BOLD Imaging in Awake Wild-Type and Mu-Opioid Receptor Knock-Out Mice Reveals On-Target Activation Maps in Response to Oxycodone

Blood oxygen level dependent (BOLD) imaging in awake mice was used to identify differences in brain activity between wild-type, and Mu (μ) opioid receptor knock-outs (MuKO) in response to oxycodone (OXY). Using a segmented, annotated MRI mouse atlas and computational analysis, patterns of integrated positive and negative BOLD activity were identified across 122 brain areas. The pattern of positive BOLD showed enhanced activation across the brain in WT mice within 15 min of intraperitoneal administration of 2.5 mg of OXY. BOLD activation was detected in 72 regions out of 122, and was most prominent in areas of high μ opioid receptor density (thalamus, ventral tegmental area, substantia nigra, caudate putamen, basal amygdala, and hypothalamus), and focus on pain circuits indicated strong activation in major pain processing centers (central amygdala, solitary tract, parabrachial area, insular cortex, gigantocellularis area, ventral thalamus primary sensory cortex, and prelimbic cortex). Importantly, the OXY-induced positive BOLD was eliminated in MuKO mice in most regions, with few exceptions (some cerebellar nuclei, CA3 of the hippocampus, medial amygdala, and preoptic areas). This result indicates that most effects of OXY on positive BOLD are mediated by the μ opioid receptor (on-target effects). OXY also caused an increase in negative BOLD in WT mice in few regions (16 out of 122) and, unlike the positive BOLD response the negative BOLD was only partially eliminated in the MuKO mice (cerebellum), and in some case intensified (hippocampus). Negative BOLD analysis therefore shows activation and deactivation events in the absence of the μ receptor for some areas where receptor expression is normally extremely low or absent (off-target effects). Together, our approach permits establishing opioid-induced BOLD activation maps in awake mice. In addition, comparison of WT and MuKO mutant mice reveals both on-target and off-target activation events, and set an OXY brain signature that should, in the future, be compared to other μ opioid agonists.

Reward magnitude tracking by neural populations in ventral striatum

Evaluation of the magnitudes of intrinsically rewarding stimuli is essential for assigning value and guiding behavior. By combining parametric manipulation of a primary reward, medial forebrain bundle (MFB) microstimulation, with functional magnetic imaging (fMRI) in rodents, we delineated a broad network of structures activated by behaviorally characterized levels of rewarding stimulation. Correlation of psychometric behavioral measurements with fMRI response magnitudes revealed regions whose activity corresponded closely to the subjective magnitude of rewards. The largest and most reliable focus of reward magnitude tracking was observed in the shell region of the nucleus accumbens (NAc). Although the nonlinear nature of neurovascular coupling complicates interpretation of fMRI findings in precise neurophysiological terms, reward magnitude tracking was not observed in vascular compartments and could not be explained by saturation of region-specific hemodynamic responses. In addition, local pharmacological inactivation of NAc changed the profile of animals' responses to rewards of different magnitudes without altering mean reward response rates, further supporting a hypothesis that neural population activity in this region contributes to assessment of reward magnitudes.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, “dynamic global signal regression” (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional “static” global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.

Age-Related Changes in BOLD Activation Pattern in Phonemic Fluency Paradigm: An Investigation of Activation, Functional Connectivity and Psychophysiological Interactions

Healthy aging is associated with decline of cognitive functions. However, even before those declines become noticeable, the neural architecture underlying those mechanisms has undergone considerable restructuring and reorganization. During performance of a cognitive task, not only have the task-relevant networks demonstrated reorganization with aging, which occurs primarily by recruitment of additional areas to preserve performance, but the task-irrelevant network of the “default-mode” network (DMN), which is normally deactivated during task performance, has also consistently shown reduction of this deactivation with aging. Here, we revisited those age-related changes in task-relevant (i.e., language system) and task-irrelevant (i.e., DMN) systems with a language production paradigm in terms of task-induced activation/deactivation, functional connectivity, and context-dependent correlations between the two systems. Our task fMRI data demonstrated a late increase in cortical recruitment in terms of extent of activation, only observable in our older healthy adult group, when compared to the younger healthy adult group, with recruitment of the contralateral hemisphere, but also other regions from the network previously underutilized. Our middle-aged individuals, when compared to the younger healthy adult group, presented lower levels of activation intensity and connectivity strength, with no recruitment of additional regions, possibly reflecting an initial, uncompensated, network decline. In contrast, the DMN presented a gradual decrease in deactivation intensity and deactivation extent (i.e., low in the middle-aged, and lower in the old) and similar gradual reduction of functional connectivity within the network, with no compensation. The patterns of age-related changes in the task-relevant system and DMN are incongruent with the previously suggested notion of anti-correlation of the two systems. The context-dependent correlation by psycho-physiological interaction (PPI) analysis demonstrated an independence of these two systems, with the onset of task not influencing the correlation between the two systems. Our results suggest that the language network and the DMN may be non-dependent systems, potentially correlated through the re-allocation of cortical resources, and that aging may affect those two systems differently.

