No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Functional magnetic resonance imaging in real time (FIRE): Sliding‐window correlation analysis and reference‐vector optimization
Abstract
New algorithms for correlation analysis are presented that allow the mapping of brain activity from functional MRI (fMRI) data in real time during the ongoing scan. They combine the computation of the correlation coefficients between measured fMRI time-series data and a reference vector with “detrending,” a technique for the suppression of non-stimulus-related signal components, and the “sliding-window technique.” Using this technique, which limits the correlation computation to the last N measurement time points, the sensitivity to changes in brain activity is maintained throughout the whole experiment. For increased sensitivity in activation detection a fast and robust optimization of the reference vector is proposed, which takes into account a realistic model of the hemodynamic response function to adapt the parameterized reference vector to the measured data. Based on the described correlation method, real-time fMRI experiments using visual stimulation paradigms have been performed successfully on a clinical MR scanner, which was linked to an external workstation for image analysis. Magn Reson Med 43:259–268, 2000. © 2000 Wiley-Liss, Inc.
... For FWR, the entire signal was regressed before slidingwindow correlation analysis, and the sliding-window averaged correlations were then computed between the regressed signals. The sliding-window regression method was implemented based on the detrending approach described in Equation 4 in Gembris et al. (2000). Sliding-window correlation between two signals x and r with regression of L confounding signals s i was computed as follows: where a i and b i are the regression coefficients. ...
... The in-plane image reconstruction with parallel imaging reconstruction was performed on the scanner. The through-plane reconstruction of MS-EVI was performed online (Posse et al., 2012) on an external Linux workstation using our custom TurboFIRE (Turbo Functional Imaging in Real-time) fMRI analysis tool (Gembris et al., 2000;Posse et al., 2013). SMS-EPI was acquired in six healthy controls using the following: TR/TE-400/35 msec, flip angle-42°, number of scans: 900, number of slices: 32, spatial matrix-64 · 64 · 32, voxel size-3 · 3 · 3 mm 3 , interslice gap-0 mm, SMS factor-8, and scan time: 6:08 min. ...
... The time courses of up to 12 selected ROIs were designated as seed regions to extract time courses for correlation analysis or as reference regions to extract time courses for regression. Voxel-based, sliding-window (window width: W 1 ) partial correlations between the regressed signal time courses x s ! in each pixel and the regressed reference vector r s ! were computed using Equation 7 by adapting the methodology described in Gembris et al. (2000). Sliding-window correlation without regression was based on a fast implementation of Equation 1 (Gembris et al., 2000). ...
Background / Introduction: There is considerable interest in using real-time fMRI for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined limited number of brain regions. This is in part due to the computational demands of traditional seed- and ICA-based methods, in particular when using increasingly available high-speed fMRI methods.
Methods:
This study describes a computationally efficient real-time seed-based resting-state fMRI analysis pipeline using moving averaged sliding-windows with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid.
Results:
Analytical and numerical analyses of averaged sliding-window correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multi-slab echo-volumar imaging and simultaneous multi-slice echo-planar imaging with repetition times as short as 136 ms. High-speed resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W1) within a 2nd level sliding-window (width: W2) enabled monitoring of intra- and inter-network correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the 1st level sliding-window and temporal resolution W1+W2.
Conclusions:
The computational performance and confound tolerance make this seed-based resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies.
... In general, there are several characteristics, such as: The basic gradient technique is often applied to machine learning, as detailed in [22]. Creating a multidimensional plane is an effective classification method [23]. ...
... It showed better performance than Eq. (22). Thus, we do not rely on a single kernel but use their linear combination [25]. ...
Various research fields, such as biology and medicine, have increasingly used techniques for the machine estimation of generated pulses. For example, in biological medicine, classifying information makes it possible to automate the interpretation of incoming data obtained owing to diagnosis, which is necessary when processing large volumes of information arrays. This study aims to develop algorithms that enable the selection of single-type objects in images with subsequent image splitting into a set list of segments with heterogeneous tension, even when the number of reference images is very small. Analysis of existing algorithms allowed us to determine the focus area for developing and improving the efficiency of the algorithms. The existing algorithms show poor performance results in analyzing the dark parts of images, so it makes sense to develop an algorithm for image intensity normalization. The developed algorithm simplifies the procedure of partitioning the training base for the classifier owing to the use of the feature vectors. A random forest algorithm was used for image classification, followed by boundary refinement using a Markov field. The image-splitting algorithm precisely separates parts of the brain structure by applying a Markov field to refine classification results. The proposed classification algorithm showed strong results in comparison with existing algorithms, particularly in the comparison of the Dice criterion. The proposed method shows an average increase of 10% in classification accuracy. One way to improve the presented algorithm is to add texture elements to the feature vector, which allows the identification of distinguishing features of the elements, such as shape and length, which could improve this algorithm for a more accurate classification of substructures. Doi: 10.28991/HEF-2024-05-04-011 Full Text: PDF
... In the early 1990s, the first fMRI experiments were reported (Kwong et al. 1992, Ogawa et al. 1992, and soon thereafter, it was shown that functional brain images could be processed in real time (Cox et al. 1995). This demonstration prompted many efforts to develop tools allowing one to conduct preprocessing steps and statistical analyses in real time (Goddard et al. 1997, Cox & Jesmanowicz 1999, Voyvodic 1999, Gembris et al. 2000, Cohen 2001, Mathiak & Posse 2001, Smyser et al. 2001, Bagarinao et al. 2003. These tools paved the way for the first fMRI neurofeedback experiments (Yoo & Jolesz 2002;Posse et al. 2003;Weiskopf et al. 2003Weiskopf et al. , 2004aWeiskopf 2012). ...
... In these studies, the connectivity between two targeted regions is typically computed in real time and used to provide feedback to participants (Figure 3b). Multiple studies reported that interregional brain connectivity could indeed be successfully manipulated using diverse connectivity metrics such as correlational approaches (Megumi et al. 2015, Yamashita et al. 2017, sliding-window analysis (Gembris et al. 2000), the two-point approach (Ramot et al. 2017, Ramot & Gonzalez-Castillo 2019, dynamic causal modeling (Koush et al. 2013(Koush et al. , 2017b, and tensor-independent component analysis (Koush et al. 2019). Importantly, connectivity neurofeedback has been shown to modulate some outcomes relevant to mental health (Ramot et al. 2017). ...
Multiple mental disorders have been associated with dysregulation of precise brain processes. However, few therapeutic approaches can correct such specific patterns of brain activity. Since the late 1960s and early 1970s, many researchers have hoped that this feat could be achieved by closed-loop brain imaging approaches, such as neurofeedback, that aim to modulate brain activity directly. However, neurofeedback never gained mainstream acceptance in mental health, in part due to methodological considerations. In this review, we argue that, when contemporary methodological guidelines are followed, neurofeedback is one of the few intervention methods in psychology that can be assessed in double-blind placebo-controlled trials. Furthermore, using new advances in machine learning and statistics, it is now possible to target very precise patterns of brain activity for therapeutic purposes. We review the recent literature in functional magnetic resonance imaging neurofeedback and discuss current and future applications to mental health.
Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... Apart from the basic real-time processing capabilities integrated into the hardware of all major MRI vendors, several proprietary, custom in-house and open-source rtfMRI solution sets or toolboxes have been developed at various locations worldwide. These include FIRE (Gembris et al., 2000) and TurboFIRE (Gao and Posse, 2003), scanSTAT (Cohen, 2001), AFNI's real-time plugin (https://afni.nimh.nih.gov/; Cox, 1996), Turbo-BrainVoyager (Brain Innovation, Maastricht, the Netherlands; Goebel, 2012), STAR (Magland et al., 2011), FRIEND (Sato et al., 2013), the FieldTrip toolbox's rtfMRI extension (http://www. ...
... This applies to all preprocessing and image analysis steps applied to fMRI data during the course of a single TR. Sliding-window approaches (Gembris et al., 2000), recursive algorithms (Cox et al., 1995) and approximations can be implemented to contain the required calculation time. ...
Real-time functional magnetic resonance imaging (rtfMRI) allows visualisation of ongoing brain activity of the subject in the scanner. Denoising algorithms aim to rid acquired data of confounding effects, enhancing the blood oxygenation level-dependent (BOLD) signal. Further image processing and analysis methods, like general linear models (GLM) or multivariate analysis, then present application-specific information to the researcher. These processes are typically applied to regions of interest but, increasingly, rtfMRI techniques extract and classify whole brain functional networks and dynamics as correlates for brain states or behaviour, particularly in neuropsychiatric and neurocognitive disorders. We present Neu³CA-RT: a Matlab-based rtfMRI analysis framework aiming to advance scientific knowledge on real-time cognitive brain activity and to promote its translation into clinical practice. Design considerations are listed based on reviewing existing rtfMRI approaches. The toolbox integrates established SPM preprocessing routines, real-time GLM mapping of fMRI data to a basis set of spatial brain networks, correlation of activity with 50 behavioural profiles from the BrainMap database, and an intuitive user interface. The toolbox is demonstrated in a task-based experiment where a subject executes visual, auditory and motor tasks inside a scanner. In three out of four experiments, resulting behavioural profiles agreed with the expected brain state.
... Data analysis was performed using the TurboFIRE real-time fMRI analysis software tool (version 5.14.5.1) (Gembris et al., 2000;Posse et al., 2001) and custom MATLAB (Mathworks Inc., Naticket, MA, USA) scripts. Raw image data were motion corrected and converted to analyze format using TurboFIRE. ...
... Analysis of connectivity was performed in TurboFIRE using windowed seed-based connectivity analysis (wSCA), which employs sliding window correlation analysis (Gembris et al., 2000) with a running mean and standard deviation . This approach, as our previous studies on low frequency connectivity have shown, minimizes the effects of confounds without the need for regression Vakamudi et al., 2014). ...
