerts: http://nro.sagepub.com/cgi/alerts
Subscriptions: http://nro.sagepub.com/subscriptions
Reprints: http://www.sagepub.com/journalsReprints.nav
Permissions: http://www.sagepub.com/journalsPermissions.nav
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
407205
07205Caria and othersThe Neuroscientist
NROXXX10.1177/10738584114
Review
The Neuroscientist
XX(X) 1­–15
© The Author(s) 2011
Reprints and permission: http://www.
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1073858411407205
http://nro.sagepub.com
Real-Time fMRI: A Tool for
Local Brain Regulation
Andrea Caria1, 2, Ranganatha Sitaram1, 3,
and Niels Birbaumer1,4
Abstract
Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. One of the most
challenging applications of real-time fMRI in neuroscientific and clinical research is the possibility of acquiring volitional control
of localized brain activity using real-time fMRI–based neurofeedback protocols. Real-time fMRI allows the experimenter
to noninvasively manipulate brain activity as an independent variable to observe the effects on behavior. Real-time fMRI
neurofeedback studies demonstrated that learned control of the local brain activity leads to specific changes in behavior.
Here, the authors describe the implementation and application of real-time fMRI with particular emphasis on the selfregulation of local brain activity and the investigation of brain-function relationships. Real-time fMRI represents a promising
new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more
causal insights into the functional role of circumscribed brain regions in behavior.
Keywords
real-time fMRI, BOLD, neurofeedback, self-regulation, operant learning
Brain imaging in cognitive and affective neuroscience adopts
experimental paradigms correlating a particular behavioral
manipulation as independent variable and recording the brain
response as dependent variable. This approach generated a
large amount of data demonstrating a significant relationship
between the two levels of observation: brain and behavior.
An opposite but complementary approach in human experimentation, in which brain activity is noninvasively manipulated as an independent variable to observe the effects on
behavior, is represented by brain stimulation methods such
as transcranial magnetic stimulation (TMS). TMS is currently an established investigative tool in the cognitive neurosciences that has made a remarkable contribution to the
understanding of perception, attention, language, learning,
and plasticity (Walsh and Cowey 2000).
Alternatively to brain stimulation, neurofeedback represents a noninvasive paradigm in which learned regulation
of local brain activity is used as an independent variable. An
extensive body of literature on neurofeedback based on
electroencephalographic (EEG) signals demonstrated that
individuals following training can learn to control oscillatory and evoked brain activity (Elbert and others 1984;
Birbaumer and others 1990). Several studies also provided
solid evidence that controlling brain activation can modify
behavior and have a therapeutic effect in particular in patients
with otherwise pharmacologically intractable epilepsy and
attention-deficit hyperactivity disorder, but only a few
indications passed rigorous clinical-experimental testing
(Barber and others 1971–1978; Birbaumer and Kimmel 1979;
Birbaumer and Cohen 2007).
Both TMS and EEG techniques have a limited spatial
resolution, and subcortical brain regions are not accessible;
thus, their usability and applications are limited. The advent
of functional neuroimaging, particularly fMRI, permitted
noninvasive assessment of brain function with high spatial
resolution by measuring changes in the blood-oxygen level–
dependent (BOLD) signal. Although the BOLD response is
an indirect measure, there is growing evidence for a correlation
of the BOLD signal with electrical brain activity (Logothetis
2008). The BOLD signal is modulated by increases and
decreases in deoxygenated hemoglobin concentration resulting from changes in cerebral blood volume, cerebral blood
1
Institute of Medical Psychology and Behavioral Neurobiology,
Eberhard-Karls-University of Tübingen, Tübingen, Germany
2
Dipartimento di Scienze della Cognizione e della Formazione,
Università di Trento, Trento, Italy
3
Sree Chitra Tirunal Institute for Medical Sciences and
Technology, Trivandrum, India
4
Ospedale San Camillo, Istituto di Ricovero e Cura a
Carattere Scientifico, IRCCS,Venezia, Italy
Corresponding Author:
Andrea Caria, Institute of Medical Psychology and Behavioural
Neurobiology, Eberhard-Karls-University of Tübingen,
Gartenstr. 29, D-72074 Tuebingen, Germany
Email: [email protected]
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
2
The Neuroscientist XX(X)
Figure 1. Online analysis of single-subject fMRI data. Orthographic 3D view of statistical maps (left) and blood-oxygen level–dependent
(BOLD) time course (right, white line) in two selected regions of interest corresponding to the red and green box, respectively (TurboBrainVoyager, Brain Innovation, Maastricht, the Netherlands). Number of volumes is in the x axis, and the magnitude of the BOLD signal
is in the y axis; these values are the raw output from the MRI scanner. Statistical significance was based on a t-test comparing voxel-level
activations during regulation blocks (green) with respect to baseline blocks (blue). Time-course windows also show estimated β values
for main conditions of the protocol as a bar graph on the right side. These β values are online estimated by an incremental general linear
model using the time-course data plotted on the left side. Event-related averaging plot (bottom left) and motion correction window
(bottom right) can also be provided.
flow, and oxygen metabolism following sensory stimulation
or cognitive tasks (Uludağ and others 2009). Innovations in
high-performance magnetic resonance scanners and computers and developments in techniques for faster acquisition,
processing, and analysis of MR images have now allowed us
to perform real-time fMRI analysis and have consequently
extended fMRI applications. Real-time fMRI permits simultaneous measurement and observation of brain activity during an ongoing task. Online single-subject preprocessing
and statistical analysis of functional data is possible within a
single repetition time (TR; 1–2 s).
One of the most challenging applications of real-time
fMRI in neuroscientific and clinical research is the possibility to acquire volitional control of localized brain activity
using real-time fMRI–based neurofeedback protocols. Realtime fMRI neurofeedback studies showed that learned
control of the local brain activity leads to specific changes in
behavior (deCharms and others 2005; Rota and others
2009; Caria and others 2010). Ratings of pain intensity
(deCharms and others 2005), prosody judgments (Rota and
others 2009), and valence of emotional stimuli (Caria and
others 2010) have been observed to vary concurrently to the
learned control of specific brain activations. These studies
demonstrated the feasibility of using the change in BOLD
signal in circumscribed regions of interest (ROIs) as an
independent variable and indicated real-time fMRI as a
complementary tool to investigate brain functions.
Here, we describe the implementation of real-time fMRI
and its application in healthy subjects and patients. Particular
emphasis is placed on the methodological aspects related to
the self-regulation of local brain activity and the investigation of brain-function relationships. We then illustrate the
concept by studies on self-regulation of the insula activity
in healthy participants and clinical populations. Finally, we
provide guidelines on the main experimental aspects of realtime fMRI–based neurofeedback.
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
3
Caria and others
Real-Time fMRI
Acquisition and Analysis
Online image reconstruction of whole-brain echo-planar
imaging (EPI) scans is now provided by most manufacturers
of MR scanners. Nevertheless, additional software is necessary for feeding back participants with useful quantitative
real-time fMRI information (Fig. 1). Once a connection to
MRI scanners is established (e.g., via TCP/IP protocols),
EPI images can be immediately retrieved for further processing and analysis. A tradeoff needs to be made between spatial
and temporal resolution as rapid acquisition of whole brain
images (1–2 s) limits the number of slices that can be acquired,
typically about 16 to 20.
Advances in fast acquisition, reconstruction scheme, and
preprocessing (Cox and Jesmanowicz 1999; Posse and others
1999; Voyvodic 1999; Yoo and others 1999; Gembris and
others 2000; Thesen and others 2000; Mathiak and Posse 2001;
Posse and others 2001; Smyser and others 2001; Esposito
and others 2003; Posse, Shen, and others 2003; Weiskopf
and others 2005) enabled real-time fMRI analysis to be performed using almost equivalent routines to those adopted for
offline MR functional imaging. Posse and colleagues (2001)
validated the real-time fMRI technique using single-block
design paradigms of standard visual, motor, and auditory
tasks and demonstrated its sensitivity for online detection of
higher cognitive functions during a language task. Real-time
acquisition methods, such as multiecho EPI (Posse and others
1999; Posse, Shen, and others 2003; Weiskopf and others 2005)
and adaptive multiresolution EPI (Yoo and others 1999),
have been optimized to ensure high speed and data quality.