Building a Science of Individual Differences from fMRI

To date, fMRI research has been concerned primarily with evincing generic principles of brain function through averaging data from multiple subjects. Given rapid developments in both hardware and analysis tools, the field is now poised to study fMRI-derived measures in individual subjects, and to relate these to psychological traits or genetic variations. We discuss issues of validity, reliability and statistical assessment that arise when the focus shifts to individual subjects and that are applicable also to other imaging modalities. We emphasize that individual assessment of neural function with fMRI presents specific challenges and necessitates careful consideration of anatomical and vascular between-subject variability as well as sources of within-subject variability.

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation.

Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data

BACKGROUND

Causal estimation methods are increasingly being used to investigate functional brain networks in fMRI, but there are continuing concerns about the validity of these methods.

NEW METHOD

Multivariate dynamical systems (MDS) is a state-space method for estimating dynamic causal interactions in fMRI data. Here we validate MDS using benchmark simulations as well as simulations from a more realistic stochastic neurophysiological model. Finally, we applied MDS to investigate dynamic casual interactions in a fronto-cingulate-parietal control network using human connectome project (HCP) data acquired during performance of a working memory task. Crucially, since the ground truth in experimental data is unknown, we conducted novel stability analysis to determine robust causal interactions within this network.

RESULTS

MDS accurately recovered dynamic causal interactions with an area under receiver operating characteristic (AUC) above 0.7 for benchmark datasets and AUC above 0.9 for datasets generated using the neurophysiological model. In experimental fMRI data, bootstrap procedures revealed a stable pattern of causal influences from the anterior insula to other nodes of the fronto-cingulate-parietal network.

COMPARISON WITH EXISTING METHODS

MDS is effective in estimating dynamic causal interactions in both the benchmark and neurophysiological model based datasets in terms of AUC, sensitivity and false positive rates.

CONCLUSIONS

Our findings demonstrate that MDS can accurately estimate causal interactions in fMRI data. Neurophysiological models and stability analysis provide a general framework for validating computational methods designed to estimate causal interactions in fMRI. The right anterior insula functions as a causal hub during working memory.

Layer-Specific fMRI Responses to Excitatory and Inhibitory Neuronal Activities in the Olfactory Bulb

High-resolution functional magnetic resonance imaging (fMRI) detects localized neuronal activity via the hemodynamic response, but it is unclear whether it accurately identifies neuronal activity specific to individual layers. To address this issue, we preferentially evoked neuronal activity in superficial, middle, and deep layers of the rat olfactory bulb: the glomerular layer by odor (5% amyl acetate), the external plexiform layer by electrical stimulation of the lateral olfactory tract (LOT), and the granule cell layer by electrical stimulation of the anterior commissure (AC), respectively. Electrophysiology, laser-Doppler flowmetry of cerebral blood flow (CBF), and blood oxygenation level-dependent (BOLD) and cerebral blood volume-weighted (CBV) fMRI at 9.4 T were performed independently. We found that excitation of inhibitory granule cells by stimulating LOT and AC decreased the spontaneous multi-unit activities of excitatory mitral cells and subsequently increased CBF, CBV, and BOLD signals. Odor stimulation also increased the hemodynamic responses. Furthermore, the greatest CBV fMRI responses were discretely separated into the same layers as the evoked neuronal activities for all three stimuli, whereas BOLD was poorly localized with some exception to the poststimulus undershoot. In addition, the temporal dynamics of the fMRI responses varied depending on the stimulation pathway, even within the same layer. These results indicate that the vasculature is regulated within individual layers and CBV fMRI has a higher fidelity to the evoked neuronal activity compared with BOLD. Our findings are significant for understanding the neuronal origin and spatial specificity of hemodynamic responses, especially for the interpretation of laminar-resolution fMRI.

SIGNIFICANCE STATEMENT

Functional magnetic resonance imaging (fMRI) is a noninvasive, in vivo technique widely used to map function of the entire brain, including deep structures, in animals and humans. However, it measures neuronal activity indirectly by way of the vascular response. It is currently unclear how finely the hemodynamic response is regulated within single cortical layers and whether increased inhibitory neuronal activities affect fMRI signal changes. Both laminar specificity and the neural origins of fMRI are important to interpret functional maps properly, which we investigated by activating discrete rat olfactory bulb circuits.