... In the literature various studies have been researched on estimating optimal data length. One of the popular techniques that gives adequate and usable results is the sliding window streaming model [11][12][13][14]. Gembris et al. studied reference vector analysis in functional magnetic resonance imaging (fMRI). ...
... Gembris et al. studied reference vector analysis in functional magnetic resonance imaging (fMRI). They presented an algorithm to compute correlation coefficients between fMRI time-series and reference time-series using a sliding window [11]. Lee et al. proposed a new sliding window filtering algorithm for incremental mining of association rules in database transaction and evaluate its performance [12]. ...
Household consumption has a significant importance for natural gas wholesale companies. These companies make one-day-ahead forecasting daily. However, there are penalties depending on the error of the estimates. These penalties increase exponentially depending on the error rate. Several studies have been done to develop mathematical models to forecast natural gas consumption and minimize the error rate. However, before mathematical model predictions, a previous step, data preparation, is also important. The data must be prepared correctly before the mathematical model. At this point, prior to the mathematical model, selecting the appropriate data set size has a vital role. In this study, one-day-ahead household natural gas consumption is forecasted for different data sizes. Forecasts have been made for the year 2012. For removing insignificant variables, multiple linear regression (MLR) is applied to all data. In this research, 2 particular scenarios are applied for forecasting. In the first scenario, 2 different data set models are prepared. These sets consist of the data collected 6 weeks before the forecasted day. Daily outcomes are added to the data set and the set is applied in a model called Model A. The other model is depicted based on a sliding window idea having 6 weeks of fixed data size with dynamic data inside (Model W6). For the two models, MLR is applied and error rates are compared. Here, Model A has 7 times higher mean absolute percent error (MAPE) than Model W6. In scenario 2, 6 models are studied and compared for the sliding window approach. The models are named according to the weeks involved (e.g., Model W1, Model W6). MAPEs for Model W3, Model W4, Model W5, and Model W6 are obtained as 11.8%, 6.8%, 7.2%, and 8.1%, respectively. The lowest preday error occurs in the 4-week data model with sliding window approach.
... Arguably the dominant approach used to obtain adaptive functional connectivity estimates involves the use of sliding windows [Hutchison et al., 2013] and this also holds true in the rt-fMRI setting [Esposito et al., 2003;Gembris et al., 2000;Ruiz et al., 2014;Zilverstand et al., 2014]. Such methods are able to obtain adaptive functional connectivity estimates in real-time by only considering a fixed number of past observations, defined as the window. ...
... The rt-SINGLE algorithm is closely related to sliding window methods which have been used extensively in the real-time setting [Esposito et al., 2003;Gembris et al., 2000;Ruiz et al., 2014;Zilverstand et al., 2014]. Extensions of sliding window methods, such as EWMA models, have been successfully applied to offline fMRI studies [Lindquist et al., 2007] and have been shown to be better suited to estimating dynamic functional connectivity [Lindquist et al., 2014]. ...
Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.
... Arguably the dominant approach used to obtain adaptive functional connectivity estimates involves the use of sliding windows [Hutchison et al., 2013] and this also holds true in the rt-fMRI setting [Gembris et al., 2000, Esposito et al., 2003, Ruiz et al., 2014, Zilverstand et al., 2014. Such methods are able to obtain adaptive functional connectivity estimates in real-time by only considering a fixed number of past observations, defined as the window. ...
... The rt-SINGLE algorithm is closely related to sliding window methods which have been employed extensively in the real-time setting [Gembris et al., 2000, Esposito et al., 2003, Ruiz et al., 2014, Zilverstand et al., 2014. Extensions of sliding window methods, such as EWMA models, have been successfully applied to offline fMRI studies [Lindquist et al., 2007] and have been shown to be better suited to estimating dynamic functional connectivity [Lindquist et al., 2014]. ...
There has been an explosion of interest in functional Magnetic Resonance
Imaging (MRI) during the past two decades. Naturally, this has been accompanied
by many major advances in the understanding of the human connectome. These
advances have served to pose novel challenges as well as open new avenues for
research. One of the most promising and exciting of such avenues is the study
of functional MRI in real-time. Such studies have recently gained momentum and
have been applied in a wide variety of settings; ranging from training of
healthy subjects to self-regulate neuronal activity to being suggested as
potential treatments for clinical populations. To date, the vast majority of
these studies have focused on a single region at a time. This is due in part to
the many challenges faced when estimating dynamic functional connectivity
networks in real-time. In this work we propose a novel methodology with which
to accurately track changes in functional connectivity networks in real-time.
We adapt the recently proposed SINGLE algorithm for estimating sparse and
temporally homo- geneous dynamic networks to be applicable in real-time. The
proposed method is applied to motor task data from the Human Connectome Project
as well as to real-time data ob- tained while exploring a virtual environment.
We show that the algorithm is able to estimate signi?cant task-related changes
in network structure quickly enough to be useful in future brain-computer
interface applications.
... The online analysis performed MR image reconstruction, head motion correction in the translational motion, and statistical tests for block or event-related design. Since then, many studies have improved the image acquisition process in terms of data quality, speed, and statistical power Posse et al., 1999Posse et al., , 2001Weiskopf et al., 2005;Yoo et al., 1999) and algorithms (Bagarinao et al., 2003;Cox & Savoy, 2003;Cox et al., 1995;Cox & Jesmanowicz, 1999;Gembris, Taylor, Schor, Frings, Suter, & Posse, 2000;Smyser et al., 2001;Voyvodic, 1999). p0030 Further developments in multiecho echo-planar imaging (mEPI) increased the functional contrast-to-noise ratio (CNR) by sampling multiple echoes in a single shot of radio frequency (RF) pulse (Posse et al., 1999). ...
... p0075 Studies (Caria et al., , 2010Rota et al., 2009) have also used the subtraction method of determining activation maps where signals in the baseline condition are subtracted from the activation condition in a sliding window to provide feedback information in the ROIs. The correlation method (Cox et al., 1995;Gembris et al., 2000;Posse et al., 2001) is applied by computing the correlation coefficient between the time-series of the measured BOLD signal at each voxel and the reference (or design) time-series representing the change in the task conditions, and assessing the coefficients with a specified threshold. The correlation method can be used in either a sliding-window fashion or an incremental fashion. ...
... To holistically identify linguistic expressions tied to rainfall and flooding, we devised an algorithm dedicated to filtering precipitation-related keywords. Initially, we employed the sliding correlation algorithm [63], a method prevalent in time series analysis and signal processing, to discern the correlation between the precipitation and WB K time series. This method identifies time lags or delays between sequences by pinpointing the highest similarity between two-time sequences and establishing their time difference. ...
Understanding the public’s diverse linguistic expressions about rainfall and flood provides a basis for flood disaster studies and enhances linguistic and cultural awareness. However, existing research tends to overlook linguistic complexity, potentially leading to bias. In this study, we introduce a novel algorithm capturing rainfall and flood-related expressions, considering the relationship between precipitation observations and linguistics expressions. Analyzing 210 million social media microblogs from 2017, we identified 594 keywords, 20 times more than usual manually created bag-of-words. Utilizing Large Language Model, we categorized these keywords into rainfall, flood, and other related terms. Semantic features of these keywords were analyzed from the viewpoint of popularity, credibility, time delay, and part-of-speech, finding rainfall-related terms most common-used, flood-related keywords often more time delayed than precipitation, and notable differences in part-of-speech across categories. We also assessed spatial characteristics from keyword and city-centric perspectives, revealing that 49.5% of the keywords have significant spatial correlation with differing median centers, reflecting regional variations. Large and disaster-impacted cities show the richest expression diversity for rainfall and flood-related terms.
... To holistically identify linguistic expressions tied to precipitation and flooding, we devised an algorithm dedicated to filtering precipitation-related keywords. Initially, we employed the sliding correlation algorithm (Gembris, Taylor et al. 2000), a method prevalent in time series analysis and signal processing, to discern the correlation between the precipitation and WB K time series. This method identifies time lags or delays between sequences by pinpointing the highest similarity between two-time sequences and establishing their time difference. ...
... However, unlike sleeping, the participants enduring resting-state fMRI scanning are on stand-by, in which the patterns of brain activity are distinguishable from those in sleep or during goal-directed activity (Deco et al., 2014;Mennes et al., 2011). The sliding-window technique is constantly sensitive to functional activation changes during the entire scan (Gembris et al., 2000), so it can precisely depict the dynamic features of brain activity according to time. Recently, this method has been widely applied to research on BD (Du et al., 2021;Shunkai et al., 2022;Tian et al., 2021;Wang et al., 2022), but is not used to distinguish BD I from BD II, and most methods are based on dynamic functional connectivity. ...
Background
Bipolar disorder is a chronic and highly recurrent mental disorder that can be classified as bipolar type I (BD I) and bipolar type II (BD II). BD II is sometimes taken as a milder form of BD I or even doubted as an independent subtype. However, the fact that symptoms and severity differ in patients with BD I and BD II suggests different pathophysiologies and underlying neurobiological mechanisms. In this study, we aimed to explore the shared and unique functional abnormalities between subtypes.
Methods
The dynamic amplitude of low-frequency fluctuation (dALFF) was performed to compare 31 patients with BD I, 32 with BD II, and 79 healthy controls (HCs). Global dALFF was calculated using sliding-window analysis. Group differences in dALFF among the 3 groups were compared using analysis of covariance (ANCOVA), with covariates of age, sex, years of education, and mean FD, and Bonferroni correction was applied for post hoc analysis. Pearson and Spearman's correlations were conducted between clusters with significant differences and clinical features in the BD I and BD II groups, after which false error rate (FDR) was used for correction.