Recently, Tang and Huang (2011) proposed a real-time
feedback method that automatically and rapidly determines
the optimal z-shim gradients for GE-EPI experiments to
compensate for susceptibility-induced local magnetic field
inhomogeneity.
Real-time fMRI methodology has been mainly explored
using block design paradigms in which task conditions are
Figure 2. Scheme of real-time fMRI signal preprocessing and classification. Brain state classification can be performed through the
following steps: 1) signal preprocessing for online realignment and spatial smoothing, 2) first-pass feature selection for selecting brain
voxels by applying an intensity threshold resulting in a brain mask, 3) second-pass thresholding for selecting informative voxels from the
first-pass brain mask by the method of effect mapping resulting in the final brain mask, 4) classifier retraining based on the brain mask
obtained in step 3, and 5) real-time classifier testing on new data using the second-pass brain mask (Sitaram and others in press).
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
4
The Neuroscientist XX(X)
Figure 3. Real-time fMRI system for neurofeedback. The
three main components (signal acquisition, online analysis,
and feedback) are usually executed by separate computers
connected via TCP/IP protocol. Spatially circumscribed brain
activity is measured by fMRI using the BOLD response with
fast echo planar imaging (EPI) sequences. The online analysis
software retrieves the data and performs data preprocessing and
statistical analysis. The signal time series of the selected regions
of interest are then exported to custom-made software, which
provides feedback to the participant.
alternated with the rest (Fig. 1). Conventional block designs
provide high functional sensitivity as the statistical power is
increased (Aguirre and D’Esposito 2000). Yet single-event
real-time fMRI has also been applied. Posse and colleagues
(2001) characterized the variability of the hemodynamic
impulse response in primary and supplementary motor cortex in consecutive trials using single movements.
Online preprocessing of fMRI data (Hollmann and others 2008) includes distortion correction (Zaitsev and others
2004; Weiskopf and others 2005), prospective (Thesen and
others 2000; Speck and others 2006; Zaitsev and others 2006;
Ooi and others 2009) or retrospective (Cox and Jesmanowicz
1999; Mathiak and others 2001) 3D motion correction, temporal filtering (Weiskopf and others 2004), spatial smoothing (Posse, Fitzgerald, and others 2003), spatial normalization
to stereotactic space (Lee and others 2008), and multivariate
classification (LaConte and others 2007). Artifacts caused
by participants’ motion during scanning are serious problems
in functional imaging. At present, offline as well as online
retrospective motion compensation algorithms are wellestablished methods for motion correction (Mathiak and
Posse 2001). However, retrospective correction might generate image blurring as it involves interpolation, whereas
the prospective approach, by keeping the image plane at a
fixed orientation with respect to the participant’s head during the acquisition, overcomes this problem. An integrated
method for real-time prospective correction was implemented by Zaitsev and others (2006) and Speck and others
(2006), who developed an efficient optical tracking device
that improves real-time slice-by-slice correction in 2D
EPI. Although optical tracking needs additional hardware
to be implemented, it does not require modification of the
pulse sequence.
Online statistical analyses can be performed not
only using univariate methods such as t-tests and correlation analysis (Voyvodic 1999), the general linear model
(GLM), and multiple regression (Voyvodic 1999; Smyser
and others 2001; Bagarinao and others 2003; Weiskopf and
others 2004) but also with more advanced multivariate
methods such as independent component analysis (ICA;
Esposito and others 2003) and pattern recognition analysis
(Laconte and others 2007; Laconte 2010; Sitaram and others 2010; Sorger and others 2010).
Two main approaches have been proposed for univariate methods: sliding window (Gembris and others 2000)
and incremental analysis (Cox and others 1995). The
sliding-window correlation and detrending technique limits the correlation computation to a susbset of measurement
time points, enabling one to maintain the sensitivity to
changes in the brain throughout the whole experiment but
reducing the statistical power (Gembris and others 2000). In
contrast, the incremental algorithms increase the statistical
power by recursively and cumulatively computing correlation analysis (Cox and others 1995),multiple regression
(Smyser and others 2001), or GLM (Bagarinao and others
2003) on the available data.
Alternatively to the traditional hypothesis-driven processing methods, data-driven approaches, such as ICA, have
been adapted to real-time fMRI data analysis. By reducing
the ICA input to a few points within a time series in a slidingwindow approach, comparable performance was obtained
with respect to standard linear regression analysis applied
either in a sliding window or in a cumulative mode (Esposito
and others 2003). Moreover, recent studies (Laconte and
others 2007; Laconte 2010; Sorger and others 2010; Sitaram
and others in press) proposed the implementation of a realtime fMRI pattern classification system using the Support
Vector Machine (Fig. 2). LaConte and colleagues (2007)
showed the feasibility of online decoding and feedback from
a single TR of fMRI scanning during block design left- and
right-hand motor imagery and further demonstrated the
classifier’s ability to decode other forms of cognitive and
emotional states. Sitaram and colleagues (in press) extended
the above work by showing robust online classification and
feedback of multiple emotional states.
Software for online univariate fMRI analysis is now provided by most MRI vendors; in addition, third-parties software packages are also available: TurboFire (Gembris and
others 2000), Turbo-BrainVoyager (Goebel 2001; Fig. 1),
and AFNI (Cox 1996). Functional MRI data can then be
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
5
Caria and others
Figure 4. Real-time fMRI experimental paradigms. (a) Experimental design used by deCharms and others (2005). Each scanning run
consisted of five increase/decrease cycles. Each cycle consisted of a 30-s rest block, followed by a 60-s increase block, during which
participants were trained to increase the activation of the rostral anterior cingulate cortex (rACC), followed by a 60-s decrease block
during which participants were trained to decrease activation in the target region of interest. A noxious thermal stimulus was applied
for 30 s during each increase and decrease block, starting 10 s after the beginning of the block. After completion of each training run,
participants rated the noxious stimulus. During a posttest run, identical to the training runs, the noxious stimulus was rated immediately
after its presentation. (b) Experimental design used by Rota and others (2009). The training consisted of four sessions, each of which
was composed of six activation blocks (50 s) separated by five baseline blocks (30 s). During activation blocks, the participants had to
increase the level of activation in the right inferior frontal gyrus (IFG; BA 45). During baseline blocks, they were instructed to relax by
performing mental imagery. At the end of the real-time fMRI training, participants were instructed to focus on the cognitive strategies
previously adopted and to up-regulate BA 45 while carrying out two linguistic tasks. No real-time fMRI feedback was provided to the
participant in this phase. (c) Experimental design used by Caria and others (2010). Training sessions for insula regulation consisted of five
regulation blocks (30 s) alternating with rest blocks (30 s), both followed by a picture presentation block (9 s) and a rating block (12 s).
Immediately after picture presentation, participants were required to rate the emotional valence and arousal of the stimuli.
displayed online in various formats including slice (single
or multislice) and 3D anatomical views, both enabling
voxel-based time courses inspection and head motion tracking
(Fig. 1). A centrally controlled communication between the
subsystems involved in the real-time fMRI experiments
based on an XML software framework called Experiment
Description Language has been recently proposed. Such
implementation permits one to define and control parameters
relevant for real-time data acquisition, real-time fMRI statistical data analysis, stimulus presentation, and activation processing during the experiment (Hollmann and others 2008).