True and apparent optogenetic BOLD fMRI signals

PURPOSE

Optogenetic fMRI (ofMRI) is a novel tool in neurophysiology and neuroimaging. The method is prone to light-induced artifacts, two of which were investigated in this study.

METHODS

ofMRI was performed in rats using two excitatory opsins (ChR2 and C1V1_TT ) virally transduced in somatosensory cortex or thalamus. Heat-induced apparent BOLD activation at the site of the optical fiber and stimulation light-induced activation of the visual pathways were investigated, and control experiments for these two artifacts were established.

RESULTS

Specific optogenetic BOLD activation was observed with both opsins, accompanied by BOLD in the visual pathways. Unspecific heat-induced BOLD was ruled out by a control experiment employing low-level constant illumination in addition to pulsed optogenetic stimulation. Activation of the visual pathways was confirmed to be physiological by direct visual stimulation of the eyes and was suppressed by additional low-level constant light to the eyes. Light inside the brain was identified as one source of the BOLD signal observed in the visual pathways.

CONCLUSION

ofMRI is a method of tremendous potential, but unspecific activations in fMRI not caused by the activation of opsins must be avoided or recognized as such. The control experiments presented here allow for validating the specificity of optogenetic stimulation. Magn Reson Med 77:126-136, 2017. © 2016 Wiley Periodicals, Inc.

Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: A pilot study

The blood oxygenation level-dependent (BOLD) signal is widely used for functional magnetic resonance imaging (fMRI) of brain function in health and disease. The statistical power of fMRI group studies is significantly hampered by high inter-subject variance due to differences in baseline vascular physiology. Several methods have been proposed to account for physiological vascularization differences between subjects and hence improve the sensitivity in group studies. However, these methods require the acquisition of additional reference scans (such as a full resting-state fMRI session or ASL-based calibrated BOLD). We present a vascular autorescaling (VasA) method, which does not require any additional reference scans. VasA is based on the observation that slow oscillations (<0.1Hz) in arterial blood CO2 levels occur naturally due to changes in respiration patterns. These oscillations yield fMRI signal changes whose amplitudes reflect the blood oxygenation levels and underlying local vascularization and vascular responsivity. VasA estimates proxies of the amplitude of these CO2-driven oscillations directly from the residuals of task-related fMRI data without the need for reference scans. The estimates are used to scale the amplitude of task-related fMRI responses, to account for vascular differences. The VasA maps compared well to cerebrovascular reactivity (CVR) maps and cerebral blood volume maps based on vascular space occupancy (VASO) measurements in four volunteers, speaking to the physiological vascular basis of VasA. VasA was validated in a wide variety of tasks in 138 volunteers. VasA increased t-scores by up to 30% in specific brain areas such as the visual cortex. The number of activated voxels was increased by up to 200% in brain areas such as the orbital frontal cortex while still controlling the nominal false-positive rate. VasA fMRI outperformed previously proposed rescaling approaches based on resting-state fMRI data and can be readily applied to any task-related fMRI data set, even retrospectively.

Using a novel source-localized phase regressor technique for evaluation of the vascular contribution to semantic category area localization in BOLD fMRI

Numerous studies have shown that gradient-echo blood oxygen level dependent (BOLD) fMRI is biased toward large draining veins. However, the impact of this large vein bias on the localization and characterization of semantic category areas has not been examined. Here we address this issue by comparing standard magnitude measures of BOLD activity in the Fusiform Face Area (FFA) and Parahippocampal Place Area (PPA) to those obtained using a novel method that suppresses the contribution of large draining veins: source-localized phase regressor (sPR). Unlike previous suppression methods that utilize the phase component of the BOLD signal, sPR yields robust and unbiased suppression of large draining veins even in voxels with no task-related phase changes. This is confirmed in ideal simulated data as well as in FFA/PPA localization data from four subjects. It was found that approximately 38% of right PPA, 14% of left PPA, 16% of right FFA, and 6% of left FFA voxels predominantly reflect signal from large draining veins. Surprisingly, with the contributions from large veins suppressed, semantic category representation in PPA actually tends to be lateralized to the left rather than the right hemisphere. Furthermore, semantic category areas larger in volume and higher in fSNR were found to have more contributions from large veins. These results suggest that previous studies using gradient-echo BOLD fMRI were biased toward semantic category areas that receive relatively greater contributions from large veins.