Results
We found a significant decrease in dALFF values in BD patients compared with HCs in the following brain regions: the bilateral-side inferior frontal gyrus (including the triangular, orbital, and opercular parts), inferior temporal gyrus, the medial part of the superior frontal gyrus, middle frontal gyrus, anterior cingulum, insula gyrus, lingual gyrus, calcarine gyrus, precuneus gyrus, cuneus gyrus, left-side precentral gyrus, postcentral gyrus, inferior parietal gyrus, superior temporal pole gyrus, middle temporal gyrus, middle occipital gyrus, superior occipital gyrus and right-side fusiform gyrus, parahippocampal gyrus, hippocampus, middle cingulum, orbital part of the medial frontal gyrus and superior frontal gyrus. Unique alterations in BD I were observed in the right-side supramarginal gyrus and postcentral gyrus. In addition, dALFF values in BD II were significantly higher than those in BD I in the right superior temporal gyrus and middle temporal gyrus. The variables of dALFF correlated with clinical characteristics differently according to the subtypes, but no correlations survived after FDR correction.
Limitations
Our study was cross-sectional. Most of our patients were on medication, and the sample was limited.
Conclusions
Our findings demonstrated neurobiological characteristics of BD subtypes, providing evidence for BD II as an independent existence, which could be the underlying explanation for the specific symptoms and/or severity and point to potential biomarkers for the differential diagnosis of bipolar subtypes.
... ANTs/). We used two methods to calculate the dynamic FC; sliding-window correlation [29] and two-point algorithm [30]. The sliding-window correlation is a z-transformed Pearson correlation between ROIs within a time window [31]. ...
Objective. Comprehensive denoising is imperative in functional magnetic resonance imaging (fMRI) analysis to reliably evaluate neural activity from the blood oxygenation level dependent signal. In real-time fMRI, however, only a minimal denoising process has been applied and the impact of insufficient denoising on online brain activity estimation has not been assessed comprehensively. This study evaluated the noise reduction performance of online fMRI processes in a real-time estimation of regional brain activity and functional connectivity. Approach. We performed a series of real-time processing simulations of online fMRI processing, including slice-timing correction, motion correction, spatial smoothing, signal scaling, and noise regression with high-pass filtering, motion parameters, motion derivatives, global signal, white matter/ventricle average signals, and physiological noise models with image-based retrospective correction of physiological motion effects (RETROICOR) and respiration volume per time (RVT). Main results. All the processing was completed in less than 400 ms for whole-brain voxels. Most processing had a benefit for noise reduction except for RVT that did not work due to the limitation of the online peak detection. The global signal regression, white matter/ventricle signal regression, and RETROICOR had a distinctive noise reduction effect, depending on the target signal, and could not substitute for each other. Global signal regression could eliminate the noise-associated bias in the mean dynamic functional connectivity across time. Significance. The results indicate that extensive real-time denoising is possible and highly recommended for real-time fMRI applications.
... In the neurofeedback scan, participants were instructed to apply a mental strategy, such as cognitive reappraisal and acceptance ("it is OK" statement), while viewing negative trait words and instructed to regulate their brain activity represented by the sidebars during the "Regulation" block locus and the rTPJ). We evaluated two methods of online connectivity neurofeedback signal, that is, sliding-window correlation (Gembris et al., 2000) and the two-point method (Ramot et al., 2017). The sliding-window correlation is a z-transformed Pearson's correlation between two ROIs within a time window, and widths of a 3-to 10-time points window were evaluated in the simulation. ...
Rumination, repetitively thinking about the causes, consequences, and one's negative affect, has been considered as an important factor of depression. The intrusion of ruminative thoughts is not easily controlled, and it may be useful to visualize one's neural activity related to rumination and to use that information to facilitate one's self‐control. Real‐time fMRI neurofeedback (rtfMRI‐nf) enables one to see and regulate the fMRI signal from their own brain. This proof‐of concept study utilized connectivity‐based rtfMRI‐nf (cnf) to normalize brain functional connectivity (FC) associated with rumination. Healthy participants were instructed to brake or decrease FC between the precuneus and the right temporoparietal junction (rTPJ), associated with high levels of rumination, while engaging in a self‐referential task. The cnf group (n = 14) showed a linear decrease in the precuneus‐rTPJ FC across neurofeedback training (trend [112] = −0.180, 95% confidence interval [CI] −0.330 to −0.031, while the sham group (n = 14) showed a linear increase in the target FC (trend [112] = 0.151, 95% CI 0.017 to 0.299). Although the cnf group showed a greater reduction in state‐rumination compared to the sham group after neurofeedback training (p < .05), decoupled precuneus‐rTPJ FC did not predict attenuated state‐rumination. We did not find any significant aversive effects of rtfMRI‐nf in all study participants. These results suggest that cnf has the capacity to influence FC among precuneus and rTPJ of a ruminative brain circuit. This approach can be applied to mood and anxiety patients to determine the clinical benefits of reduction in maladaptive rumination.
... A variety of tools currently exist that support real-time fMRI to varying degrees, including AFNI (Cox and Jesmanowicz, 1995), FIRE (Gembris et al., 2000), scanSTAT (Cohen, 2001), STAR (Magland et al., 2011), FieldTrip toolbox extension (Oostenveld et al., 2011), Turbo-BrainVoyager (Goebel, 2012), FRIEND (Sato et al., 2013), BART (Hellrung et al., 2015), OpenNFT (Koush et al., 2017), and Neu3CA-RT (Heunis et al., 2018). At a time when implementing real-time fMRI meant researchers had to develop custom in-house software solutions, these tools presented a valuable alternative, catalyzing new experiments, and supporting pioneering early research with real-time fMRI. ...
Increasingly, neuroimaging researchers are exploring the use of real-time functional magnetic resonance imaging (rt-fMRI) as a way to access a participant’s ongoing brain function throughout a scan. This approach presents novel and exciting experimental applications ranging from monitoring data quality in real time, to delivering neurofeedback from a region of interest, to dynamically controlling experimental flow, or interfacing with remote devices. Yet, for those interested in adopting this method, the existing software options are few and limited in application. This presents a barrier for new users, as well as hinders existing users from refining techniques and methods. Here we introduce a free, open-source rt-fMRI package, the Pyneal toolkit, designed to address this limitation. The Pyneal toolkit is python-based software that offers a flexible and user friendly framework for rt-fMRI, is compatible with all three major scanner manufacturers (GE, Siemens, Phillips), and, critically, allows fully customized analysis pipelines. In this article, we provide a detailed overview of the architecture, describe how to set up and run the Pyneal toolkit during an experimental session, offer tutorials with scan data that demonstrate how data flows through the Pyneal toolkit with example analyses, and highlight the advantages that the Pyneal toolkit offers to the neuroimaging community.
... To design an optimal neurofeedback signal for the identified FC associated with an RRS score, we performed a simulation to calculate an online real-time FC feedback signal. Here, two methods of online connectivity neurofeedback signal, sliding-window correlation (Gembris et al., 2000) and the two-point algorithm (Ramot et al., 2017), were evaluated. The sliding-window correlation is a z-transformed Pearson correlation between ROIs within a time window. ...
Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (-6, -54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, -49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.
... To design an optimal neurofeedback signal for the identified FC associated with an RRS score, we performed a simulation to calculate an online real-time FC feedback signal. Here, two methods of online connectivity neurofeedback signal, sliding-window correlation (Gembris et al., 2000) and the two-point algorithm (Ramot et al., 2017), were evaluated. The sliding-window correlation is a z-transformed Pearson correlation between ROIs within a time window. ...
Real-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (−6, −54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, −49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.
... In addition to ROI signals from CSF and WM, the time courses from six motion parameters were also regressed. Voxel-based sliding-window correlation coefficients between the regressed signal time courses in each pixel and the regressed reference vector were computed by adapting the methodology described in Gembris et al. (2000). A maximum of four seed regions were processed in parallel with detrending of six rigid body motion parameters along with WM and CSF time courses. ...
Resting‐state functional magnetic resonance imaging (rsfMRI) is a promising task‐free functional imaging approach, which may complement or replace task‐based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real‐time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)‐echo planar imaging (EPI) with repetition time: 400 ms. Moving‐averaged sliding‐window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting‐state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement‐related false‐positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting‐state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task‐activation in motor cortex, Broca's, and Wernicke's areas was 5–10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real‐time high‐speed rsfMRI for presurgical mapping of eloquent cortex with real‐time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.
... For multi-variate pattern analysis based rt-fMRI, however, these voxel-wise detrending approaches might be sub-optimal (Lee et al., 2015). Correlation-based approaches, such as many functional connectivity methods, could benefit from directly implementing detrending during sliding-window correlation analysis (Gembris et al., 2000). Additionally, some metrices derived from resting state fMRI connectivity (i.e., ALFF, fALFF, and hfALFF) are affected differently by signal drifts and need to be processed accordingly . ...
As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLM window ). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI.
... The main objective of the univariate analysis is to determine the voxels that are significantly correlated with a specific task. The univariate methods include the real-time correlation and general linear model analysis ( Gembris et al., 2000;Bagarinao et al., 2003). In contrast to univariate methods, multivariate or pattern-based methods take into account the pattern information of the brain activity measured simultaneously at many locations ( Cox and Savoy, 2003;Haynes and Rees, 2005b;Kriegeskorte et al., 2006). ...