Self-regulation of Local Brain
Activity Using Real-Time fMRI
Besides fMRI data quality assessment and neurosurgical
applications (for an overview, see Gasser and others 2005;
Kesavadas and others 2007; Weiskopf and others 2007;
Feigl and others 2008) and monitoring of anesthesia effects
(Wibral and others 2007), one of the most intriguing applications of real-time fMRI in neuroscientific and clinical
research is the self-regulation of localized brain activity. A
number of studies have shown that by providing real-time
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
6
The Neuroscientist XX(X)
fMRI feedback to a human person (Fig. 3), learned regulation
of the BOLD signal in several brain areas involved in motor,
sensory, cognitive, and emotional processing is achievable in
a few training sessions (Yoo and Jolesz 2002; Weiskopf and
others 2003; Posse, Fitzgerald, and others 2003; deCharms
and others 2004; Weiskopf and others 2004; deCharms and
others 2005; Yoo and others 2006; Caria and others 2007;
Rota and others 2009; Haller and others 2010; Johnston and
others 2010; Hamilton and others 2011; McCaig and others
2011). Despite the inherent low temporal resolution of fMRI
and so of real-time fMRI technique, human participants
successfully achieved control of the BOLD response through
operant learning. The functional characteristics essential
for effective operant learning correspond to the key elements of classical and operant conditioning (Brogden 1951;
Ferster and Skinner 1957). In particular, feedback training
should be configured to provide for discrete trials, in which
each rewarded response interspersed with a short pause is an
independent event. The response and reward must be contingent for optimal learning to occur, with reward immediately
following response (Felsinger and others 1947; Grice 1948).
The brain implicitly takes into account the delay between
response and reward (feedback delay) and adapts its performance. Specific schedules of reinforcement (Ferster and
Skinner 1957) to maximize learning can be applied to realtime fMRI neurofeedback (deCharms and others 2004; Bray
and others 2007; Johnson and others 2010). In accordance
with learning theories, such as prediction error theories, learning occurs through updating expectations of the outcome
proportionally to prediction error, in a way that across trials
the expected outcome converges to the actual outcome
(Rescorla and Wagner 1972; Pearce and Hall 1980). This
accepted learning strategy, also called model-free learning,
distinguishes from a model-based learning in which cognitive maps describing the relationship between different situations are used to generate a state prediction error. Recent
neuroimaging findings from Gläscher and colleagues (2010)
supported the existence of these two forms of learning models
in humans.
Although mechanisms underlying self-regulation of brain
activity through electroencephalography (EEG)-based neurofeedback have been extensively documented (Elbert and
others 1984; Birbaumer and others 1990; Birbaumer 1999;
Kotchoubey and others 2001; Hinterberger and others 2004;
Strehl and others 2006), formal models of real-time fMRI
neurofeedback have not been elaborated yet.
Using real-time fMRI brain-function relationships can
be investigated by training participants to self-regulate
BOLD activity in specific brain regions and by testing them
for concurrent changes in behavior. In fact, real-time fMRI
studies demonstrated that the learned regulation of the neurophysiological activity in circumscribed brain regions can
be used as an independent variable to observe its effects on
behavior. Studies reported behavioral changes due to
self-induced alterations of brain activity in specific areas
(deCharms and others 2004, 2005; Bray and others 2007;
Rota and others 2009; Caria and others 2010). DeCharms
and colleagues (2005) demonstrated that participants
were able to learn to control activation in the rostral anterior cingulate cortex (rACC), a region implicated in mediating the conscious perception of pain. Learned control of the
rACC activation was associated with changes in pain perception induced by noxious thermal stimulation (Fig. 4a). A
group of chronic pain patients was also trained to control
activation in the rACC and reported reduction in the
level of chronic pain after training. A study by Rota and
colleagues (2009) explored the effects of self-regulation of
brain areas involved in language processing, the right inferior
frontal gyrus (IFG). Participants succeeded in achieving voluntary regulation of their right Brodmann’s area (BA) 45. In
addition, two linguistic tasks were performed immediately before and after the training (Fig. 4b). A significant
improvement of accuracy was observed for the identification of emotional prosodic intonations but not for syntactic
processing.
Caria and colleagues (2007) first demonstrated the possibility of specifically increasing the BOLD signal in the
anterior insula in healthy participants through real-time
fMRI training. Second, they tested the hypothesis that voluntary modulation of activity in the anterior insula induces
changes in the subjective response to emotional stimuli
(Fig. 4c). Enhanced anterior insula activity was associated
with increased negative perception of aversive stimuli
(Caria and others 2010).
Recently, manipulation of visual perception has been demonstrated in a real-time fMRI neurofeedback experiment in
which participants were instructed to regulate activation of a
target ROI in the early visual cortex (Scharnowski and others
2010). The level of ongoing activity in retinotopically specific
areas of the human visual cortex caused by effective self-regulation had a causal effect on the detectability of visual stimuli. Specifically, a significant improvement in visual sensitivity
was observed when participants up-regulated BOLD activity.
Most of the studies so far described assessed the specificity of the observed changes in behavior with control groups.
Groups performed real-time fMRI training with unspecific
feedback, receiving information either from distinct brain
regions supposedly not involved in the target task (deCharms
and others 2005; Rota and others 2009; Caria and others
2010) or from a previously tested experimental participants
(deCharms and others 2005). In addition, the effects of cognitive strategies such as performing a mental imagery training alone (Caria and others 2010; Scharnowski and others
2010) or modulating the attention to the target stimuli were
also tested (deCharms and others 2005). None of the control
groups learned to regulate the activity in target brain region
and concurrently no changes in behavior were observed.
Control conditions indicated that unspecific feedback or
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
7
Caria and others
Figure 5. Insula training: offline and online fMRI analysis.
(Left) Statistical maps resulting from an offline mixed-effects
analysis on the experimental group (EX). EX participants,
trained with contingent real-time feedback information, show a
specific increase of the blood-oxygen level–dependent (BOLD)
magnitude in the left anterior insula (yellow box) during the last
session (S4, bottom) with respect to the first session (S1, top).
(Right) BOLD time courses of the left anterior insula during
the first (top) and the last session (bottom). The selected ROI
is delineated by a yellow box. Online analysis is based on t-tests
comparing activation on each voxel during the regulation blocks
with respect to the baseline blocks, with a threshold of
P < 0.05 false discovery rate corrected for multiple comparisons.
The time course of the BOLD activity (white line) is related to
the targeted ROI and shows the progress during the increase
blocks (red), baseline blocks (blue), picture presentation blocks
(yellow and cyan), and picture evaluation blocks (gray). Number
of volumes is in the x axis, and magnitude signal is in the y axis; y
values are the raw output from the scanner.
behavioral training performed in the absence of contingent
feedback does not lead to learned regulation of localized
brain activity. A combination of cognitive, emotional, and
motor strategies and real-time fMRI information drives participants to acquire successful control. Whether this is a
sufficient condition has still to be ascertained.
Nevertheless, time-contingent feedback of the physiological response (BOLD) constitutes a necessary ingredient of
brain self-regulation. Bray and colleagues (2007) demonstrated that monetary reinforcement of increased activity in
motor/somatosensory regions provided after a conditioning
trial was sufficient for participants to voluntarily control
their BOLD signal. The instrumentally conditioned brain
activity was observed to have a facilitator effect on reaction
times when the physical response engaged activity of the
same regions. However, a clear negative correlation
between reaction-time measure and BOLD signal change
was not reported.
Furthermore, the studies so far conducted indicate that
the acquired control does not result from general arousal
and/or global brain activation. Physiological signals should
be monitored to detect and prevent subjects from developing undesirable regulation strategies, consciously or unconsciously, for example, developing unspecific brain activation
Figure 6. Learning effect. Brain activity in the left insula in the
experimental and control groups during real-time fMRI training.
Percentage signal change was calculated by computing the
difference in the percentage BOLD signal during regulation and
baseline for each participant, averaged across all participants.