Successful antidepressant chronotherapeutics enhance fronto-limbic neural responses and connectivity in bipolar depression

The identification of antidepressant response predictors in bipolar disorder (BD) may provide new potential enhancements in treatment selection. Repeated total sleep deprivation combined with light therapy (TSD+LT) can acutely reverse depressive symptoms and has been proposed as a model antidepressant treatment. This study aims at investigating the effect of TSD+LT on effective connectivity and neural response in cortico-limbic circuitries during implicit processing of fearful and angry faces in patients with BD. fMRI and Dynamic Causal Modeling (DCM) were combined to study the effect of chronotherapeutics on neural responses in healthy controls (HC, n = 35) and BD patients either responder (RBD, n = 26) or non responder (nRBD, n = 11) to 3 consecutive TSD+LT sessions. Twenty-four DCMs exploring connectivity between anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), Amygdala (Amy), fusiform gyrus and visual cortex were constructed. After treatment, patients significantly increased their neural responses in DLPFC, ACC and insula. nRBD showed lower baseline and endpoint neural responses than RBD. The increased activity in ACC and in medial prefrontal cortex, associated with antidepressant treatment, was positively associated with the improvement of depressive symptomatology. Only RBD patients increased intrinsic connectivity from DLPFC to ACC and reduced the modulatory effect of the task on Amy-DLPFC connection. A successful antidepressant treatment was associated with an increased functional activity and connectivity within cortico-limbic networks, suggesting the possible role of these measures in providing possible biomarkers for treatment efficacy.

On the distinguishability of HRF models in fMRI

Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.

Can apparent resting state connectivity arise from systemic fluctuations?

It is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.

On tests of activation map dimensionality for fMRI-based studies of learning

A methodology for investigating learning is developed using activation distributions, as opposed to standard voxel-level interaction tests. The approach uses tests of dimensionality to consider the ensemble of paired changes in voxel activation. The developed method allows for the investigation of non-focal and non-localized changes due to learning. In exchange for increased power to detect learning-based changes, this procedure sacrifices the localization information gained via voxel-level interaction testing. The test is demonstrated on an arc-pointing motor task for the study of motor learning, which served as the motivation for this methodological development. The proposed framework considers activation distribution, while the specific proposed test investigates linear tests of dimensionality. This paper includes: the development of the framework, a large scale simulation study, and the subsequent application to a study of motor learning in healthy adults. While the performance of the method was excellent when model assumptions held, complications arose in instances of massive numbers of null voxels or varying angles of principal dimension across subjects. Further analysis found that careful masking addressed the former concern, while an angle correction successfully resolved the latter. The simulation results demonstrated that the study of linear dimensionality is able to capture learning effects. The motivating data set used to illustrate the method evaluates two similar arc-pointing tasks, each over two sessions, with training on only one of the tasks in between sessions. The results suggests different activation distribution dimensionality when considering the trained and untrained tasks separately. Specifically, the untrained task evidences greater activation distribution dimensionality than the trained task. However, the direct comparison between the two tasks did not yield a significant result. The nature of the indication for greater dimensionality in the untrained task is explored and found to be non-linear variation in the data.

BOLD fMRI study of ultrahigh frequency encoding in the inferior colliculus

Many vertebrates communicate with ultrahigh frequency (UHF) vocalizations to limit auditory detection by predators. The mechanisms underlying the neural encoding of such UHF sounds may provide important insights for understanding neural processing of other complex sounds (e.g. human speeches). In the auditory system, sound frequency is normally encoded topographically as tonotopy, which, however, contains very limited representation of UHFs in many species. Instead, electrophysiological studies suggested that two neural mechanisms, both exploiting the interactions between frequencies, may contribute to UHF processing. Neurons can exhibit excitatory or inhibitory responses to a tone when another UHF tone is presented simultaneously (combination sensitivity). They can also respond to such stimulation if they are tuned to the frequency of the cochlear-generated distortion products of the two tones, e.g. their difference frequency (cochlear distortion). Both mechanisms are present in an early station of the auditory pathway, the midbrain inferior colliculus (IC). Currently, it is unclear how prevalent the two mechanisms are and how they are functionally integrated in encoding UHFs. This study investigated these issues with large-view BOLD fMRI in rat auditory system, particularly the IC. UHF vocalizations (above 40kHz), but not pure tones at similar frequencies (45, 55, 65, 75kHz), evoked robust BOLD responses in multiple auditory nuclei, including the IC, reinforcing the sensitivity of the auditory system to UHFs despite limited representation in tonotopy. Furthermore, BOLD responses were detected in the IC when a pair of UHF pure tones was presented simultaneously (45 & 55kHz, 55 & 65kHz, 45 & 65kHz, 45 & 75kHz). For all four pairs, a cluster of voxels in the ventromedial side always showed the strongest responses, displaying combination sensitivity. Meanwhile, voxels in the dorsolateral side that showed strongest secondary responses to each pair of UHF pure tones also showed the strongest responses to a pure tone at their difference frequency, suggesting that they are sensitive to cochlear distortion. These BOLD fMRI results indicated that combination sensitivity and cochlear distortion are employed by large but spatially distinctive neuron populations in the IC to represent UHFs. Our imaging findings provided insights for understanding sound feature encoding in the early stage of the auditory pathway.