A tight coupling between the neuronal activity and the cerebral blood flow (CBF) is the motivation of many hemodynamic response (HR)-based neuroimaging modalities. The increase in neuronal activity causes the increase in CBF that is indirectly measured by HR modalities. Upon functional stimulation, the HR is mainly categorized in three durations: (i) initial dip, (ii) conventional HR (i.e., positive increase in HR caused by an increase in the CBF), and (iii) undershoot. The initial dip is a change in oxygenation prior to any subsequent increase in CBF and spatially more specific to the site of neuronal activity. Despite additional evidence from various HR modalities on the presence of initial dip in human and animal species (i.e., cat, rat, and monkey); the existence/occurrence of an initial dip in HR is still under debate. This article reviews the existence and elusive nature of the initial dip duration of HR in intrinsic signal optical imaging (ISOI), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The advent of initial dip and its elusiveness factors in ISOI and fMRI studies are briefly discussed. Furthermore, the detection of initial dip and its role in brain-computer interface using fNIRS is examined in detail. The best possible application for the initial dip utilization and its future implications using fNIRS are provided.
... Correlation in a sliding (or rolling) window is a very common method of statistical evaluation of synchronisation of two data sets used in image processing [1], signal analysis [2], medicine [3], geophysics [4], etc. The functions for calculating parameters in a sliding window (sum, arithmetic mean, correlation, etc.) are included in many software packages (for example, MATLAB, R, S-PLUS) and are actively used in the analysis of time series in financial statistics [5]. ...
Calculating the correlation in a sliding window is a common method of statistical evaluation of the interconnect between two sets of data. And although the calculation of a single correlation coefficient is not resource-intensive and algorithmically complex, sequential computation in a large number of windows on large data sets can take quite a long time. In this case, each value in the data, falling into different windows, will be processed many times, increasing the complexity of the algorithm and the processing time. We took this fact into account and optimized the correlation calculation in the sliding window, reducing the number of operations in the overlapping area of the windows. In addition, we developed a parallel version of the optimized algorithm for the GPU architecture. Experimental studies have shown that for a 7x7 correlation window sliding in one pixel increments, we were able to accelerate the processing of an 12 MPixel image pixels on the GPU by about 60 times compared to the serial version running on the CPU. The article presents an optimized version of the algorithm, a scheme for its parallelization, as well as the results of experimental studies.
... window (Gembris et al., 2000) or incremental approaches (Bagarinao et al., 2003) when analysing 244 time-series data. While time-windowed algorithms allow more sensitivity to temporal brain activity 245 fluctuations by only analysing a recent subset of the acquired data, they are characterised by a 246 decrease in statistical power (Weiskopf et al., 2007b), the converse being the case for incremental or 247 ...
... These methods perform a new GLM based analysis for each new fMRI volume. Other online fMRI methods use correlation analysis (Cox et al., 1995;Gembris et al., 2000) or ICA (Esposito et al., 2003;Chiew, 2013;Soldati et al., 2013a,b). ...
Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.
... To generate the neurofeedback signal, reconstructed and distortion-corrected images were exported directly from the Siemens reconstruction computer via a custom designed TCP/IP-based pipeline to the scanner console and forwarded to an external Linux workstation (Intel(R) Core(TM) i7-3770K CPU @ 3.50 GHz, 16 GB RAM, 1 TB SSD) running TurboFire Version 5.14.4.0 (NeurInsight LLC, Albuquerque, NM, USA; Gembris et al., 2000) inside a virtual Linux machine (1 CPU, 10 GB RAM). TurboFire performed motion correction of the weighted images to the MoCo-target generated in the pretraining session. ...
Within the field of functional magnetic resonance imaging (fMRI) neurofeedback, most studies provide subjects with instructions or suggest strategies to regulate a particular brain area, while other neuro-/biofeedback approaches often do not. This study is the first to investigate the hypothesis that subjects are able to utilize fMRI neurofeedback to learn to differentially modulate the fMRI signal from the bilateral amygdala congruent with the prescribed regulation direction without an instructed or suggested strategy and apply what they learned even when feedback is no longer available. Thirty-two subjects were included in the analysis. Data were collected at 3 Tesla using blood oxygenation level dependent (BOLD)-sensitivity optimized multi-echo EPI. Based on the mean contrast between up- and down-regulation in the amygdala in a post-training scan without feedback following three neurofeedback sessions, subjects were able to regulate their amygdala congruent with the prescribed directions with a moderate effect size of Cohen’s d = 0.43 (95% conf. int. 0.23–0.64). This effect size would be reduced, however, through stricter exclusion criteria for subjects that show alterations in respiration. Regulation capacity was positively correlated with subjective arousal ratings and negatively correlated with agreeableness and susceptibility to anger. A learning effect over the training sessions was only observed with end-of-block feedback (EoBF) but not with continuous feedback (trend). The results confirm the above hypothesis. Further studies are needed to compare effect sizes of regulation capacity for approaches with and without instructed strategies.
... The advent of functional magnetic resonance imaging (fMRI), which offers much better spatial accuracy across the entire brain, raised the question, if smaller anatomically or functionally circumscribed brain regions could be targeted with a suitable fMRI neurofeedback training (Yoo and Jolesz, 2002). Its feasibility was largely improved by the development of real-time (rt) fMRI (Cox et al., 1995;Lee et al., 1998;Voyvodic, 1999;Gembris et al., 2000), accomplishing image reconstruction and activation analysis within the acquisition time of a single-volumetric fMRI dataset. Despite the poor temporal resolution of fMRI and the 6-to 8-s latency of the underlying hemodynamic response, several studies demonstrated successful neurofeedback trainings in brain areas, such as the motor cortex (deCharms et al., 2004;Yoo et al., 2008;Berman et al., 2012;Chiew et al., 2012), the anterior cingulate cortex (Weiskopf et al., 2003;Hamilton et al., 2011), the amygdala (Posse et al., 2003;Zotev et al., 2011), the parahippocampal place area, the supplementary motor area (Weiskopf et al., 2004), the auditory cortex (Yoo et al., 2007), and the insular cortex (Caria et al., 2007(Caria et al., , 2010Johnston et al., 2010). ...
This study investigated the level of self-regulation of the somatomotor cortices (SMCs) attained by an extended functional magnetic resonance imaging (fMRI) neurofeedback training. Sixteen healthy subjects performed 12 real-time functional magnetic resonance imaging neurofeedback training sessions within 4 weeks, involving motor imagery of the dominant right as well as the non-dominant left hand. Target regions of interests in the SMC were individually localized prior to the training by overt finger movements. The feedback signal (FS) was defined as the difference between fMRI activation in the contra- and ipsilateral SMC and visually presented to the subjects. Training efficiency was determined by an off-line general linear model analysis determining the fMRI percent signal changes in the SMC target areas accomplished during the neurofeedback training. Transfer success was assessed by comparing the pre- and post-training transfer task, i.e., the neurofeedback paradigm without the presentation of the FS. Group results show a distinct increase in feedback performance (FP) in the transfer task for the trained group compared to a matched untrained control group, as well as an increase in the time course of the training, indicating an efficient training and a successful transfer. Individual analysis revealed that the training efficiency was not only highly correlated to the transfer success but also predictive. Trainings with at least 12 efficient training runs were associated with a successful transfer outcome. A group analysis of the hemispheric contributions to the FP showed that it is mainly driven by increased fMRI activation in the contralateral SMC, although some individuals relied on ipsilateral deactivation. Training and transfer results showed no difference between left- and right-hand imagery, with a slight indication of more ipsilateral deactivation in the early right-hand trainings.
... Independently for each AAL region, the time-series were divided into short segments using sliding windows (sliding step 5 1 pt) [Gembris et al., 2000]. For the analyses of the real fMRI experiment, the size of the window was set to be equal to the duration of the hemodynamic response function (HRF), that is, approximately 16 s [cf., also Glerean et al., 2012]. ...
Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time-series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time-series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task-dependent changes of connectivity). We present a fully data-driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time-windows was used to identify if/when any area showed stimulus/task-related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time-series (stimulus/task-related networks). Finally, for each network, a second clustering step grouped together all the time-windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task-related activity at specific time-points during the fMRI time-series. We label these configurations: "brain modes" (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
© 2015 Wiley Periodicals, Inc.
... To study the effect of events detected by PFM on the correlation analysis, sliding window correlation analysis [14,30,31] (2s steps, 10 to 240s window lengths) was performed between the average signal from a seed region and all the voxels in a comparison node of the network (Table 1) for each network. This was performed on three sets of data: before and after removal of all events detected by PFM [15] and after removal of only the Coordinated Network Events. ...
Functional brain signals are frequently decomposed into a relatively small set of large scale, distributed cortical networks that are associated with different cognitive functions. It is generally assumed that the connectivity of these networks is static in time and constant over the whole network, although there is increasing evidence that this view is too simplistic. This work proposes novel techniques to investigate the contribution of spontaneous BOLD events to the temporal dynamics of functional connectivity as assessed by ultra-high field functional magnetic resonance imaging (fMRI). The results show that: 1) spontaneous events in recognised brain networks contribute significantly to network connectivity estimates; 2) these spontaneous events do not necessarily involve whole networks or nodes, but clusters of voxels which act in concert, forming transiently synchronising sub-networks and 3) a task can significantly alter the number of localised spontaneous events that are detected within a single network. These findings support the notion that spontaneous events are the main driver of the large scale networks that are commonly detected by seed-based correlation and ICA. Furthermore, we found that large scale networks are manifestations of smaller, transiently synchronising sub-networks acting dynamically in concert, corresponding to spontaneous events, and which do not necessarily involve all voxels within the network nodes oscillating in unison.
... After the rest phase, the result was displayed for 2 sec. The current activation level was determined using a sliding-window technique encompassing only the actual baseline and the BOLD signal 48 . ...