Increased percentage BOLD in the target area over sessions is
observed in the experimental group (EX) only. The two control
groups trained with sham feedback (SH) and using mental
imagery (MI) alone were not successful in learning to regulate
insula activity. *Significant changes (P < 0.05) in the last session
(S4) with respect to the first (S1).
by general arousal, variation of heart beat or tongue movement, and changes in breathing patterns. Real-time routines
can be adopted to determine physiological artifacts by parallel monitoring of the fMRI signal changes and physiological parameters (Glover and others 2000; Smyser and others
2001; Birn and others 2006). An open-source hardware and
software system for acquisition and real-time processing of
electrophysiology has been recently proposed for high-field
MRI (Purdon and others 2008).
Effects of real-time fMRI training were also demonstrated
in terms of functional brain reorganization (Rota and others
2010; Lee and others 2011). Changes in functional and
effective connectivity were investigated in subjects who
learned to deliberately increase activation in the right IFG
(rIFG) and improved their ability to identify emotional intonations after real-time fMRI training (Rota and others 2010).
The initial training process is characterized by a massive
connectivity of the rIFG to a widespread network of frontal
and temporal areas, which decreased and lateralized to the
right hemisphere with practice. Volitional control of activation strengthened connectivity of this brain region to the right
prefrontal cortex, whereas training increased its connectivity
to the bilateral precentral gyri.
Lee and colleagues (2011) examined brain changes while
individuals were trained to regulate the insular cortex by using
imagery of emotional episodes guided by real-time fMRI feedback information. Multivariate pattern-based spatial analysis
indicated that real-time fMRI training leads to more spatially
focused recruitment of areas relevant for processing of emotions. Effective connectivity analysis revealed that initial training causes an increase of network density, whereas further
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
8
The Neuroscientist XX(X)
training prunes redundant connections but strengthens relevant
connections (Lee and others 2011). These findings suggest that
changes of connectivity in a functionally specific manner can
be achieved with real-time fMRI training.
Moreover, multivariate methods were applied not only
for evaluating cerebral reorganization due to prolonged neurofeedback training but also for the implementation of realtime fMRI.
Recent studies indicate that the application of multivariate pattern classification analysis to real-time fMRI overcomes
the limitations of considering a single or combination of ROI
and does not require prior assumptions about functional
localization (Laconte and others 2007; Laconte 2010; Sitaram
and others 2010; Sorger and others 2010). Therefore, participants can be trained to manipulate their brain state by providing feedback of the BOLD activity in a network of brain
regions (Fig. 2). This would allow the experimenter to establish a more clear relationship between patterns of cerebral
activity and the observed behavior.
In the perspective of using real-time fMRI-based training as an interventional approach, a recent pilot study
trained chronic subcortical stroke patients to regulate the
BOLD response in the ventral premotor cortex. The participants’ ability to learn self-regulation was found to depend
linearly on the intracortical facilitation and correlated negatively with the intracortical inhibition measured by TMS
prior to feedback training. After training, intracortical inhibition decreased significantly with the volitional increase of
the BOLD in the PMv, indicating a beneficial effect of selfregulation training on motor cortical output (Sitaram and
others in press).
Further studies assessed the feasibility of real-time fMRI
neurofeedback as a behavioral intervention for nicotine
dependence (Chiu and others 2010; Stoeckel and others 2010).
The authors first showed that the nucleus accumbens (NAcc)
is involved in processing smoking-related information and
anticipating future smoking-related episodes. Second, they
reported that nine nicotine-dependent smokers successfully
increased activation in the left NAcc over six training runs.
Finally, they suggested that generating a future rewarding,
smoking-related episode, as opposed to a past memory, may be
a potential cognitive strategy nicotine-dependent individuals
can use to self-regulate reward-related brain activation and,
ultimately, the craving to smoke.
Driven by the same intent but inspired by the multivariate approach, Chiu and colleagues (2010) have shown data
indicating the suitability of multivoxel pattern analysis–
based real-time fMRI for modeling craving versus noncraving brain states and that chronic smokers can learn to
modulate a neurofeedback interface reflecting these states.
These results highlight the potentiality of the integration of
real-time fMRI and advances in computational approaches
to feedback to understand the neurobiology and treatment
of substance dependence (Laconte 2010).
Figure 7. Main components of fMRI neurofeedback
experimental design.
Learned Regulation of Insula
Activity through Real-Time fMRI
Recently Caria and colleagues investigated the modulatory
effect of the BOLD response in the left anterior insula on
the perception of visual emotional stimuli. Three groups of
participants were tested: two underwent four real-time fMRI
training sessions receiving either specific or unspecific feedback of the insula’s BOLD response, respectively, and one
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
9
Caria and others
group used emotional imagery alone without receiving realtime fMRI feedback information. The experimental protocol
consisted of four real-time fMRI sessions performed on one
day. Sessions consisted of five regulation blocks alternating
with six baseline blocks, both followed by a picture presentation block and a rating block (Fig. 4c). During the regulation blocks (30 s), indicated by a red background, participants
were asked to increase insula activity, whereas during baseline (30 s), indicated by a blue background, they had to return
the activity to the baseline level. Participants were provided
with a graphical thermometer displaying online changes of
BOLD activity with increasing or decreasing numbers of
bars (Fig. 4c). Thermometer bars were updated every 1.5 s
when a new BOLD signal from the ROIs was available.
After both regulation and baseline blocks, one emotionally
negative or neutral picture from the International Affective
Picture System (Bradley and Lang, 2007a, 2007b; Lang and
others 2008) was presented (9 s) and rated using a buttonbased control device inside the MRI scanner. Pictures were
evaluated in terms of subjective emotional valence and arousal
using the Self-Assessment Manikin (Bradley and Lang 1994).
Participants in the experimental group, who learned to significantly increase the BOLD signal in the target region (Fig. 5),
rated aversive pictures more negatively after regulation. The
larger the positive difference in the BOLD activation in the
anterior insula between regulation and baseline conditions,
the higher the level of perceived negative emotion of the aversive stimuli. Conversely, the smaller or more negative this
difference of the BOLD amplitude, the lower was the level
of perceived negative emotion of the aversive stimuli. The
groups trained with unspecific feedback and using only mental
imagery were unable to successfully achieve control of the
target brain region over time (Fig. 6) and showed no significant changes in the evaluation of the emotional stimuli.
Similarly, Lawrence and colleagues (2010) reported that
healthy participants are able to modulate the BOLD signal in
the right anterior insula after real-time fMRI neurofeedback
training. Interestingly, they observed higher valence ratings
and increased skin conductance response to positive stimuli
(the International Affective Picture System) presented after
increase blocks during the posttraining runs in the contingent
feedback group only. Altogether, these results complement
traditional neuroimaging studies (Critchley and others 2004;
Craig 2009), indicating the anterior insula as critical region
for the explicit appraisal of emotional stimuli, and they suggest a potential role of the real-time fMRI approach in clinical
applications of emotional disorders.
Using an analogous experimental protocol, ongoing studies
are currently investigating the ability of patients with emotional disorders to achieve volitional control of this region and
whether this has an effect on behavior and brain reorganization
(Sitaram 2007; Ruiz and others 2008). Sitaram (2007) assessed
whether criminal psychopaths could be trained to self-regulate
left anterior insula. Four psychopathic individuals who
underwent real-time fMRI neurofeedback sessions have
learned to regulate their left anterior insula after two to three
days of training, each day consisting of four feedback runs.
Participants with higher psychopathic checklist-revised (PCLR; Hare 2003) scores were less successful at self-regulation
than their lower PCL-R counterparts, supporting the existing
notion that psychopaths have deficient anticipatory fear-conditioning capacity. Effective connectivity analysis using Granger
causality modeling showed that learning to regulate the anterior insula not only increases the number of connections
(causal density) in the emotional network but also increases the
difference between the number of outgoing and incoming connections (causal flow) of the left insula (Sitaram 2007).