Abnormal cortico-limbic connectivity during emotional processing correlates with symptom severity in schizophrenia

BACKGROUND

Impaired emotional processing is a core feature of schizophrenia (SZ). Consistent findings suggested that abnormal emotional processing in SZ could be paralleled by a disrupted functional and structural integrity within the fronto-limbic circuitry. The effective connectivity of emotional circuitry in SZ has never been explored in terms of causal relationship between brain regions. We used functional magnetic resonance imaging and Dynamic Causal Modeling (DCM) to characterize effective connectivity during implicit processing of affective stimuli in SZ.

METHODS

We performed DCM to model connectivity between amygdala (Amy), dorsolateral prefrontal cortex (DLPFC), ventral prefrontal cortex (VPFC), fusiform gyrus (FG) and visual cortex (VC) in 25 patients with SZ and 29 HC. Bayesian Model Selection and average were performed to determine the optimal structural model and its parameters.

RESULTS

Analyses revealed that patients with SZ are characterized by a significant reduced top-down endogenous connectivity from DLPFC to Amy, an increased connectivity from Amy to VPFC and a decreased driving input to Amy of affective stimuli compared to HC. Furthermore, DLPFC to Amy connection in patients significantly influenced the severity of psychopathology as rated on Positive and Negative Syndrome Scale.

CONCLUSIONS

Results suggest a functional disconnection in brain network that contributes to the symptomatic outcome of the disorder. Our findings support the study of effective connectivity within cortico-limbic structures as a marker of severity and treatment efficacy in SZ.

Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI

BACKGROUND

Functional magnetic resonance imaging (fMRI) time series are subject to corruption by many noise sources, especially physiological noise and motion. Researchers have developed many methods to reduce physiological noise, including RETROICOR, which retroactively removes cardiac and respiratory waveforms collected during the scan, and CompCor, which applies principal components analysis (PCA) to remove physiological noise components without any physiological monitoring during the scan.

NEW METHOD

We developed four variants of the CompCor method. The optimized CompCor method applies PCA to time series in a noise mask, but orthogonalizes each component to the BOLD response waveform and uses an algorithm to determine a favorable number of components to use as "nuisance regressors." Whole brain component correction (WCompCor) is similar, except that it applies PCA to time-series throughout the whole brain. Low-pass component correction (LCompCor) identifies low-pass filtered components throughout the brain, while high-pass component correction (HCompCor) identifies high-pass filtered components.

COMPARISON WITH EXISTING METHOD

We compared the new methods with the original CompCor method by examining the resulting functional contrast-to-noise ratio (CNR), sensitivity, and specificity.

RESULTS

(1) The optimized CompCor method increased the CNR and sensitivity compared to the original CompCor method and (2) the application of WCompCor yielded the best improvement in the CNR and sensitivity.

CONCLUSIONS

The sensitivity of the optimized CompCor, WCompCor, and LCompCor methods exceeded that of the original CompCor method. However, regressing noise signals showed a paradoxical consequence of reducing specificity for all noise reduction methods attempted.

Simultaneous multislice spectral-spatial excitations for reduced signal loss susceptibility artifact in BOLD functional MRI

PURPOSE

Simultaneous multislice (SMS) imaging can significantly increase image acquisition rates and improve temporal resolution and contrast in gradient-echo blood oxygen level-dependent (BOLD) functional MRI (fMRI) experiments. Through-plane signal loss due to B(0) inhomogeneities at air-tissue interfaces limits fMRI of structures near the nasal cavity and ear canals. This study implemented spectral-spatial (SPSP) radiofrequency pulses for reduced through-plane signal loss across multiple simultaneously excited slices.

THEORY AND METHODS

Multiband (MB) and power independent of number of slices (PINS) methods are combined with SPSP excitation for signal loss compensation in slice-accelerated human brain imaging. Nine simultaneous slices of 5-mm thickness and 20 mm apart were excited using standard MB radiofrequency pulses and the proposed SPSP-SMS pulses, yielding coverage of 36 slices in four shots with 350-ms volume pulse repetition time. The pulses were compared in breath-hold fMRI at 3T.

RESULTS

The SPSP-SMS pulses recovered ∼45% of voxels with signal loss in standard SMS images. Activation in areas of signal recovery increased by 26.4% using a 12.6-ms SPSP-MB pulse and 20.3% using a 12.1-ms SPSP-PINS pulse.

CONCLUSIONS

It is demonstrated that SPSP-SMS pulses can improve BOLD sensitivity in areas of signal loss across simultaneous multiple slices.