Real-time functional Magnetic Resonance Imaging (rtfMRI) is used mainly for neurofeedback or for brain-computer interfaces (BCI). But multi-site rtfMRI could in fact help in the application of new interactive paradigms such as the monitoring of mutual information flow or the controlling of objects in shared virtual environments. For that reason, a previously developed framework that provided an integrated control and data analysis of rtfMRI experiments was extended to enable multi-site rtfMRI. Important new components included a data exchange platform for analyzing the data of both MR scanners independently and/or jointly. Information related to brain activation can be displayed separately or in a shared view. However, a signal calibration procedure had to be developed and integrated in order to permit the connecting of sites that had different hardware and to account for different inter-individual brain activation levels. The framework was successfully validated in a proof-of-principle study with twelve volunteers. Thus the overall concept, the calibration of grossly differing signals, and BCI functionality on each site proved to work as required. To model interactions between brains in real-time, more complex rules utilizing mutual activation patterns could easily be implemented to allow for new kinds of social fMRI experiments.
... Plenty of methods have been proposed to address this problem, which can be roughly categorized into model-based and model-independent. Correlation analysis (CA), as a model-based method[58,59], combines the subspace modeling of the hemodynamic response and the use of the spatial information to analyze fMRI series. However, the model-based methods are not effective in neuronal pattern analysis when the temporal information is not available. ...
... In real-time fMRI, it is common that only a part of the data is available. An example of this is that the analysis is performed on a sliding time window (Gembris et al., 2000; Nakaia et al., 2006). Real-time fMRI places high demands on computational performance, as for example motion correction has to be applied in real-time. ...
... Instead, the method can add the newly arrived volume to the current statistical parametric map, requiring very little processing. Since the discovery of this method, other methods have been proposed that incrementally update a sliding-window statistical correlation operation [37,66]. The correlation uses the last N volumes (typically 20 volumes), casting off the oldest volume every time a new one is received. ...
... Since the discovery of incremental statistics, other methods have been proposed that incrementally update a sliding-window statistical correlation operation. The correlation uses the last N volumes (typically 20 volumes), casting off the oldest volume every time a new one is received [28,52]. ...
... Most often, fMRI signals are longer (approximately 100 -200 samples), but short signals are of interest in some applications, e.g. where sliding window approaches are used (Gembris et al., 2000). One example is real-time analysis, which is necessary when fMRI is used in a feedback loop, i.e. when the stimuli delivered to the patient or subject depends on previous brain activity. ...
Background:
Bipolar disorder (BD) is difficult to discriminate from major depressive disorder (MDD) before the appearance of mania or hypomania. This study was designed to identify whether patients with MDD and those who converted to BD are distinguishable using dynamic amplitude low-frequency fluctuations (dALFF) and describe the sex effects on the identification of the two disorders.
Methods:
We compared the dALFF values of 35 BD patients who converted from MDD during the 2-year follow-up, 99 MDD patients, and 130 healthy controls (HCs) using two-way ANOVA. Pearson's correlation was used to compare dALFF in dysfunctional brain regions and clinical characteristics.
Results:
A main effect of diagnosis was discovered in the frontal and occipital gyrus. For the main effect of sex, both the left middle occipital gyrus and the medial part of the superior frontal gyrus had higher dALFF values in males compared to females. An interaction of sex and diagnosis effect was observed in the right precentral gyrus. Male MDD patients exhibited a higher dALFF value than male BD patients. Additionally, we discovered a higher dALFF value in females than in males in BD patients. WCST scores were positively associated with dALFF values in the frontal and occipital gyrus in MDD patients. Meanwhile, dALFF values in the occipital gyrus positively correlated with WCST in female MDD patients only.
Limitation:
Most of the participants were on medication and the sample size was small.
Conclusions:
Our study is the first to find the non-neglectable role of sex effects in differentiating BD and MDD at an early stage.
Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity. With advances in signal processing, fMRI data can be analyzed in real time and used in a brain–computer interface (BCI). The feasibility and applications of an fMRI-based BCI (fMRI-BCI) have been studied with increasing frequency in the last decade. This chapter describes aspects of the fMRI-BCI technology and provides a thorough overview of the topic. We review the foundations of the fMRI-BCI, including the fundamental physics and physiology behind fMRI, present a qualitative introduction to relevant statistical analysis techniques, and discuss current applications including control of an external device and therapeutic neurofeedback techniques. Innovations in study design and neuroscience research will increase the suitability for fMRI-BCI to be integrated into clinical methods.
Neurofeedback training using real‐time functional magnetic resonance imaging (rtfMRI‐NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non‐invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI‐NF studies. We found: (a) that less than a third of the studies reported implementing standard real‐time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI‐NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI‐NF studies: (a) report implementation of a set of standard real‐time fMRI denoising steps according to a proposed COBIDAS‐style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community‐informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open‐source rtfMRI‐NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality‐and‐denoising‐in‐rtfmri‐nf.
NF is the process of feeding back real-time information to an individual about his/her ongoing brain activity, so that he/she can train to self-regulate neural substrates of specific behavioral functions. NF has been extensively studied for brain rehabilitation of patients with psychiatric and neurological disorders. However its effective deployment in the clinical armamentarium is being held back by the lack of evidence about its efficacy. One of the possible reason for the debated efficacy of current approaches could be the inherent limitations of single imaging modalities. Indeed, most NF approaches rely on the use of a single modality, EEG and fMRI being the two most widely used. While EEG is inexpensive and benefits from a high temporal resolution (millisecond), its spatial resolution (centimeters) is limited by volume conduction of the head and the number of electrodes. Also source localization from EEG is inaccurate because of the ill-posed inverse problem. In a complementary way, fMRI gives access to the self-regulation of specific brain regions at high spatial resolution (millimeter) but has low temporal resolution (second). Combined EEG-fMRI has proven much valuable for the study of human brain function, however it has rarely been exploited for NF purpose. In the context of NF, combining EEG and fMRI enables cross-modal paradigm evaluation and validation. But more interestingly it opens up avenues for the development of new NF approaches that would mix both modalities, either at the calibration phase or to provide a bimodal NF signal. Combined EEG-fMRI poses numerous challenges with regard to basic physiology, study design, data quality, analysis/integration and interpretation. These challenges are even greater if EEG and fMRI are both to be used simultaneously for online NF computation, because of the real-time constraint and the difficulty to find a task design compatible with EEG and fMRI' diverging natures. The theoretical part of this PhD dissertation aims at identifying methodological aspects that differ between EEG-NF and fMRI-NF and at examining the motivations and strategies for combining EEG and fMRI for NF purpose. Among these combination strategies, we choose to focus on bimodal EEG-fMRI-NF as it seems to be one of the most promising approach and is mostly unexplored. The feasibility of this approach was recently demonstrated and opened an entire new field of investigation. First and foremost, we would like to address the following questions: what is the added value of bimodal NF over unimodal NF; are there any specific mechanisms involved when learning to control two NF signals simultaneously; how to integrate EEG and fMRI to derive a single feedback ? The experimental part of this PhD dissertation therefore focuses on the development and evaluation of methods for bimodal EEG-fMRI-NF. In order to conduct bimodal NF experiments, we start by building up a real-time EEG-fMRI platform. Then in a first study, we compare for the first time bimodal EEG-fMRI-NF with unimodal EEG-NF and fMRI-NF. Eventually, in a second study, we introduce and evaluate two integrated feedback strategies for EEG-fMRI-NF.
NF is the process of feeding back real-time information to an individual about his/her ongoing brain activity, so that he/she can train to self-regulate neural substrates of specific behavioral functions. NF has been extensively studied for brain rehabilitation of patients with psychiatric and neurological disorders. However its effective deployment in the clinical armamentarium is being held back by the lack of evidence about its efficacy.One of the possible reason for the debated efficacy of current approaches could be the inherent limitations of single imaging modalities. Indeed, most NF approaches rely on the use of a single modality, EEG and fMRI being the two most widely used. While EEG is inexpensive and benefits from a high tem- poral resolution (millisecond), its spatial resolution (centimeters) is limited by volume conduction of the head and the number of electrodes. Also source lo- calization from EEG is inaccurate because of the ill-posed inverse problem. In a complementary way, fMRI gives access to the self-regulation of specific brain regions at high spatial resolution (millimeter) but has low temporal resolution (second).Combined EEG-fMRI has proven much valuable for the study of human brain function, however it has rarely been exploited for NF purpose. In the con- text of NF, combining EEG and fMRI enables cross-modal paradigm evaluation and validation. But more interestingly it opens up avenues for the development of new NF approaches that would mix both modalities, either at the calibration phase or to provide a bimodal NF signal. Combined EEG-fMRI poses numer- ous challenges with regard to basic physiology, study design, data quality, analy- sis/integration and interpretation. These challenges are even greater if EEG and fMRI are both to be used simultaneously for online NF computation, because of the real-time constraint and the difficulty to find a task design compatible with EEG and fMRI’ diverging natures.The theoretical part of this PhD dissertation aims at identifying methodologi- cal aspects that differ between EEG-NF and fMRI-NF and at examining the mo- tivations and strategies for combining EEG and fMRI for NF purpose. Among these combination strategies, we choose to focus on bimodal EEG-fMRI-NF as it seems to be one of the most promising approach and is mostly unexplored. The feasibility of this approach was recently demonstrated and opened an en- tire new field of investigation. First and foremost, we would like to address the following questions: what is the added value of bimodal NF over unimodal NF; are there any specific mechanisms involved when learning to control two NF signals simultaneously; how to integrate EEG and fMRI to derive a single feed- back ? The experimental part of this PhD dissertation therefore focuses on the development and evaluation of methods for bimodal EEG-fMRI-NF. In order to conduct bimodal NF experiments, we start by building up a real-time EEG- fMRI platform. Then in a first study, we compare for the first time bimodal EEG-fMRI-NF with unimodal EEG-NF and fMRI-NF. Eventually, in a second study, we introduce and evaluate two integrated feedback strategies for EEG- fMRI-NF.
