Enhanced phase regression with savitzky‐golay filtering for high‐resolution BOLD fMRI

Phase regression exploits the temporal evolution of phase in individual voxels to suppress blood oxygenation level dependent (BOLD) signal fluctuations caused by larger vessels and draining veins while preserving signal changes from microvascular effects. However, this process does not perform well when phase time series have low signal‐to‐noise ratios because of high levels of physiological noise. We demonstrate that Savitzky‐Golay filters may be used to recover the underlying change in phase and completely restore the efficacy of phase regression. We do not make a priori assumptions regarding phase evolution and perform a data‐driven exploration of parameter space to select the Savitzky‐Golay filter parameters that minimize temporal variance in each voxel after phase regression. This approach is shown to work well on data acquired with single‐shot and multi‐shot pulse sequences, and should therefore be useful for both human and animal gradient‐echo fMRI at high spatial resolutions at high fields. The ability to improve the spatial specificity of BOLD activation may be especially advantageous for clinical applications of fMRI that rely upon the accuracy of individual subject's activation maps to assist with presurgical planning and clinical decision‐making. Enhanced phase regression with Savitzky‐Golay filtering may also find other uses in analyses of resting state functional connectivity. Hum Brain Mapp 35:3832–3840, 2014. © 2014 Wiley Periodicals, Inc.     

Default mode network deactivation during odor–visual association

Default mode network (DMN) deactivation has been shown to be functionally relevant for goal‐directed cognition. In this study, the DMN's role during olfactory processing was investigated using two complementary functional magnetic resonance imaging (fMRI) paradigms with identical timing, visual‐cue stimulation, and response monitoring protocols. Twenty‐nine healthy, non‐smoking, right‐handed adults (mean age = 26 ± 4 years, 16 females) completed an odor–visual association fMRI paradigm that had two alternating odor + visual and visual‐only trial conditions. During odor + visual trials, a visual cue was presented simultaneously with an odor, while during visual‐only trial conditions the same visual cue was presented alone. Eighteen of the twenty‐nine participants (mean age = 27.0 ± 6.0 years, 11 females) also took part in a control no‐odor fMRI paradigm that consisted of a visual‐only trial condition which was identical to the visual‐only trials in the odor–visual association paradigm. Independent Component Analysis (ICA), extended unified structural equation modeling (euSEM), and psychophysiological interaction (PPI) were used to investigate the interplay between the DMN and olfactory network. In the odor–visual association paradigm, DMN deactivation was evoked by both the odor + visual and visual‐only trial conditions. In contrast, the visual‐only trials in the no‐odor paradigm did not evoke consistent DMN deactivation. In the odor–visual association paradigm, the euSEM and PPI analyses identified a directed connectivity between the DMN and olfactory network which was significantly different between odor + visual and visual‐only trial conditions. The results support a strong interaction between the DMN and olfactory network and highlights the DMN's role in task‐evoked brain activity and behavioral responses during olfactory processing. Hum Brain Mapp 38:1125–1139, 2017. © 2016 Wiley Periodicals, Inc.     

Partitioning of physiological noise signals in the brain with concurrent near-infrared spectroscopy and fMRI

The blood–oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.     

A correlation‐based method for extracting subject‐specific components and artifacts from group‐fMRI data

We present a simple but effective correlation‐based method (maxCorr) for extracting subject‐specific components from group‐fMRI data. The method finds signal components that correlate maximally with the data set of one subject and minimally with the data sets of the other subjects. We show that such subject‐specific components are often related to movement and physiological noise (e.g. cardiac cycle, respiration). We further demonstrate that removing the most subject‐specific components for each subject reduces the overall data variance and improves the statistical identification of true fMRI activations. We compare the performance of maxCorr with CompCor, a commonly used artifact‐finding method in fMRI analysis. We show that maxCorr is less likely than CompCor to remove actual stimulus‐related activity, especially when no information about the stimulus is available. MaxCorr operates without stimulus information and is therefore well suitable for analyses of fMRI experiments employing naturalistic stimuli, such as movies, where stimulus regressors are difficult to construct, and for brain decoding techniques benefiting from reduced subject‐specific variance in each subject's data.     

Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling

The study of human olfaction is complicated by the myriad of processing demands in conscious perceptual and emotional experiences of odors. Combining functional magnetic resonance imaging with convergent multivariate network analyses, we examined the spatiotemporal behavior of olfactory‐generated blood‐oxygenated‐level‐dependent signal in healthy adults. The experimental functional magnetic resonance imaging (fMRI) paradigm was found to offset the limitations of olfactory habituation effects and permitted the identification of five functional networks. Analysis delineated separable neuronal circuits that were spatially centered in the primary olfactory cortex, striatum, dorsolateral prefrontal cortex, rostral prefrontal cortex/anterior cingulate, and parietal‐occipital junction. We hypothesize that these functional networks subserve primary perceptual, affective/motivational, and higher order olfactory‐related cognitive processes. Results provided direct evidence for the existence of parallel networks with top‐down modulation for olfactory processing and clearly distinguished brain activations that were sniffing‐related versus odor‐related. A comprehensive neurocognitive model for olfaction is presented that may be applied to broader translational studies of olfactory function, aging, and neurological disease. Hum Brain Mapp 35:2055–2072, 2014. © 2013 Wiley Periodicals, Inc .     

Brain atrophy can introduce age‐related differences in BOLD response

Use of functional magnetic resonance imaging (fMRI) in studies of aging is often hampered by uncertainty about age‐related differences in the amplitude and timing of the blood oxygenation level dependent (BOLD) response (i.e., hemodynamic impulse response function (HRF)). Such uncertainty introduces a significant challenge in the interpretation of the fMRI results. Even though this issue has been extensively investigated in the field of neuroimaging, there is currently no consensus about the existence and potential sources of age‐related hemodynamic alterations. Using an event‐related fMRI experiment with two robust and well‐studied stimuli (visual and auditory), we detected a significant age‐related difference in the amplitude of response to auditory stimulus. Accounting for brain atrophy by circumventing spatial normalization and processing the data in subjects' native space eliminated these observed differences. In addition, we simulated fMRI data using age differences in brain morphology while controlling HRF shape. Analyzing these simulated fMRI data using standard image processing resulted in differences in HRF amplitude, which were eliminated when the data were analyzed in subjects' native space. Our results indicate that age‐related atrophy introduces inaccuracy in co‐registration to standard space, which subsequently appears as attenuation in BOLD response amplitude. Our finding could explain some of the existing contradictory reports regarding age‐related differences in the fMRI BOLD responses. Hum Brain Mapp 38:3402–3414, 2017. © 2017 Wiley Periodicals, Inc.     

Detection of physiological noise in resting state fMRI using machine learning

We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR—a popular physiological noise removal tool—the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data. Hum Brain Mapp , 2013. © 2011 Wiley Periodicals, Inc.     

Tonotopic maps in human auditory cortex using arterial spin labeling

A tonotopic organization of the human auditory cortex (AC) has been reliably found by neuroimaging studies. However, a full characterization and parcellation of the AC is still lacking. In this study, we employed pseudo‐continuous arterial spin labeling (pCASL) to map tonotopy and voice selective regions using, for the first time, cerebral blood flow (CBF). We demonstrated the feasibility of CBF‐based tonotopy and found a good agreement with BOLD signal‐based tonotopy, despite the lower contrast‐to‐noise ratio of CBF. Quantitative perfusion mapping of baseline CBF showed a region of high perfusion centered on Heschl's gyrus and corresponding to the main high‐low‐high frequency gradients, co‐located to the presumed primary auditory core and suggesting baseline CBF as a novel marker for AC parcellation. Furthermore, susceptibility weighted imaging was employed to investigate the tissue specificity of CBF and BOLD signal and the possible venous bias of BOLD‐based tonotopy. For BOLD only active voxels, we found a higher percentage of vein contamination than for CBF only active voxels. Taken together, we demonstrated that both baseline and stimulus‐induced CBF is an alternative fMRI approach to the standard BOLD signal to study auditory processing and delineate the functional organization of the auditory cortex. Hum Brain Mapp 38:1140–1154, 2017. © 2016 Wiley Periodicals, Inc.     