Blood oxygen level dependent functional magnetic resonance imaging for presurgical planning

Functional magnetic resonance imaging (fMRI) has become a common tool for presurgical sensorimotor mapping, and is a significant preoperative asset for tumors located adjacent to the central sulcus. fMRI has changed surgical options for many patients. This noninvasive tool allows for easy display and integration with other neuroimaging techniques. Although fMRI is a useful preoperative tool, it is not perfect. Tumors that affect the normal vascular coupling of neuronal activity will affect fMRI measurements. This article discusses the usefulness of blood oxygen level dependent (BOLD) fMRI with regard to preoperative motor mapping.

Reduced CMRO₂ and cerebrovascular reserve in patients with severe intracranial arterial stenosis: a combined multiparametric qBOLD oxygenation and BOLD fMRI study

Multiparametric quantitative blood oxygenation level dependent (mqBOLD) magnetic resonance Imaging (MRI) approach allows mapping tissular oxygen saturation (StO2 ) and cerebral metabolic rate of oxygen (CMRO2 ). To identify hemodynamic alteration related to severe intracranial arterial stenosis (SIAS), functional MRI of cerebrovascular reserve (CVR BOLD fMRI) to hypercapnia has been proposed. Diffusion imaging suggests chronic low grade ischemia in patients with impaired CVR. The aim of the present study was to evaluate how oxygen parameters (StO2 and CMRO2 ), assessed with mqBOLD approach, correlate with CVR in patients (n = 12) with SIAS and without arterial occlusion. The perfusion (dynamic susceptibility contrast), oxygenation, and CVR were compared. The MRI protocol conducted at 3T lasted approximately 1 h. Regions of interest measures on maps were delineated on segmented gray matter (GM) of middle cerebral artery territories. We have shown that decreased CVR is spatially associated with decreased CMRO2 in GM of patients with SIAS. Further, the degree of ipsilateral CVR reduction was well-correlated with the amplitude of the CMRO2 deficit. The altered CMRO2 suggests the presence of a moderate ischemia explained by both a decrease in perfusion and in CVR. CVR and mqBOLD method may be helpful in the selection of patients with SIAS to advocate for medical therapy or percutaneous transluminal angioplasty-stenting.

Anterior and posterior MTL networks in aging and MCI

Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anterior and posterior medial temporal lobe (MTL) networks have recently been described in young adults using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-based functional connectivity (fc-BOLD). They have been hypothesized to subserve distinct mnemonic and non-memory cognitive functions and are thought to be associated with differential vulnerability in neurological disorders. In this article, we demonstrate the existence of these functional networks in an older adult population and in a cohort of patients diagnosed with amnestic mild cognitive impairment (aMCI). Anatomic subregions of interest in the MTL were defined using high-resolution T2-weighted MRI and used as seeds for defining the putative networks using fc-BOLD. Although the literature has suggested that the posterior MTL network is particularly vulnerable to early Alzheimer's disease, we show that both the networks are affected in MCI, to varying degrees, compared with the control group. Furthermore, cortical thickness in the brain regions defined by these networks was reduced in MCI.

Comparing within-subject classification and regularization methods in fMRI for large and small sample sizes

In recent years, a variety of multivariate classifier models have been applied to fMRI, with different modeling assumptions. When classifying high-dimensional fMRI data, we must also regularize to improve model stability, and the interactions between classifier and regularization techniques are still being investigated. Classifiers are usually compared on large, multisubject fMRI datasets. However, it is unclear how classifier/regularizer models perform for within-subject analyses, as a function of signal strength and sample size. We compare four standard classifiers: Linear and Quadratic Discriminants, Logistic Regression and Support Vector Machines. Classification was performed on data in the linear kernel (covariance) feature space, and classifiers are tuned with four commonly-used regularizers: Principal Component and Independent Component Analysis, and penalization of kernel features using L₁ and L₂ norms. We evaluated prediction accuracy (P) and spatial reproducibility (R) of all classifier/regularizer combinations on single-subject analyses, over a range of three different block task contrasts and sample sizes for a BOLD fMRI experiment. We show that the classifier model has a small impact on signal detection, compared to the choice of regularizer. PCA maximizes reproducibility and global SNR, whereas Lp -norms tend to maximize prediction. ICA produces low reproducibility, and prediction accuracy is classifier-dependent. However, trade-offs in (P,R) depend partly on the optimization criterion, and PCA-based models are able to explore the widest range of (P,R) values. These trends are consistent across task contrasts and data sizes (training samples range from 6 to 96 scans). In addition, the trends in classifier performance are consistent for ROI-based classifier analyses.