The widespread use of arterial spin labeling (ASL) for functional neuroimaging, has been hampered by its low signal to noise ratio (SNR) and poor temporal resolution.
In this dissertation, we propose a novel method to optimize the SNR of pseudo-continuous arterial spin labeling (pCASL) technique. pCASL has been the most popular ASL method, however, in this study it is demonstrated through experiment and computer simulation that the SNR of this technique can be degraded due to off-resonance effects. The proposed method can effectively recover the lost SNR of pCASL using the B0 field map information. In our preliminary study the proposed method improved the inversion efficiency of the original pCASL by up to 56%. This method allows the use of pCASL in a wider range of conditions and applications, including real-time fMRI and ultra-high field MRI, where it may have otherwise been impractical.
ASL is traditionally used for measurement of cerebral blood flow (CBF). In this dissertation we also develop a new framework for dynamic imaging of arterial blood volume (aCBV) utilized for functional brain imaging. This method employs the developed optimized pCASL technique, takes advantage of the kinetics of ASL signal and provides a signal, which is primarily determined by arterial blood volume with little or no contributions from the parenchyma. The proposed aCBV ASL approach has several important advantages over existing fMRI techniques. The temporal resolution of the developed aCBV ASL technique is approximately half of the temporal resolution of the conventional CBF ASL. We also found that the activation detection sensitivity of the aCBV ASL was by average 30% higher than that of the CBF ASL. Consequently, aCBV produced wider activated areas compared to CBF. The active areas in the aCBV map were also more focal compared to BOLD.
Background:
The simultaneous acquisition of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) provides both high temporal and spatial resolution when measuring brain activity. A real-time analysis during a simultaneous EEG-fMRI acquisition is essential when studying neurofeedback and conducting effective brain activity monitoring. However, the ballistocardiogram (BCG) artifacts which are induced by heartbeat-related electrode movements in an MRI scanner severely contaminate the EEG signals and hinder a reliable real-time analysis.
New method:
The optimal basis sets (OBS) method is an effective candidate for removing BCG artifacts in a traditional offline EEG-fMRI analysis, but has yet to be applied to a real-time EEG-fMRI analysis. Here, a novel real-time technique based on OBS method (rtOBS) is proposed to remove BCG artifacts on a moment-to-moment basis. Real-time electrocardiogram R-peak detection procedure and sliding window OBS method were adopted.
Results:
A series of simulated data was constructed to verify the feasibility of the rtOBS technique. Furthermore, this method was applied to real EEG-fMRI data to remove BCG artifacts. The results of both simulated data and real EEG-fMRI data from eight healthy human subjects demonstrate the effectiveness of rtOBS in both the time and frequency domains.
Comparison with existing methods:
A comparison between rtOBS and real-time averaged artifact subtraction (rtAAS) was conducted. The results suggest the efficacy and advantage of rtOBS in the real-time removal of BCG artifacts.
Conclusions:
In this study, a novel real-time OBS technique was proposed for the real-time removal of BCG artifacts. The proposed method was tested using simulated data and applied to real simultaneous EEG-fMRI data. The results suggest the effectiveness of this method.
How are languages represented in the human brain? Ideas from neuroscience have increasingly been applied to the study of language, exploring the neural processes involved in acquisition, maintenance and loss of language and languages, and the interaction between languages in bi- and multilingual speakers. With a sharp focus on multilingualism, this culmination of cutting-edge research sheds light on this challenging question. Using data from a variety of experiments, this is the first book length study to offer a new neuroscientific model for analysing multilingualism. Alongside a comprehensive analysis of the theoretical and experimental contributions to the field, it presents new data and analysis obtained from a multilingualism fMRI study. It also includes a unique longitudinal study of second and third language acquisition combined with extensive empirically valid language proficiency data of the subjects. A must-read for researchers and advanced students interested in neurolinguistics, second language acquisition, and bi- and multilingualism.
The ability to detect dynamic changes in brain activity during affective processing within individual subjects in real-time can advance our understanding of the neural mechanisms of emotion, psychiatric illness, and therapeutic intervention. We investigated whether activity in limbic and paralimbic regions elicited by blocks of aversive (AV) and neutral (NEU) pictures can be detected by real-time fMRI. Real-time analysis of signal change during each block revealed that activations in insula and medial frontal cortex were more frequent during AV than NEU epochs. Single subject and group analysis off-line with conventional statistical parametric mapping methods matched the results obtained in real-time. Detecting cortico-limbic brain activation during perception and experience of emotionally salient visual stimuli with real-time fMRI technology is feasible.
Functional magnetic resonance imaging (fMRI) is a noninvasive imaging technique that is able to detect hemodynamic changes associated with neuronal activity in vivo. In the past decade, it has been used extensively for mapping functional neuroanatomy in basic neuroscience and clinical applications. A typical fMRI study includes acquisition of a time series of images (during which the subject is instructed to perform certain tasks) and offline processing and analysis of the image series. In real-time fMRI (rtfMRI), however, image processing and analysis start shortly after the beginning of the scan and then follow the image acquisition procedure as closely as possible. Real-time fMRI provides not only a useful tool for effective quality assurance of fMRI studies, but also opportunities for designing functional examinations with interactive controls and feedback. In this chapter, we describe the basic concept and technical solution of rtfMRI, with a focus on a technique that we have recently developed and implemented on a Siemens 3T MRI scanner. We then demonstrate potential applications of rtfMRI techniques for neurofeedback and motion artifact reduction. In particular, we present a novel method of voluntary head-motion suppression using the feedback of the subject's own motion information in real time. This method was tested and the possible influence on the performance of tasks in fMRI study was assessed utilizing a commonly used working memory task. Our experiments showed that significant suppression of head motion was achieved consistently while the influence on the performance of task and the interference to brain activation could be minimized by careful design of the feedback system.
Functional magnetic resonance imaging (fMRI) has become a common tool in investigating brain activities on human subjects. Although each region of the brain was thought to be independent and responsible only for particular tasks and specialized functions, recent studies have shown that different regions of the brain interact with each other in performing specific tasks or even during the resting state. However, the temporal variations in functional connectivity have been largely overlooked by most studies. This study aims to explore time-varying properties of functional connectivity through comparing results from different correlation and regression analysis methods on a sample set of fMRI data acquired from a visual task. The results clearly show that functional connectivity in the visual task is transient, which suggests that simply assuming a sustained connectivity change during task period might not be sufficient to capture dynamic functional connectivity changes induced by tasks.
This chapter allows noninvasive assessment of brain function with high spatial resolution and whole brain coverage, by measuring changes of the blood oxygenation level- dependent (BOLD) signal. Although the BOLD response is an indirect measure of neural activity, there is accumulating evidence suggesting the close coupling between BOLD and electrical activity of the neurons. It is postulated that the combined effect of increases and decreases in deoxygenated hemoglobin content resulting from changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism following neural firing results in the BOLD signal. fMRI (functional magnetic resonance imaging) data typically consist of time-series of several hundred 3D images across the brain over a period of time, with each image acquired every few seconds. fMRI usability and applications have been somewhat limited by the offline mode of data analysis; due to the large size of the data, very intensive computation involved in preprocessing and analysis of fMRI images.
This paper proposes an online framework to dynamically model the impulse-response function (IRF) of a system having shared-band noises, to facilitate the output prediction in real-time. The online independent-component analysis is performed to un-mix the measured signal. The automatic recognition of the anticipated IRF, amongst the unmixed signals, is achieved by proposing a peak-detection-&-correlation technique. A mathematical model of the acquired IRF is, then, dynamically identified by the subspace-based state-space method. The validity of the proposed methodology is demonstrated by a simulation study where the anticipated IRF is blindly identified and then modelled with an accuracy of 92%. The framework has the potential of online modelling of a system having shared-band noises.
A simple method for reducing the uncertainty intervals of models describing the maturation of Venturia inaequalis (Cooke) Wint ascospores is described. The number of trapped ascospores related to the current accumulated degree days of the season, and the confidence interval predicted with the historical ascospore release datasets, were used to re-calculate the confidence interval for the ascospore proportion for the rest of the season. The historical uncertainty in ascospore proportion was described with logistic functions fitted to upper and lower confidence limits of the mean of fitted values for several years of ascospore release data. The re-calculated confidence interval was derived by the determination of the total number of ascospores that would be trapped during the season, if the current day cumulative release were at either the upper or lower confidence limit of the historical data. The method was examined by applying three models of ascospore maturation and using ascospore trap and weather data collected in Skierniewice, Poland during the period from 2005 to 2009. The historical uncertainties were established by the calibration of models to the data from 2005–2007. The effectiveness of the method was simulated using data of 2008 and 2009. In these years, the re-calculated confidence interval was never greater than the historical confidence interval and was often 3–4 times smaller than it, even though ascospore release observations in 2009 strongly differed from those of the years 2005–2007. In conclusion, the proposed method may be an effective approach to a more precise description of V. inaequalis ascospores maturation.