The effects of capillary transit time heterogeneity on the BOLD signal

Neurovascular coupling mechanisms give rise to vasodilation and functional hyperemia upon neural activation, thereby altering blood oxygenation. This blood oxygenation level dependent (BOLD) contrast allows studies of activation patterns in the working human brain by functional MRI (fMRI). The BOLD‐weighted fMRI signal shows characteristic transients in relation to functional activation, such as the so‐called initial dip, overshoot, and post‐stimulus undershoot. These transients are modulated by other physiological stimuli and in disease, but the underlying physiological mechanisms remain incompletely understood. Capillary transit time heterogeneity (CTH) has been shown to affect oxygen extraction, and hence blood oxygenation. Here, we examine how recently reported redistributions of capillary blood flow during functional activation would be expected to affect BOLD signal transients. We developed a three‐compartment (hemoglobin, plasma, and tissue) model to predict the BOLD signal, incorporating the effects of dynamic changes in CTH. Our model predicts that the BOLD signal represents the superposition of a positive component resulting from increases in cerebral blood flow (CBF), and a negative component, resulting from elevated tissue metabolism and homogenization of capillary flows (reduced CTH). The model reproduces salient features of BOLD signal dynamics under conditions such as hypercapnia, hyperoxia, and caffeine intake, where both brain physiology and BOLD characteristics are altered. Neuroglial signaling and metabolism could affect CBF and capillary flow patterns differently. Further studies of neurovascular and neuro‐capillary coupling mechanisms may help us relate BOLD signals to the firing of certain neuronal populations based on their respective BOLD “fingerprints.”     

Exploring the post‐stimulus undershoot with spin‐echo fMRI: Implications for models of neurovascular response

As a consequence of neural stimulation the blood oxygenation‐level dependent (BOLD) contrast in gradient‐echo echo‐planar imaging (GE‐EPI) based functional MRI (fMRI) leads to an increased MR signal in activated brain regions. Following this, a BOLD signal undershoot below baseline is generally observed with GE‐EPI. The origin of this undershoot has been the focus of many investigations using fMRI and optical modalities, but the underlying mechanisms remain disputed. Here, we investigate the BOLD undershoot following visual stimulation by using a purely T₂‐weighted fMRI sequence at 1.5 and 3 T. By taking advantage of the field strength dependency of the T₂ BOLD contrast and complete absence of static dephasing effects due to the pure spin echoes, one can draw conclusions about the origin of the BOLD undershoot and test the hypotheses in the literature. We observe a significant undershoot at both field strengths, with constant undershoot‐to‐main response ratio. This provides strong evidence that the undershoot is caused by BOLD changes due to elevated post‐stimulus deoxyhaemoglobin concentration in the small vessels. ‘Delayed vascular compliance’ as suggested by the well‐known Balloon and Windkessel models does not appear capable of explaining the undershoot. Our results also suggest that blood volume changes in arterioles and capillaries, for which there is consistent evidence from optical imaging studies, cannot alone cause the undershoot. This has important implications for models of neurovascular response and provides further support for the decoupling of changes in the rate of oxygen metabolism and blood flow. In addition, we found that an ‘arteriolar balloon’ (delayed arterial compliance) may provide a plausible explanation for the temporal characteristics of the BOLD undershoot. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Functional magnetic resonance imaging of working memory in Huntington's disease: Cross‐sectional data from the IMAGE‐HD study