A further real-time fMRI study recruited chronic schizophrenic patients with negative symptoms for more than 20
real-time fMRI training sessions targeting the left and right
anterior insula (Ruiz and others 2008). The percentage BOLD
signal increased from an early weak regulation session to a
late strong regulation session. The success in regulation was
accompanied by an increase in the causal density of the
functional connections of the network involved in selfregulation of emotions, including insula; emotion recognition tests of participants who completed regulation training
showed a better accuracy in the recognition of disgust faces
after up-regulation compared with the baseline condition; and
both emotions disgust and happiness were rated as more
intense after up-regulation.
These preliminary results indicate the feasibility of realtime fMRI-based neurofeedback training in clinical groups.
However, more controlled studies assessing short- and longterm effects in clinical populations still need to be carried
out before adopting real-time fMRI training as a clinical tool.
Guidelines for Real-Time
fMRI Experimental Paradigms
In neurofeedback applications, training to self-regulate localized brain activity can be implemented by providing information about the BOLD signal from selected ROIs. A typical
implementation of a real-time fMRI-based neurofeedback
experiment consists of three separate components aimed to
signal acquisition, signal preprocessing and analysis, and
feedback computation and presentation (Figs. 3 and7).
Besides the technical aspects related to signal acquisition
and analysis (above described), there are some important
methodological issues to be considered when investigating
the effects on behavior of learned regulation of a specific
brain region, specifically ROI selection, feedback computation, behavioral assessment, and instructions to be provided
to the participants (Fig. 7).
Once a specific anatomical location is identified as target
brain ROI, delineation of the area can be accomplished anatomically and/or functionally. T1-weighted structural imaging can be used to define specific anatomical landmarks,
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
10
The Neuroscientist XX(X)
whereas specific functional localizer sessions, acquired
before real-time fMRI training, can be devoted to the detection of brain areas involved in motor, visual, emotional, and
higher cognitive processing. The ROI is chosen by drawing
a rectangular area on the anatomical image or by selecting
active clusters on functional maps computed by the signal
analysis software (e.g., Turbo-BrainVoyager). To improve
accuracy of the selection of ROIs, functional maps can be
co-registered with previously acquired anatomical scans of
the participant.
After one or more ROIs are selected, further processing of
the fMRI signal needs to be performed to arrive at a suitable
representation of brain activity to be presented as feedback.
Effective signal change is usually computed as a difference
of the average BOLD signal between the activation and the
baseline blocks in a single ROI or a combination of ROIs. It
is also possible to cancel out the effects of global drifting,
scaling, physiological fluctuations, and unspecific arousal by
using differential feedback; that is, the BOLD signal changes
in one reference region are subtracted from the targeted
ROIs (Weiskopf and others 2004; Caria and others 2007;
Rota and others 2009; Caria and others 2010). Recently,
online methods to explicitly model and remove nuisance signals in fMRI data have been shown to improve the quality
of neurofeedback (Hinds and others 2011). The authors also
proposed an alternative approach for a more flexible ROI
combination scheme via voxel efficiency weighting.
In additioin, a multiecho coarse voxel pulse sequence
has been proposed to increase contrast-to-noise ratio and
temporal sampling of BOLD signals (Kuo and others 2011).
This technique might increase the fraction of the total brain
voxels covered by the selected ROIs and enhance the ability
to regulate brain activity.
Real-time fMRI neurofeedback paradigms are particularly
sensitive to task-related BOLD signal artifacts. However,
appropriate scan parameters (e.g., specific phase encoding
direction) can reduce BOLD signal vulnerability to contamination from nonneuronal sources (Zhang and others 2011).
In contrast to select circumscribed brain regions by the
ROI method employing univariate analysis, pattern-based
methods permit extracting brain activity from spatially distributed interacting regions (Laconte and others 2007; Laconte
2010; Sorger and others 2010; Sitaram and others in press;
Table 1). Multivariate approaches enable the optimal weighting to combine ROIs and thus provide a flexible and more
comprehensive model, in terms of spatiotemporal relationships, of the brain network dynamics. Magland and colleagues (2011) have developed a new real-time processing
technique (spatio-temporal activity in real time [STAR])
that takes advantage of noise-reduction properties of multivoxel techniques without significantly affecting regional
specificity (Magland and others 2011).
Thus far, preselection of the ROI for feedback control has
been shown to be a successful approach for achieving BOLD
control (deCharms and others 2007; Weiskopf and others
2007; deCharms and others 2008). Alternatively, using multivariate classification models, neurofeedback signals can
cue participants about a specific brain state, rather than time
series fluctuations in localized brain regions. To this aim, participants can be trained with feedback information of the classifier’s output to learn to control their pattern of brain activation
(LaConte and others 2007; Chiu and others 2010; Sorger and
others 2010; Sitaram and others in press; Table 1).
Overall both the univariate-, ROI based- and the multivariate approaches can be beneficial for self-regulation of
brain activity through real-time fMRI. While the ROI based
approach is geared to study how self-regulation of specific
brain regions can influence mental states and behavior
through concomitant changes in a distributed neuronal network (Lee and others 2011; Rota and others 2010), the multivariate methods directly target brain patterns. Although
many feedback modalities are possible, visual feedback has
been the most frequently used method. A variety of visual
stimuli has been employed to indicate the required level of
activation at different time points, such as scrolling timeseries graphs and curves of BOLD activation (Weiskopf and
others 2003; deCharms and others 2004; Weiskopf and others 2004), functional maps of the brain (Yoo and Jolesz
2002), and a graphical thermometer that shows varying levels of ROI activity as changing bars (Caria and others 2007;
Rota and others 2009; Caria and others 2010).
Real-time fMRI neurofeedback is based on the principles of operant learning, and it uses the feedback of the neurophysiologic signal (BOLD) as intrinsic reward to learn to
acquire control over the brain activity. External factors such
as participants’ selection, instructions prior to and during
neurofeedback training, the interaction between participants and experimenter, and, in general, the experimental
environment can influence learning. Pilot experiments
showed that learning without any guidelines for mental
strategies was not achievable in a short training period and
led to a drop of motivation, especially in the uncomfortable
environment such as the MRI scanner (deCharms and others 2005; Sitaram 2007). For this purpose, participants are
usually instructed to use cognitive or emotional or motor
imagery to influence the feedback signal in the required
direction. Areas with unknown function or participants
without cognitive abilities to imagine are treated identically,
but participants receive only the instruction to influence the
feedback signal in the required direction, and participants
are rewarded for successful attempts to modify the metabolic flow in the particular brain region. Whether these
instructions have any specific effect or constitute mere placebo motivators remains to be shown. Previous studies have
shown that contingent feedback is the critical variable for
learning BOLD control, and cognitive or emotional imagery instructions have no effect (deCharms and others 2005;
Caria and others 2010). Similar results were reported for
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
11
Caria and others
slow cortical potential biofeedback (Rockstroh and others
1989; Birbaumer and others 1990).
Moreover, Laconte and colleagues (2007) demonstrated
using a multivariate technique that by receiving brain-state
feedback, participants are able to gain control over different
brain patterns with no need of cognitive strategy guidelines.
Typically, during operant conditioning of behavioral
responses, participants have a conscious awareness of the
responses leading to rewards, whereas in neurofeedback
experiments, change of the neurophysiological signals may
take place with little or no direct experience of it (Elbert and
others 1984). Accordingly, no instructions for feedback control may actually constitute the more successful real-time
fMRI paradigm. On the other hand, awareness of a direct
association between mental strategies and specific brain
activity seems to help participants to achieve successful
brain regulation through real-time fMRI.
After training participants retained the ability to control
the BOLD signal even in the absence of fMRI feedback
information (deCharms et al 2004; Caria and others 2007).
Transfer runs, where participants are instructed to perform
the same task as during training but without fMRI feedback,
are usually adopted to test whether training effects persist
beyond the experimental situation.