Real-time functional magnetic resonance imaging (fMRI) was used to quantify activation in ventral prefrontal areas during symptom provocation in children with obsessive compulsive disorder (OCD) and in normal controls. Significant activation in ventral prefrontal areas was found in patients as compared to controls in single trials in real-time. This was consistent with self-ratings of anxiety and contamination. Further analysis with statistical parametric mapping identified activation in anterior cingulate, medial prefrontal cortex and orbital-frontal cortex in the majority of patients compared to controls. Introduction Despite OCD being a severe, highly prevalent and often chronically disabling illness with onset in childhood in up to 80 % of all cases (I), the developmental underpinnings of OCD in children have not yet been studied with iMRI. A previous study suggests a selective abnormality in ventral prefrontal cortex circuitry based on deficits in oculomotor response inhibition tasks in pediatric OCD patients vs. controls (2). We have also found a volumetric abnormality in OCD patients in ventral prefrontal cortex: increased anterior cingulate volume was correlated with OCD symptom severity (3). Previous neuroimaging studies using symptom provocation in adults (4-6) have implicated a major role of orbital frontal cortex and anterior cingulate association with OCD symptoms. Therefore, we hypothesized increased activation in ventral prefrontal cortical regions, specifically anterior cingulate, during OCD symptom provocation as compared to when neutral stimuli were presented. Methods Five OCD symptomatic children (age range: 10-19 years, 2 males, 3 female) participated in the study. Two patients were scanned twice. All patients were on medication: three patients on fluoxetine, one patient on sertraline, one patient on flnvoxamine.Two healthy children (age: 13, 18 years, 1 female, 1 male) were studied as controls. Each symptom provocation trial started with the subject holding a clean tissue (wet or dry), we then introduced a contaminated tissue (either with effluvium or toilet water). Self-report of anxiety (Anxiety Analogue Scale, AAS) and OCD symptoms (OCD Analogue Scale, OCDAS) using ten point analogue scales were obtained before entering the magnet and after each trial. Using a 1.5 T Siemens Vision scanner with gradient booster, near-axial Echo-Planar-Images were acquired (TE: 40 ms, T R 3 s, matrix: 32x32, FOV: 200, slice thickness: 6 mm, no. slices: 16, no. scans: 33 (single block design) or 53 (dual block design)). Head motion was restricted using either foam pads or expanding foam that was molded around the head. Real-time fMRI analysis with FIRE software package (5,6) included motion correction, sliding-window correlation analysis, image filtering and ROI time course analysis. Two SPM (7) analyses were done, using motion correction, spatial normalization, smoothing, general-linear model, amplitude threshold at either p=0.05 (corrected) and p=O.OOOl (uncorrected) with extent threshold=2 voxels in the uncorrected analysis.
We describe a new capability for analyzing and visualizing brain activity while a subject is performing a cognitive or perceptual task in a magnetic resonance scanner. This online capability integrates geographically distributed hardware (scanner, parallel computer, visualization platform) via commodity networking. We describe how we parallelized the existing analysis software and present results for the three main classes of parallel platforms. Finally we discuss some of the new possibilities this online capability presents for scientific studies and clinical intervention.
Functional magnetic resonance imaging (fMRI) using blood oxygenation level-dependent (BOLD) contrast has progressed rapidly and is commonly used to study function in many regions of the human brain. This paper introduces a method for characterizing the linear and nonlinear properties of the hemodynamic response. Such characterization is essential for accurate prediction of time-course behavior. Linearity of the BOLD response was examined in the primary visual cortex for manipulations of the stimulus amplitude and duration. Stimuli of 1, 2, 4, and 8 s duration (80% contrast) and 10, 20, 40, and 80% contrast (4 s duration) were used to test the hemodynamic response. Superposition of the obtained responses was performed to determine if the BOLD response is nonlinear. The nonlinear characteristics of the BOLD response were assessed using a Laplacian linear system model cascaded with a broadening function. Discrepancies between the model and the observed response provide an indirect measure of the nonlinearity of the response. The Laplacian linear system remained constant within subjects so the broadening function can be used to absorb nonlinearities in the response. The results show that visual stimulation under 4 s in duration and less than 40% contrast yield strong nonlinear responses.
A typical scene contains many different objects, but the capacity of the visual system to process multiple stimuli at a given time is limited. Thus, attentional mechanisms are required to select relevant objects from among the many objects competing for visual processing. Evidence from functional magnetic resonance imaging (MRI) in humans showed that when multiple stimuli are present simultaneously in the visual field, their cortical representations within the object recognition pathway interact in a competitive, suppressive fashion. Directing attention to one of the stimuli counteracts the suppressive influence of nearby stimuli. This mechanism may serve to filter out irrelevant information in cluttered visual scenes.
Cerebral hemodynamic responses to brief periods of neural activity are delayed and dispersed in time. The specific shape of these responses is of some importance to the design and analysis of blood oxygenation level-dependent (BOLD), functional magnetic resonance imaging (fMRI) experiments. Using fMRI scanning, we examine here the characteristics and variability of hemodynamic responses from the central sulcus in human subjects during an event-related, simple reaction time task. Specifically, we determine the contribution of subject, day, and scanning session (within a day) to variability in the shape of evoked hemodynamic response. We find that while there is significant and substantial variability in the shape of responses collected across subjects, responses collected during multiple scans within a single subject are less variable. The results are discussed in terms of the impact of response variability upon sensitivity and specificity of analyses of event-related fMRI designs.
Recursive least squares (RLS) estimations are used extensively in many signal processing and control applications. In this paper we consider RLS with sliding data windows involving multiple (rank k) updating and downdating computations. The least squares estimator can be found by solving a near-Toeplitz matrix system at each step. Our approach is to employ the preconditioned conjugate gradient method with circulant preconditioners to solve such systems. Here we iterate in the time domain (using Toeplitz matrix-vector multiplications) and precondition in the Fourier domain, so that the fast Fourier transform (FFT) is used throughout the computations. The circulant preconditioners are derived from the spectral properties of the given input stochastic process. When the input stochastic process is stationary, we prove that with probability 1. the spectrum of the preconditioned system is clustered around 1 and the method converges superlinearly provided that a sufficient number of data samples are taken, i.e., the length of the sliding window is sufficiently long. In the case of point-processing (k = 1), our method requires O(n log n) operations per adaptive filter input where n is the number of least squares estimators. In the case of block-processing (k ≥ n), our method requires only O(log n) operations per adaptive filter input. A simple method is given for tracking the spectral condition number of the data matrix at each step, and numerical experiments are reported in order to illustrate the effectiveness of our FFT-based method for fast RLS filtering.
An approach to solving continuous global optimization problems was developed. It builds on two innovative concepts, subenergy
tunneling and non-Lipschitzian terminal repellers, to ensure escape from local minima in a fast, reliable, and computationally
efficient manner. The generally applicable methodology is embodied in the TRUST (terminal repeller unconstrained subenergy
tunneling) algorithm, which is deterministic, scalable, and easy to implement. Benchmark results show that TRUST is faster
and more accurate than previously reported global optimization techniques. An application of TRUST to a large-scale exploratory
seismology problem of substantial computational complexity (that is, residual statics corrections) is also reported.
Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor—cortex and compared with more conventional image—subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.
A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. The method involves testing for correlations between sensory input and the hemodynamic response after convolving the sensory input with an estimate of the hemodynamic response function. This estimate is obtained without reference to any assumed input. To lend the approach statistical validity, it is brought into the framework of statistical parametric mapping by using a measure of cross-correlations between sensory input and hemodynamic response that is valid in the presence of intrinsic autocorrelations. These autocorrelations are necessarily present, due to the hemodynamic response function or temporal point spread function. © 1994 Wiley-Liss, Inc.
A recursive algorithm suitable for functional magnetic resonance imaging (FMRI) calculations is presented. The correlation coefficient of a time course of images with a reference time series, with the mean and any linear trend projected out, may be computed with 22 operations per voxel, per image; the storage overhead is four numbers per voxel. A statistical model for the FMRI signal is presented, and thresholds for the correlation coefficient are derived from it. Selected images from the first real-time functional neuroimaging experiment (at 3 Tesla) are presented. Using a 50-MHz workstation equipped with a 14-bit analog-to-digital converter, each echo planar image was acquired, reconstructed, correlated, thresh-olded, and displayed in pseudocolor (highlighting active regions in the brain) within 500 ms of the RF pulse.
A recursive, fading memory filter for time-continuous and time-discrete systems is presented as a means for overcoming the destructive influence of model errors in Kalman filter applications that lead to the occurrence of divergence. This fading memory filter is shown to be uniformly asymptotically stable under basically the same conditions as the Kalman filter and bounds on the error covariance matrix of the filter are given. An adaptive procedure for implementing the procedure is discussed in terms of a scalar example. The ease of implementation of this filter and the highly satisfactory nature of numerical results indicate the efficacy of the procedure as a desirable recursive data processing method.
Do you want easy access to the latest methods in scientific computing? This greatly expanded third edition of Numerical Recipes has it, with wider coverage than ever before, many new, expanded and updated sections, and two completely new chapters. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. Co-authored by four leading scientists from academia and industry, Numerical Recipes starts with basic mathematics and computer science and proceeds to complete, working routines. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Highlights of the new material include: a new chapter on classification and inference, Gaussian mixture models, HMMs, hierarchical clustering, and SVMs; a new chapter on computational geometry, covering KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres; interior point methods for linear programming; MCMC; an expanded treatment of ODEs with completely new routines; and many new statistical distributions.
"Pt.1. Consists of papers published in the 1960s -- Pt.2. Originally appeared as a special issue for Mar. 1983 of IEEE transactions on automatic control" Incluye índice
Image processing strategies for functional magnetic resonance imaging (FMRI) data sets acquired using a gradient-recalled echo-planar imaging sequence are considered. The analysis is carried out using the mathematics of vector spaces. Data sets consisting of N sequential images of the same slice of brain tissue are analyzed in the time-domain and also, after Fourier transformation, in the frequency domain. A technique for thresholding is introduced that uses the shape of the response in a pixel compared with the shape of a reference waveform as the decision criterion. A method is presented to eliminate drifts in data that arise from subject movement. The methods are applied to experimental FMRI data from the motor-cortex and compared with more conventional image-subtraction methods. Several finger motion paradigms are considered in the context of the various image processing strategies. The most effective method for image processing involves thresholding by shape as characterized by the correlation coefficient of the data with respect to a reference waveform followed by formation of a cross-correlation image. Emphasis is placed not only on image formation, but also on the use of signal processing techniques to characterize the temporal response of the brain to the paradigm.
A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.