We used functional magnetic resonance imaging (fMRI) to investigate spatial working memory (WM) in an N–BACK task (0, 1, and 2‐BACK) in premanifest Huntington's disease (pre‐HD, n = 35), early symptomatic Huntington's disease (symp‐HD, n = 23), and control (n = 32) individuals. Overall, both WM conditions (1‐BACK and 2‐BACK) activated a large network of regions throughout the brain, common to all groups. However, voxel‐wise and time‐course analyses revealed significant functional group differences, despite no significant behavioral performance differences. During 1‐BACK, voxel‐wise blood‐oxygen‐level‐dependent (BOLD) signal activity was significantly reduced in a number of regions from the WM network (inferior frontal gyrus, anterior insula, caudate, putamen, and cerebellum) in pre‐HD and symp‐HD groups, compared with controls; however, time‐course analysis of the BOLD response in the dorsolateral prefrontal cortex (DLPFC) showed increased activation in symp‐HD, compared with pre‐HD and controls. The pattern of reduced voxel‐wise BOLD activity in pre‐HD and symp‐HD, relative to controls, became more pervasive during 2‐BACK affecting the same structures as in 1‐BACK, but also incorporated further WM regions (anterior cingulate gyrus, parietal lobe and thalamus). The DLPFC BOLD time‐course for 2‐BACK showed a reversed pattern to that observed in 1‐BACK, with a significantly diminished signal in symp‐HD, relative to pre‐HD and controls. Our findings provide support for functional brain reorganisation in cortical and subcortical regions in both pre‐HD and symp‐HD, which are modulated by task difficulty. Moreover, the lack of a robust striatal BOLD signal in pre‐HD may represent a very early signature of change observed up to 15 years prior to clinical diagnosis. Hum Brain Mapp 35:1847–1864, 2014. © 2013 Wiley Periodicals, Inc.     

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification

Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co‐variations of the blood oxygenation level‐dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS‐fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor‐based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS‐fMRI data. Moreover, a sliding window approach is further used to partition the voxel‐wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS‐fMRI data alone. Hum Brain Mapp 38:5019–5034, 2017. © 2017 Wiley Periodicals, Inc.     

Methods for diagnosis and treatment of stimulus‐correlated motion in generic brain activation studies using fMRI

Movement‐related effects in realigned fMRI timeseries can be corrected by regression on linear functions of estimated positional displacements of an individual subject's head during image acquisition. However, this entails biased (under)estimation of the experimental effect whenever subject motion is not independent of the experimental input function. Methods for diagnosing such stimulus‐correlated motion (SCM) are illustrated by application to fMRI data acquired from 5 schizophrenics and 5 normal controls during periodic performance of a verbal fluency task. The schizophrenic group data were more severely affected by SCM than the control group data. Analysis of covariance (ANCOVA) was used, with a voxelwise measure of SCM as a covariate, to estimate between‐group differences in power of periodic signal change while controlling for variability in SCM across groups. Failure to control for SCM in this way substantially exaggerated the number of voxels, apparently demonstrating a between‐group difference in task response. Hum. Brain Mapping 7:38–48, 1999. © 1999 Wiley‐Liss, Inc.     

Accounting for the role of hematocrit in between‐subject variations of MRI‐derived baseline cerebral hemodynamic parameters and functional BOLD responses

Baseline hematocrit fraction (Hct) is a determinant for baseline cerebral blood flow (CBF) and between‐subject variation of Hct thus causes variation in task‐based BOLD fMRI signal changes. We first verified in healthy volunteers (n = 12) that Hct values can be derived reliably from venous blood T₁ values by comparison with the conventional lab test. Together with CBF measured using phase‐contrast MRI, this noninvasive estimation of Hct, instead of using a population‐averaged Hct value, enabled more individual determination of oxygen delivery (DO₂), oxygen extraction fraction (OEF), and cerebral metabolic rate of oxygen (CMRO₂). The inverse correlation of CBF and Hct explained about 80% of between‐subject variation of CBF in this relatively uniform cohort of subjects, as expected based on the regulation of DO₂ to maintain constant CMRO₂. Furthermore, we compared the relationships of visual task‐evoked BOLD response with Hct and CBF. We showed that Hct and CBF contributed 22%–33% of variance in BOLD signal and removing the positive correlation with Hct and negative correlation with CBF allowed normalization of BOLD signal with 16%–22% lower variability. The results of this study suggest that adjustment for Hct effects is useful for studies of MRI perfusion and BOLD fMRI. Hum Brain Mapp 39:344–353, 2018. © 2017 Wiley Periodicals, Inc.     