A single session of exercise increases connectivity in sensorimotor-related brain networks: a resting-state fMRI study in young healthy adults

Habitual long term physical activity is known to have beneficial cognitive, structural, and neuro-protective brain effects, but to date there is limited knowledge on whether a single session of exercise can alter the brain's functional connectivity, as assessed by resting-state functional magnetic resonance imaging (rs-fMRI). The primary objective of this study was to characterize potential session effects in resting-state networks (RSNs). We examined the acute effects of exercise on the functional connectivity of young healthy adults (N = 15) by collecting rs-fMRI before and after 20 min of moderate intensity aerobic exercise and compared this with a no-exercise control group (N = 15). Data were analyzed using independent component analysis, denoising and dual regression procedures. Regions of interest-based group session effect statistics were calculated in RSNs of interest using voxel-wise permutation testing and Cohen's D effect size. Group analysis in the exercising group data set revealed a session effect in sub-regions of three sensorimotor related areas: the pre and/or postcentral gyri, secondary somatosensory area and thalamus, characterized by increased co-activation after exercise (corrected p < 0.05). Cohen's D analysis also showed a significant effect of session in these three RSNs (p< 0.05), corroborating the voxel-wise findings. Analyses of the no-exercise dataset produced no significant results, thereby providing support for the exercise findings and establishing the inherent test-retest reliability of the analysis pipeline on the RSNs of interest. This study establishes the feasibility of rs-fMRI to localize brain regions that are associated with acute exercise, as well as an analysis consideration to improve sensitivity to a session effect.

Studies on the Q175 Knock-in Model of Huntington's Disease Using Functional Imaging in Awake Mice: Evidence of Olfactory Dysfunction

Blood oxygen level dependent (BOLD) imaging in awake mice was used to identify differences in brain activity between wild-type, HETzQ175, and HOMzQ175 genotypes in response to the odor of almond. The study was designed to see how alterations in the huntingtin gene in a mouse model of Huntington’s disease would affect the perception and processing of almond odor, an evolutionarily conserved stimulus with high emotional and motivational valence. Moreover, the mice in this study were “odor naïve,” i.e., never having smelled almond or any nuts. Using a segmented, annotated MRI atlas of the mouse and computational analysis, 17 out of 116 brain regions were identified as responding differently to almond odor across genotypes. These regions included the glomerulus of the olfactory bulb, forebrain cortex, anterior cingulate, subiculum, and dentate gyrus of the hippocampus, and several areas of the hypothalamus. In many cases, these regions showed a gene-dose effect with HETzQ175 mice showing a reduction in brain activity from wild-type that is further reduced in HOMzQ175 mice. Conspicuously absent were any differences in brain activity in the caudate/putamen, thalamus, CA3, and CA1 of the hippocampus and much of the cortex. The glomerulus of the olfactory bulb in HOMzQ175 mice showed a reduced change in BOLD signal intensity in response to almond odor as compared to the other phenotypes suggesting a deficit in olfactory sensitivity.

Tracking cerebral blood flow in BOLD fMRI using recursively generated regressors

BOLD functional MRI (fMRI) data are dominated by low frequency signals, many of them of unclear origin. We have recently shown that some portions of the low frequency oscillations found in BOLD fMRI are systemic signals closely related to the blood circulation (Tong et al. [2013]: NeuroImage 76:202-215). They are commonly treated as physiological noise in fMRI studies. In this study, we propose and test a novel data-driven analytical method that uses these systemic low frequency oscillations in the BOLD signal as a tracer to follow cerebral blood flow dynamically. Our findings demonstrate that: (1) systemic oscillations pervade the BOLD signal; (2) the temporal traces evolve as the blood propagates though the brain; and, (3) they can be effectively extracted via a recursive procedure and used to derive the cerebral circulation map. Moreover, this method is independent from functional analyses, and thus allows simultaneous and independent assessment of information about cerebral blood flow to be conducted in parallel with the functional studies. In this study, the method was applied to data from the resting state scans, acquired using a multiband EPI sequence (fMRI scan with much shorter TRs), of seven healthy participants. Dynamic maps with consistent features resembling cerebral blood circulation were derived, confirming the robustness and repeatability of the method.

Magnetic resonance imaging of the retina: from mice to men

This mini-review provides an overview of magnetic resonance imaging (MRI) applications to study rodent, cat, non-human primate, and human retinas. These techniques include T(1) - and T(2) -weighted anatomical, diffusion, blood flow, blood volume, blood-oxygenation level dependent, manganese-enhanced, physiological, and functional MRI. Applications to study the retinas in diabetic retinopathy, glaucoma, and retinal degeneration are also reviewed. MRI offers some unique advantages compared with existing imaging techniques and has the potential to further our understanding of physiology and function in healthy and diseased retinas.