A rapid method of generating functional scout images to map cortical activity is presented. Maps were produced immediately "on-line" on the scanner's console using fast low-angle shot gradient-recalled echo and echo-planar imaging sequences without special hardware. To demonstrate the technique, raw data collected during photic stimulation and dark control periods were subtracted through phase alternation of the receiver with constant transmitted radio frequency phase. A blood oxygen level-dependent signal difference map was produced by magnitude reconstruction of the resulting complex-difference data. Maps improved in contrast with an increasing number of stimulation-control cycles and showed similar areas of activation to traditional fMRI processing methods. Such a functional scout procedure allows rapid localization of brain function within the anatomical region of interest. From these functional scout images, slices or voxels may be planned by using the scanner manufacturers prescription tools for further specialized studies.
Using a model of the functional MRI (fMRI) impulse response based on published data, we have demonstrated that the form of the fMRI response to stimuli of freely varied timing can be modeled well by convolution of the impulse response with the behavioral stimulus. The amplitudes of the responses as a function of parametrically varied behavioral conditions are fitted well using a piecewise linear approximation. Use of the combined model, in conjunction with correlation analysis, results in an increase in sensitivity for the MRI study. This approach, based on the well-established methods of linear systems analysis, also allows a quantitative comparison of the response amplitudes across subjects to a broad range of behavioral conditions. Fit parameters, derived from the amplitude data, are relatively insensitive to a variety of MRI-related artifacts and yield results that are compared readily across subjects.
In this paper we introduce the idea of explaining responses, in one cortical area, in terms of an interaction between the influence of another area and some experimental (sensory or task-related) parameter. We refer to these effects as psychophysiological interactions and relate them to interactions based solely on experimental factors (i.e., psychological interactions), in factorial designs, and interactions among neurophysiological measurements (i.e., physiological interactions). We have framed psychophysiological interactions in terms of functional integration by noting that the degree to which the activity in one area can be predicted, on the basis of activity in another, corresponds to the contribution of the second to the first, where this contribution can be related to effective connectivity. A psychophysiological interaction means that the contribution of one area to another changes significantly with the experimental or psychological context. Alternatively these interactions can be thought of as a contribution-dependent change in regional responses to an experimental or psychological factor. In other words the contribution can be thought of as modulating the responses elicited by a particular stimulus or psychological process. The potential importance of this approach lies in (i) conferring a degree of functional specificity on this aspect of effective connectivity and (ii) providing a model of modulation, where the contribution from a distal area can be considered to modulate responses to the psychological or stimulus-specific factor defining the interaction. Although distinct in neurobiological terms, these are equivalent perspectives on the same underlying interaction. We illustrate these points using a functional magnetic resonance imaging study of attention to visual motion and a position emission tomography study of visual priming. We focus on interactions among extrastriate, inferotemporal, and posterior parietal regions during visual processing, under different attentional and perceptual conditions.
Functional MRI (fMRI) methods have been demonstrated to noninvasively identify motor-sensory, visual, and other areas of eloquent cortex for guiding surgical intervention. Typically, fMRI data are acquired preoperatively during a conventional surgical planning MRI examination. Unlike direct cortical stimulation at the time of surgery, however, preoperative fMRI methods do not account for the potential movement of tissues (relative to the time of functional imaging) that may occur in the surgical suite as a direct result of the intervention. Recently, an MRI device has been demonstrated for use in the surgical suite that has the potential to reduce the extent of cortical exposure required for the intervention. However, the invasive requirements of cortical mapping may supersede the invasive requirements of the surgical intervention itself. Consequently, we demonstrate here a modification to the intraoperative MRI device that facilitates a noninvasive, real-time, functional MR examination in the surgical suite.
Vascular responses to neural activity are exploited as the basis of a number of brain imaging techniques. The vascular response is thought to be too slow to resolve the temporal sequence of events involved in cognitive tasks, and hence, imaging studies of mental chronometry have relied on techniques such as the evoked potential. Using rapid functional MRI (fMRI) of single trials of two simple behavioral tasks, we demonstrate that while the microvascular response to the onset of neural activity is delayed consistently by several seconds, the relative timing between the onset of the fMRI responses in different brain areas appears preserved. We examined a number of parameters that characterize the fMRI response and determined that its onset time is best defined by the inflection point from the resting baseline. We have found that fMRI onset latencies determined in this manner correlate well with independently measurable parameters of the tasks such as reaction time or stimulus presentation time and can be used to determine the origin of processing delays during cognitive or perceptual tasks with a temporal accuracy of tens of milliseconds and spatial resolution of millimeters.
There has been a good deal of controversy over whether attention influences area V1-the first cortical area onto which information from the retina is projected. Attention to motion has been found to modulate monkey area MT and the human homolog of MT/MST. Here we show that activation of V1 by attention to motion is task dependent. Our stimulus consisted of a group of translating random dots superimposed over another group of random dots executing expansion motion. Subjects were instructed to pay attention selectively to the translation, expansion, or neither in particular (passive condition). The activity in the human MT/MST homolog measured by functional magnetic resonance imaging (fMRI) was significantly higher in both the translation and the expansion conditions than in the passive condition, while the activity in area V1 was significantly higher only in the translation condition. These results show that attention to motion modulates area V1, and more interestingly that high-level cognitive processing such as attention may directly or indirectly determine the retroactive extent of feedback within the motion pathway in a manner dependent on the type of motion attended.
Gradual drifting of baseline signal intensity is common in functional MRI (fMRI) time course data. Methods for dealing with this effect are studied.
Simulations and fMRI data are used to study three statistical models that account for baseline drift. A method is proposed in which the time course data are linear least-squares fit to a reference function that includes the slope of the baseline drift as a free parameter.
It is shown that the least-squares method is equivalent to cross-correlation with Gram-Schmidt orthogonalization. Additionally, it is shown that certain paradigm designs improve the sensitivity of statistical tests when using any of the drift correction methods commonly employed. The least-squares method results in a variety of useful parameters such as activation amplitude, with a well characterized error.
Very simple techniques can effectively account for observed drifts. It is important to design paradigms that are symmetric about the midpoint of the time series. In calculating confidence levels, a proper statistical model that accounts for baseline drifts is necessary to ensure accurate confidence level assessment.
This study presents an integrated approach to on-line fMRI data processing that combines real-time paradigm control and real-time MR image statistical analysis with nearly real-time integration of fMRI behavioral and physiological data. The real-time paradigms involve accurate timing control of multiple independent processing streams for stimulus presentation, physiological monitoring, behavioral response recording, and scanner synchronization. The real-time image analysis provides high resolution MR image reconstruction, head motion detection, translational motion correction, and t test statistical activation maps for either block design or single-trial based paradigms. The near real-time analysis allows physiological and behavioral data collected during a paradigm to be combined with the MR time series and provides extended data filtering and statistical processing within a few minutes after the end of the scan. This integrated approach improves fMRI reliability for both clinical and research studies.
Conventional methods of supervised learning are inevitably faced with the problem of local minima; evidence is presented that second order methods such as the conjugate gradient and quasi-Newton techniques are particularly susceptible to being trapped in sub-optimal solutions. A new technique, expanded range approximation (ERA), is presented, which by the use of a homotopy on the range of the target outputs allows supervised learning methods to find a global minimum of the error function in almost every case. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.
Nonlinear Regression for FMRI time series analysis
- Bd Ward
- H Garavan
- Tj Ross
- As Bloom
- Rw Cox
- Stein
Ward BD, Garavan H, Ross TJ, Bloom AS, Cox RW, Stein EA. Nonlinear Regression for FMRI time series analysis. In: Proceedings, 4th International Conference on Functional Mapping of the Human Brain, Montreal, 1998. p 767.
Diploma thesis, Echtzeitverarbeitung von funktionaler Kernspin-Tomographie
- S Schor
Schor S. Diploma thesis, Echtzeitverarbeitung von funktionaler Kernspin-Tomographie, Giessen/Friedberg (Germany), 1997.
Methodology of fast correlation analysis for real-time fMRI experiments
- D Gembris
- Jg Taylor
- S Schor
- V Kiselev
- D Suter
- S Posse
Gembris D, Taylor JG, Schor S, Kiselev V, Suter D, Posse S. Methodology of fast correlation analysis for real-time fMRI experiments. In:
Proceedings, ISMRM, 6th Annual Meeting, Sydney, 1998. p 1486.
Real time fMRI on a clinical whole body scanner—Single trial detection of sensorimotor stimulation and visual recall activation
- S Posse
- S Schor
- D Gembris
- E Müller
- M Peyerl
- R Kroeker
- Ml Grosse-Ruyken
- B Elghahwagi
- Jg Taylor
Posse S, Schor S, Gembris D, Müller E, Peyerl M, Kroeker R, Grosse-
Ruyken ML, Elghahwagi B, Taylor JG. Real time fMRI on a clinical
whole body scanner—Single trial detection of sensorimotor stimulation and visual recall activation. In: Proceedings, ISMRM, 6th Annual
Meeting, Sydney, 1998. p 162.
Functional imaging in real-time (FIRE)
- S Schor
- D Gembris
- Jg Taylor
- M Peyerl
- E Müeller
- S Posse
Schor S, Gembris D, Taylor JG, Peyerl M, Müeller E, Posse S. Functional imaging in real-time (FIRE). In: Proceedings, ISMRM, 6th Annual
Meeting, Sydney, 1998. p 1140.
Whole brain real-time fMRI
- Rw Cox
- A Jesmanowicz
Cox RW, Jesmanowicz A. Whole brain real-time fMRI. In: Proceedings,
ISMRM, 6th Annual Meeting, Sydney, 1998. p 295.
Progress in connectionist-based information systems (ICONIP'97)
- Rajapakse J
- Kruggel F
- von Cramon DY