Measuring vascular reactivity with breath‐holds after stroke: A method to aid interpretation of group‐level BOLD signal changes in longitudinal fMRI studies

Blood oxygenation level‐dependent (BOLD) contrast functional magnetic resonance imaging (fMRI) is a widely used technique to map brain function, and to monitor its recovery after stroke. Since stroke has a vascular etiology, the neurovascular coupling between cerebral blood flow and neural activity may be altered, resulting in uncertainties when interpreting longitudinal BOLD signal changes. The purpose of this study was to demonstrate the feasibility of using a recently validated breath‐hold task in patients with stroke, both to assess group level changes in cerebrovascular reactivity (CVR) and to determine if alterations in regional CVR over time will adversely affect interpretation of task‐related BOLD signal changes. Three methods of analyzing the breath‐hold data were evaluated. The CVR measures were compared over healthy tissue, infarcted tissue and the peri‐infarct tissue, both sub‐acutely (～2 weeks) and chronically (～4 months). In this cohort, a lack of CVR differences in healthy tissue between the patients and controls indicates that any group level BOLD signal change observed in these regions over time is unlikely to be related to vascular alterations. CVR was reduced in the peri‐infarct tissue but remained unchanged over time. Therefore, although a lack of activation in this region compared with the controls may be confounded by a reduced CVR, longitudinal group‐level BOLD changes may be more confidently attributed to neural activity changes in this cohort. By including this breath‐hold‐based CVR assessment protocol in future studies of stroke recovery, researchers can be more assured that longitudinal changes in BOLD signal reflect true alterations in neural activity. Hum Brain Mapp 36:1755–1771, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc .     

A bold view of the lactating brain: functional magnetic resonance imaging studies of suckling in awake dams

Functional magnetic resonance imaging (fMRI) has been used to investigate the responsiveness of the maternal rat brain to pup-suckling under various experimental paradigms. Our research employing the lactating rat model has explored the cortical sensory processing of pup stimuli and the effect of suckling on the brain's reward system. Suckling was observed to increase blood-oxygen-level-dependent (BOLD) signal intensity in the midbrain, striatum and prefrontal cortex, which are areas that receive prominent dopaminergic inputs. The BOLD activation of the reward system occurs in parallel with the activation of extensive cortical sensory areas. The observed regions include the olfactory cortex, auditory cortex and gustatory cortex, and could correspond to cortical representations of pup odours, vocalisations and taste that are active during lactation. Activation patterns within reward regions are consistent with past research on maternal motivation and we explore the possibility that exposure to drugs of abuse might be disruptive of maternal neural responses to pups, particularly in the prefrontal cortex. Our ongoing fMRI studies support and extend past research on the maternal rat brain and its functional neurocircuitry.     

Respiration phase‐locks to fast stimulus presentations: Implications for the interpretation of posterior midline “deactivations”

The posterior midline region (PMR)—considered a core of the default mode network—is deactivated during successful performance in different cognitive tasks. The extent of PMR‐deactivations is correlated with task‐demands and associated with successful performance in various cognitive domains. In the domain of episodic memory, functional MRI (fMRI) studies found that PMR‐deactivations reliably predict learning (successful encoding). Yet it is unclear what explains this relation. One intriguing possibility is that PMR‐deactivations are partially mediated by respiratory artifacts. There is evidence that the fMRI signal in PMR is particularly prone to respiratory artifacts, because of its large surrounding blood vessels. As respiratory fluctuations have been shown to track changes in attention, it is critical for the general interpretation of fMRI results to clarify the relation between respiratory fluctuations, cognitive performance, and fMRI signal. Here, we investigated this issue by measuring respiration during word encoding, together with a breath‐holding condition during fMRI‐scanning. Stimulus‐locked respiratory analyses showed that respiratory fluctuations predicted successful encoding via a respiratory phase‐locking mechanism. At the same time, the fMRI analyses showed that PMR‐deactivations associated with learning were reduced during breath‐holding and correlated with individual differences in the respiratory phase‐locking effect during normal breathing. A left frontal region—used as a control region—did not show these effects. These findings indicate that respiration is a critical factor in explaining the link between PMR‐deactivation and successful cognitive performance. Further research is necessary to demonstrate whether our findings are restricted to episodic memory encoding, or also extend to other cognitive domains. Hum Brain Mapp 35:4932–4943, 2014. © 2014     