A further critical aspect in investigating the effects of
learned regulation of local brain activity is the behavioral
assessment (Fig. 4). EEG-based neurofeedback studies
on operant control of slow cortical potentials (SCPs)
assessed the behavioral effects of different types of
motor, perceptual, and cognitive tasks after training and
during self-generated SCP amplitudes (Elbert and others
1984; Rockstroh and others 1989; Birbaumer and others
1990; Birbaumer 1999). In clinical applications, such as
those for attention-deficit hyperactivity disorder, changes
in behavior and in clinical scores were assessed immediately after training and in a subsequent follow-up after a
few months (Strehl and others 2006).
Hence, a clear distinction between volitional regulation,
stimulus-induced activity, and response collection is necessary to reasonably infer the influence of volition (instrumental learning) on behavior. Timing of stimulus
presentation and collection of participants’ response is thus
a critical issue to be carefully considered. In some real-time
fMRI studies, self-regulation was performed simultaneously with the stimulus presentation and the subjective
response was recorded after each training run (deCharms
and others 2005; Fig. 4a) or session (Rota and others 2009;
Fig. 4b). Although this approach enables one to directly test
the ability to regulate brain activity in the presence of a
stimulus, it does not clearly resolve the issue of attentional
workload and dual task processing, which might influence
the behavioral performance. To circumvent this problem,
deCharms and colleagues (2005) asked participants to additionally rate the stimulus immediately after its presentation
during a posttest run, after training. Similarly, Caria and
colleagues (2010) separated the effects of self-regulation on
the behavioral response by asking participants to evaluate
the stimuli presented after regulation conditions, during the
real-time fMRI training (Fig. 4c). Yet this approach raises
the question about short-term effects, that is, how long after
regulation the behavioral effects can still be observed.
Future studies should clearly address this issue.
Very recently, Scott and others (2010) investigated the
effects of long-term real-time fMRI training in the motor cortex. Participants, involved in eight neuroimaging sessions
over the course of several weeks, were required to maintain
their level of brain activation within a specified range. Results
demonstrated performance improvements associated with
real-time fMRI feedback over multiple scanning sessions;
participants continued to demonstrate successively greater control over activation in their somatomotor cortex.
Thus far, most of the studies tested for changes in behavior concurrent with learned regulation of specific brain
activity using explicit tasks such as subjective ratings
(deCharms and others 2005; Caria, Sitaram and others
2010) or linguistic tasks (Rota and others 2009).
Nonetheless, implicit tasks and/or recordings of physiological responses such as skin conductance response (Lawrence
and others 2010) might strengthen the consistency of the
observed effects. The behavioral and cognitive assessment
of the real-time fMRI neurofeedback effects is critical for
the scientific as well as the clinical future of this methodology. Behavioral measures should test not only the specific
behavioral variable targeted but also several other nontargeted behavioral variables to exclude unspecific effects such
as arousal, emotional excitation, attention, and so forht.
With the appropriate control conditions and control groups, a
double dissociation is highly desirable.
Conclusions
Real-time fMRI enables the investigation of brain-function
relationships through the observation of the effects on behavior of self-regulation of local brain activity. There is growing
evidence that learned control of the BOLD signal in localized
brain regions leads to changes in behavior in both healthy
participants and clinical populations.
Real-time fMRI inspired a promising new approach to cognitive neuroscience that could complement traditional neuroimaging techniques by providing more causal insights into the
functional role of brain regions in behavior. These findings
also raise a fundamental question as to how learned regulation
of the BOLD signal in target brain regions might influence
behavior. A clearer understanding of the neural mechanisms
underlying the fMRI BOLD response will help us to extend the
neuroscientific and clinical applications of real-time fMRI.
Eventually, simultaneous EEG acquisition during real-time
fMRI based neurofeedback experiments might provide useful
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
12
The Neuroscientist XX(X)
insights about the relationship between the BOLD signal and
the underlying electrophysiological brain activity.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support
for the research and/or authorship of this article: The preparation
of this article was supported by the Deutsche Forschungsgemeinschaft
(DFG; BI 195/56-1 and BI 195/59-1), the Bundesministerium für
Bildung und Forschung (BMBF), the European Union (CEEDS;
258749), and the Provincia Autonoma di Trento.
References
Aguirre GK, D’Esposito M. 2000. Experimental design for brain
fMRI. In: Moonen CTW, Bandettini PA editors. Functional MRI.
Heidelberg (Germany): Springer-Verlag Berlin, p 369–380.
Bagarinao E, Matsuo K, Nakai T, Sato S. 2003. Estimation of general
linear model coefficients for real-time application. Neuroimage
19:422–9.
Barber TX, Kamiya J, Miller NE, editors. 1971–1978. Biofeedback
and Self-control. Chicago: Aldine.
Birbaumer N. 1999. Slow cortical potentials: plasticity, operant
control, and behavioral effects. Neuroscientist 5:74–78.
Birbaumer N, Cohen L. 2007. Brain–computer interfaces: communication and restoration of movement in paralysis. J Physiol
579:621–636.
Birbaumer N, Elbert T, Canavan A, Rockstroh B. 1990. Slow potentials of the cerebral cortex and behavior. Physiol Rev 70:1–41.
Birbaumer N, Kimmel H, editors. 1979. Biofeedback and Selfregulation. Hillsdale (FL): Erlbaum.
Birn RM, Diamond JB, Smith MA, Bandettini PA. 2006. Separating respiratory-variation-related fluctuations from neuronalactivity-related fluctuations in fMRI. Neuroimage 31:1536–48.
Bradley MM, Lang PJ. 1994. Measuring emotion: the self-assessment
manikin and the semantic differential. J Behav Ther Exp
Psychiatry 25:49 –59.
Bradley MM, Lang PJ. 2007a. Emotion and motivation. In:
Cacioppo JT, Tassinary LG, Berntson G, editors. Handbook of
Psychophysiology, 2nd ed. New York: Cambridge University
Press, p 581–607.
Bradley MM, Lang PJ. 2007b. The International Affective Picture
System (IAPS) in the study of emotion and attention. In: Coan
JA, Allen JJB, editors. Handbook of Emotion Elicitation and
Assessment. Oxford (UK): Oxford University Press, p 29–46.
Bray S, Shimojo S, O’Doherty JP. 2007. Direct instrumental conditioning of neural activity using functional magnetic resonance
imaging-derived reward feedback. J Neurosci 27:7498–507.
Brogden WJ. 1951. Animal studies of learning. In: Stevens SS, editor.
Handbook of Experimental Psychology. New York: John Wiley
& Sons, p 568–612.
Caria A, Sitaram R, Veit R, Begliuomini C, Birbaumer N. 2010. Volitional control of anterior insula activity modulates the response to
aversive stimuli: a real-time fMRI study. Biol Psychiatry 68:425–32.
Caria A, Veit R, Sitaram R, Lotze M, Weiskopf N, Grodd W, and
others. 2007. Regulation of anterior insular cortex activity using
real-time fMRI. NeuroImage 35:1238–46.
Chiu P, Lisinski J, King-Casas B, Sharp J, Eagleman D, Versace F,
and others. 2010. Modulating “crave” and “don’t crave” brain
states with real-time fMRI neurofeedback in smokers. 16th
Annual Meeting of the Organization for Human Brain Mapping,
Barcelona, Spain, 298 WTh-PM.
Cox RW. 1996. AFNI: software for analysis and visualization of
functional magnetic resonance neuroimages. Comput Biomed
Res 29:162–73.
Cox RW, Jesmanowicz A. 1999. Real-time 3D image registration
for functional MRI. Magn Reson Med 42:1014–8.
Cox RW, Jesmanowicz A, Hyde JS. 1995. Real-time functional
magnetic resonance imaging. Magn Reson Med 33:230–6.
Craig AD. 2009. How do you feel—now? The anterior insula and
human awareness. Nat Rev Neurosci 10:59–70.
Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. 2004.
Neural systems supporting interoceptive awareness. Nat Neurosci
7:189–95.
deCharms RC. 2007. Reading and controlling human brain activation using real-time functional magnetic resonance imaging.
Trends Cogn Sci 11:473–81.
deCharms RC. 2008. Applications of real-time fMRI. Nat Rev
Neurosci 9:720–9.
deCharms RC, Christoff KG, Glover HJ, Pauly M, Whitfield
S, Gabrieli JD. 2004. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21:
436–43.
deCharms RC, Maeda F, Glover GH, Ludlow D, Pauly JM, Soneji D,
and others. 2005. Control over brain activation and pain learned
by using real-time functional MRI. Proc Natl Acad Sci U S A
102:18626–31.
Elbert T, Rockstroh B, Lutzenberger W, Birbaumer N, editors. 1984.
Self-Regulation of the Brain and Behavior. New York: Springer.
Esposito F, Seifritz E, Formisano E, Morrone R, Scarabino T,
Tedeschi G, and others. 2003. Real-time independent component analysis of fMRI time series. Neuroimage 20:2209–24.
Feigl GC, Safavi-Abbasi S, Gharabaghi A, Gonzalez-Felipe V, El
Shawarby A, Freund HJ, and others. 2008. Real-time 3T fMRI
data of brain tumour patients for intra-operative localization of
primary motor areas. Eur J Surg Oncol 34:708–15.
Felsinger JM, Gladstone AL, Yamaguchi HG, Hull CL. 1947.
Reaction latency (StR) as a function of the number of reinforcements. J Exp Psychol 37:214–28.
Ferster CB, Skinner BF. 1957. Schedules of Reinforcement. New York:
Appleton-Century-Crofts.
Gasser T, Ganslandt O, Sandalcioglu E, Stolke D, Fahlbusch R,
Nimsky C. 2005. Intraoperative functional MRI: implementation and preliminary experience. Neuroimage 26:685–93.
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
13
Caria and others
Gembris D, Taylor JG, Schor S, Frings W, Suter D, Posse S. 2000.
Functional magnetic resonance imaging in real time (FIRE):
sliding window correlation analysis and reference-vector optimization. Magn Reson Med 43:259–68.
Gläscher J, Daw N, Dayan P, O’Doherty JP. 2010. States versus
rewards: dissociable neural prediction error signals underlying
model-based and model-free reinforcement learning. Neuron
66:585–95.
Glover GH, Li TQ, Ress D. 2000. Image-based method for retrospective correction of physiological motion effects in fMRI:
RETROICOR. Magn Reson Med 44:162–7.
Goebel R. 2001. Cortex-based real-time fMRI. NeuroImage 13:S129.
Grice GR. 1948. The relation of secondary reinforcement to delayed
reward in visual discrimination learning. J Exp Psychol 38:1–16.
Haller S, Birbaumer N, Veit R. 2010. Real-time fMRI feedback training may improve chronic tinnitus. Eur Radiol 20(3):696–703.
Hamilton JP, Glover GH, Hsu J, Johnson RF, Gotlib IH. 2011. Modulation of subgenual anterior cingulate cortex activity with realtime neurofeedback. Hum Brain Mapp 32(1):22–31.
Hare RD. 2003. Manual for the Revised Psychopathy Checklist.
2nd ed. Toronto (Canada): Multi-Health Systems.
Hinds O, Ghosh S, Thompson TW, Yoo JJ, Whitfield-Gabrieli S,
Triantafyllou C, and others. 2011. Computing moment-tomoment BOLD activation for real-time neurofeedback. Neuroimage 54(1):361–8.
Hinterberger T, Neumann N, Pham M, Kubler A, Grether A,
Hofmayer N, and others. 2004. A multimodal brain-based feedback and communication system. Exp Brain Res 154:521–6.
Hollmann M, Mönch T, Mulla-Osman S, Tempelmann C, Stadler
J, Bernarding J. 2008. A new concept of a unified parameter
management, experiment control, and data analysis in fMRI:
application to real-time fMRI at 3T and 7T. J Neurosci Methods
175:154–62.
Johnson KA, Hartwell K, Lematty T, Borckardt J, Morgan PS, Govindarajan K, and others. 2010. Intermittent “real-time” fMRI
feedback is superior to continuous presentation for a motor
imagery task: a pilot study. J Neuroimaging. Epub ahead of print.
Johnston SJ, Boehm SG, Healy D, Goebel R, Linden DEJ. 2010.
Neurofeedback: a promising tool for the self-regulation of emotion networks. Neuroimage 49:1066–72.
Kesavadas C, Thomas B, Sujesh S, Ashalata R, Abraham M,
Gupta AK, and others. 2007. Real-time functional MR imaging
(fMRI) for presurgical evaluation of paediatric epilepsy. Pediatr
Radiol 37:964–74.
Kotchoubey B, Strehl U, Uhlmann C, Holzapfel S, Konig M,
Froscher W, and others. 2001. Modification of slow cortical
potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42:406–16.
Kuo AY-C, Chiew M, Tam F, Cunningham C, Graham SJ. 2011.
Multiecho coarse voxel acquisition for neurofeedback fMRI.
Magn Res Med 65:715–24.
Laconte SM. 2010. Decoding fMRI brain states in real-time. Neuroimage. Epub ahead of print.
Laconte SM, Peltier SJ, Hu XP. 2007. Real-time fMRI using brainstate classification. Hum Brain Mapp 28:1033–44.
Lang PJ, Bradley MM, Cuthbert BN. 2008. International Affective Picture System (IAPS): Affective Ratings of Pictures and
Instruction Manual. Technical report A-7. Gainesville: University of Florida.
Lawrence E, Su L, Giampietro V, Barker G, Medford N, Dalton J,
and others. 2010. Modulation of the anterior insula using realtime fMRI neural feedback. Presented at the 16th Annual Meeting of the Organization for Human Brain Mapping, Barcelona,
Spain, 486 WTh-PM.
Lee J, O’Leary HM, Park H, Jolesz FA, Yoo S. 2008. Atlas-based
multichannel monitoring of functional MRI signals in real-time:
automated approach. Hum Brain Mapp 29:157–66.
Lee S, Ruiz S, Caria A, Birbaumer N, Sitaram R. 2011. Cerebral
reorganization induced by real-time fMRI feedback training of
the insular cortex: a multivariate investigation. Neurorehabil
Neural Repair 25(3):259–67.
Logothetis NK. 2008. What we can do and what we cannot do with
real-time fMRI. Nature 453:869–78.
Magland JF, Tjoa CW, Childress AR. 2011. Spatio-temporal activity
in real time (STAR): optimization of regional fMRI feedback.
55(3):1044–53.
Mathiak K, Posse S. 2001. Evaluation of motion and realignment
for functional magnetic resonance imaging in real time. Magn
Reson Med 45:167–71.
McCaig RG, Dixon M, Keramatian K, Liu I, Christoff K. 2011.
Improved modulation of merostrolateral prefrontal cortex
using real-time fMRI training and meta-cognitive awareness.
Neuroimage 55(3):1298–305.
Ooi MB, Krueger S, Thomas WJ, Swaminathan SV, Brown TR.
2009. Prospective real-time correction for arbitrary head motion
using active markers. Magn Reson Med 62(4):943–54.
Pearce JM, Hall G. 1980. A model for Pavlovian learning: variations
in the effectiveness of conditioned but not of unconditioned
stimuli. Psychol Rev 87:532–52.
Posse S, Binkofski F, Schneider F, Gembris D, Frings W, Habel U,
and others. 2001. A new approach to measure single-event related
brain activity using real-time fMRI: feasibility of sensory, motor,
and higher cognitive tasks. Hum Brain Mapp 12:25–41.
Posse S, Fitzgerald D, Gao K, Habel U, Rosenberg D, Moore GJ,
and others. 2003. Real-time fMRI of temporolimbic regions
detects amygdala activation during single-trial self-induced
sadness. Neuroimage 18:760–8.