Patient specific hemodynamic response functions associated with interictal discharges recorded via simultaneous intracranial EEG‐fMRI

Simultaneous collection of scalp EEG and fMRI has become an important tool for studying the hemodynamic changes associated with interictal epileptiform discharges (IEDs) in persons with epilepsy, and has become a standard presurgical assessment tool in some centres. We previously demonstrated that performing EEG‐fMRI using intracranial electrodes (iEEG‐fMRI) is of low risk to patients in our research centre, and offers unique insight into BOLD signal changes associated with IEDs recorded from very discrete sources. However, it is unknown whether the BOLD response corresponding to IEDs recorded by iEEG‐fMRI follows the canonical hemodynamic response. We therefore scanned 11 presurgical epilepsy patients using iEEG‐fMRI, and assessed the hemodynamic response associated with individual IEDs using two methods: assessment of BOLD signal changes associated with isolated IEDs at the location of the active intracranial electrode, and by estimating subject‐specific impulse response functions to isolated IEDs. We found that the hemodynamic response associated with the intracranially recorded discharges varied by patient and by spike location. The observed shape and timing differences also deviated from the canonical hemodynamic response function traditionally used in many fMRI experiments. It is recommended that future iEEG‐fMRI studies of IEDs use a flexible hemodynamic response model when performing parametric tests to accurately characterize these data. Hum Brain Mapp 36:5252–5264, 2015. © 2015 Wiley Periodicals, Inc.     

Defining language networks from resting‐state fMRI for surgical planning—a feasibility study

Presurgical language mapping for patients with lesions close to language areas is critical to neurosurgical decision‐making for preservation of language function. As a clinical noninvasive imaging technique, functional MRI (fMRI) is used to identify language areas by measuring blood‐oxygen‐level dependent (BOLD) signal change while patients perform carefully timed language vs. control tasks. This task‐based fMRI critically depends on task performance, excluding many patients who have difficulty performing language tasks due to neurologic deficits. On the basis of recent discovery of resting‐state fMRI (rs‐fMRI), we propose a “task‐free” paradigm acquiring fMRI data when patients simply are at rest. This paradigm is less demanding for patients to perform and easier for technologists to administer. We investigated the feasibility of this approach in right‐handed healthy control subjects. First, group independent component analysis (ICA) was applied on the training group (14 subjects) to identify group level language components based on expert rating results. Then, four empirically and structurally defined language network templates were assessed for their ability to identify language components from individuals' ICA output of the testing group (18 subjects) based on spatial similarity analysis. Results suggest that it is feasible to extract language activations from rs‐fMRI at the individual subject level, and two empirically defined templates (that focuses on frontal language areas and that incorporates both frontal and temporal language areas) demonstrated the best performance. We propose a semi‐automated language component identification procedure and discuss the practical concerns and suggestions for this approach to be used in clinical fMRI language mapping. Hum Brain Mapp 35:1018–1030, 2014. © 2013 Wiley Periodicals, Inc.     

A reward prediction error for charitable donations reveals outcome orientation of donators

The motives underlying prosocial behavior, like charitable donations, can be related either to actions or to outcomes. To address the neural basis of outcome orientation in charitable giving, we asked 33 subjects to make choices affecting their own payoffs and payoffs to a charity organization, while being scanned by functional magnetic resonance imaging (fMRI). We experimentally induced a reward prediction error (RPE) by subsequently discarding some of the chosen outcomes. Co-localized to a nucleus accumbens BOLD signal corresponding to the RPE for the subject''s own payoff, we observed an equivalent RPE signal for the charity''s payoff in those subjects who were willing to donate. This unique demonstration of a neuronal RPE signal for outcomes exclusively affecting unrelated others indicates common brain processes during outcome evaluation for selfish, individual and nonselfish, social rewards and strongly suggests the effectiveness of outcome-oriented motives in charitable giving.