Posse S, Shen Z, Kiselev V, Kemna LJ. 2003. Single-shot T(2)*
mapping with 3D compensation of local susceptibility gradients
in multiple regions. Neuroimage 18(2):390–400.
Posse S, Wiese S, Gembris D, Mathiak K, Kessler C, GrosseRuyken ML, and others. 1999. Enhancement of BOLD-contrast
sensitivity by single-shot multi-echo functional MR imaging.
Magn Reson Med 42:87–97.
Purdon PL, Millan H, Fuller PL, Bonmassar G. 2008. An opensource hardware and software system for acquisition and
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
14
The Neuroscientist XX(X)
real-time processing of electrophysiology during high field
MRI. J Neurosci Methods 175:165–86.
Rescorla RA, Wagner AR. 1972. A theory of Pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement. In: Black AH, Prokasy WF, editors. Classical
Conditioning II: Current Research and Theory. New York:
Appleton Century Crofts: p 64–99.
Rockstroh B, Elbert T, Birbaumer N, Lutzenberger W. 1989.
Slow brain potentials and behavior. 2nd ed. Baltimore: Urban
& Schwarzenberg.
Rota G, Handjaras G, Sitaram R, Birbaumer N, Dogil G. 2010.
Reorganization of functional and effective connectivity during
real-time fMRI-BCI modulation of prosody processing. Brain
Lang. Epub ahead of print.
Rota G, Sitaram R, Veit R, Erb M, Weiskopf N, Dogil G, and others.
2009. Self-regulation of regional cortical activity using real-time
fMRI: the right inferior frontal gyrus and linguistic processing.
Hum Brain Mapp 30:1605–14.
Ruiz S, Sitaram R, Lee S, Soekadar S, Caria A, Veit R, and others
2008. Learned control of insular activity and functional connectivity changes using a fMRI Brain Computer Interface in
Schizophrenia. Presented at the 38th Annual Meeting of the
Society for Neuroscience, Washington, DC.
Scharnowski F, Hutton C, Josephs O, Weiskopf N, Rees G. 2010.
Manipulating visual perception with real-time fMRI based neurofeedback training. Presented at the 16th Annual Meeting of
the Organization for Human Brain Mapping, Barcelona, Spain,
1471 MT-AM.
Scott D, Ross A, Vinberg J, Rekshan W, deCharms C. 2010. Effects
of long term real-time fMRI training in motor cortex. Presented
at the 16th Annual Meeting of the Organization for Human
Brain Mapping, Barcelona, Spain, 1279 MT-AM.
Sitaram R. 2007. fMRI brain-computer interfaces. Presented at
the 15th Annual Conference of the International Society for
Neurofeedback & Research, Current Perspectives In Neuroscience: Neuroplasticity & Neurofeedback, San Diego, CA,
September.
Sitaram R, Lee S, Ruiz S, Rana M, Veit R, Birbaumer N. 2010.
Real-time support vector classification and feedback of multiple
emotional brain states. Neuroimage. Epub ahead of print.
Sitaram R, Veit R, Birte S, Caria A, Gerloff C, Birbaumer N, and
others. In press. Acquired control of ventral premotor cortex
activity by feedback training: an exploratory real-time fMRI
and TMS study. Neurorehabil Neural Repair.
Smyser C, Grabowski TJ, Frank RJ, Haller JW, Bolinger L. 2001.
Real-time multiple linear regression for fMRI supported by
time-aware acquisition and processing. Magn Reson Med
45:289–98.
Sorger B, Peters J, van den Boomen C, Zilverstand A, Reithler
J, Goebel R. 2010. Real-time decoding of the locus of visuospatial attention using multi-voxel pattern classification. Presented at the 16th Annual Meeting of the Organization for
Human Brain Mapping, Barcelona, Spain, 1410 WTh-PM.
Speck O, Hennig J, Zaitsev M. 2006. Prospective real-time sliceby-slice motion correction for fMRI in freely moving subjects.
Magma 19(2):55–61.
Stoeckel L, Chai X, Hinds O, Thompson T, Sinclair P, Gabrieli J,
and others. 2010. Feasibility of real-time fMRI neurofeedback
as a behavioral intervention for nicotine dependence. Presented
at the 16th Annual Meeting of the Organization for Human
Brain Mapping, Barcelona, Spain, 268 WTh-PM.
Strehl U, Leins U, Goth G, Klinger C, Hinterberger T, Birbaumer N.
2006. Self-regulation of slow cortical potentials: a new treatment for children with attention deficit/hyperactivity disorder.
Pediatrics 118:e1530–40.
Tang Y-W, Huang T-Y. 2011. Real-time feedback optimization
of z-shim gradient for automatic compensation of susceptibility-induced signal loss in EPI. Neuroimage 55(4):
1587–92.
Thesen S, Heid O, Mueller E, Schad LR. 2000. Prospective acquisition correction for head motion with image-based tracking
for real-time fMRI. Magn Reson Med 44:457–65.
Uludağ K, Müller-Bierl B, Uğurbil K. 2009. An integrative model
for neuronal activity-induced signal changes for gradient and
spin echo functional imaging. Neuroimage 48:150–65.
Voyvodic JT. 1999. Real-time fMRI paradigm control, physiology,
and behavior combined with near real-time statistical analysis.
Neuroimage 10:91–106.
Walsh V, Cowey A. 2000. Transcranial magnetic stimulation and
cognitive neuroscience. Nat Rev Neurosci 1:73–8.
Weiskopf N, Klose U, Birbaumer N, Mathiak K. 2005. Single-shot
compensation of image distortions and BOLD contrast optimization using multi-echo EPI for real-time fMRI. Neuroimage
24:1068–79.
Weiskopf N, Scharnowski F, Veit R, Goebel R, Birbaumer N,
Mathiak K. 2004. Self-regulation of local brain activity using
real-time functional magnetic resonance imaging (fMRI). J
Physiol Paris 98:357–73.
Weiskopf N, Sitaram R, Josephs O, Veit R, Scharnowski F, Goebel
R, and others. 2007. Real-time functional magnetic resonance
imaging: methods and applications. Magn Reson Imaging
25:989–1003.
Weiskopf N, Veit R, Erb M, Mathiak K, Grodd W, Goebel R, and others. 2003. Physiological self-regulation of regional brain activity
using real-time functional magnetic resonance imaging (fMRI):
methodology and exemplary data. Neuroimage 19:577–86.
Wibral M, Muckli L, Melnikovic K, Scheller B, Alink A, Singer W,
and others. 2007. Time-dependent effects of hyperoxia on the
BOLD fMRI signal in primate visual cortex and LGN. Neuroimage 35:1044–63.
Yoo SS, Guttmann CR, Zhao L, Panych LP. 1999. Real-time adaptive
functional MRI. Neuroimage 10:596–606.
Yoo SS, Jolesz FA. 2002. Functional MRI for neurofeedback: feasibility study on a hand motor task. Neuroreport 13:1377–81.
Yoo SS, O’Leary HM, Fairneny T, Chen NK, Panych LP, Park H,
and others. 2006. Increasing cortical activity in auditory areas
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011
15
Caria and others
through neurofeedback functional magnetic resonance imaging. Neuroreport 17:1273–8.
Zaitsev M, Hennig J, Speck O. 2004. Point spread function
mapping with parallel imaging techniques and high acceleration factors: fast, robust, and flexible method for echoplanar imaging distortion correction. Magn Reson Med
52:1156–66.
Zaitsev M, Dold C, Sakas G, Hennig J, Speck O. 2006. Magnetic
resonance imaging of freely moving objects: prospective realtime motion correction using an external optical motion tracking system. Neuroimage 31:1038–50.
Zhang X, Ross TJ, Salmeron BJ, Yang S, Yang Y, Stein EA. 2011.
Single subject task-related BOLD signal artifact in a real-time
fMRI feedback paradigm. Hum Brain Mapp 32:592–600.
Downloaded from nro.sagepub.com at Biblioteca di Ateneo - Trento on June 9, 2011