Studying the Spatial Distribution of Physiological Effects on BOLD Signals Using Ultrafast fMRI

The blood-oxygen-level dependent (BOLD) signal in functional MRI (fMRI) reflects both neuronal activations and global physiological fluctuations. These physiological fluctuations can be attributed to physiological low frequency oscillations (pLFOs), respiration, and cardiac pulsation. With typical TR values, i.e., 2 s or longer, the high frequency physiological signals (i.e., from respiration and cardiac pulsation) are aliased into the low frequency band, making it hard to study the individual effect of these physiological processes on BOLD. Recently developed multiband EPI sequences, which offer full brain coverage with extremely short TR values (400 ms or less) allow these physiological signals to be spectrally separated. In this study, we applied multiband resting state scans on nine healthy participants with TR = 0.4 s. The spatial distribution of each physiological process on BOLD fMRI was explored using their spectral features and independent component analysis (ICA). We found that the spatial distributions of different physiological processes are distinct. First, cardiac pulsation affects mostly the base of the brain, where high density of arteries exists. Second, respiration affects prefrontal and occipital areas, suggesting the motion associated with breathing might contribute to the noise. Finally, and most importantly, we found that the effects of pLFOs dominated many prominent ICA components, which suggests that, contrary to the popular belief that aliased cardiac and respiration signals are the main physiological noise source in BOLD fMRI, pLFOs may be the most influential physiological signals. Understanding and measuring these pLFOs are important for denoising and accurately modeling BOLD signals.

Oxytocin and vasopressin modulation of the neural correlates of motivation and emotion: results from functional MRI studies in awake rats

Oxytocin and vasopressin modulate a range of species typical behavioral functions that include social recognition, maternal-infant attachment, and modulation of memory, offensive aggression, defensive fear reactions, and reward seeking. We have employed novel functional magnetic resonance mapping techniques in awake rats to explore the roles of these neuropeptides in the maternal and non-maternal brain. Results from the functional neuroimaging studies that are summarized here have directly and indirectly confirmed and supported previous findings. Oxytocin is released within the lactating rat brain during suckling stimulation and activates specific subcortical networks in the maternal brain. Both vasopressin and oxytocin modulate brain regions involved unconditioned fear, processing of social stimuli and the expression of agonistic behaviors. Across studies there are relatively consistent brain networks associated with internal motivational drives and emotional states that are modulated by oxytocin and vasopressin. This article is part of a Special Issue entitled Oxytocin and Social Behav.

Multimodal study of the hemodynamic response to hypercapnia in anesthetized aged rats

With aging, the brain undergoes changes in metabolism and perfusion, both of which influence the widely used blood-oxygenation-level-dependent (BOLD) MRI signal. To isolate the vascular effects associated with age, this study measured the response to a hypercapnic challenge using different imaging modalities in 19 young (3 months-old) and 13 old (24 months-old) Long-Evans rats. Intrinsic optical imaging was used to measure oxy (HbO), deoxy (HbR) and total (HbT) hemoglobin concentration changes, laser speckle for cerebral blood flow (CBF) changes, and MRI for the BOLD signal. Older rats had smaller HbO (41% smaller), HbT (50%) and CBF (34%) responses, but the temporal dynamics did not exhibit significant age differences. The ratio of CBV to CBF responses was also smaller in older adults, potentially indicating a change in the compliance of vessels.

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

GLMdenoise: a fast, automated technique for denoising task-based fMRI data

In task-based functional magnetic resonance imaging (fMRI), researchers seek to measure fMRI signals related to a given task or condition. In many circumstances, measuring this signal of interest is limited by noise. In this study, we present GLMdenoise, a technique that improves signal-to-noise ratio (SNR) by entering noise regressors into a general linear model (GLM) analysis of fMRI data. The noise regressors are derived by conducting an initial model fit to determine voxels unrelated to the experimental paradigm, performing principal components analysis (PCA) on the time-series of these voxels, and using cross-validation to select the optimal number of principal components to use as noise regressors. Due to the use of data resampling, GLMdenoise requires and is best suited for datasets involving multiple runs (where conditions repeat across runs). We show that GLMdenoise consistently improves cross-validation accuracy of GLM estimates on a variety of event-related experimental datasets and is accompanied by substantial gains in SNR. To promote practical application of methods, we provide MATLAB code implementing GLMdenoise. Furthermore, to help compare GLMdenoise to other denoising methods, we present the Denoise Benchmark (DNB), a public database and architecture for evaluating denoising methods. The DNB consists of the datasets described in this paper, a code framework that enables automatic evaluation of a denoising method, and implementations of several denoising methods, including GLMdenoise, the use of motion parameters as noise regressors, ICA-based denoising, and RETROICOR/RVHRCOR. Using the DNB, we find that GLMdenoise performs best out of all of the denoising methods we tested.

Short repetition time multiband echo-planar imaging with simultaneous pulse recording allows dynamic imaging of the cardiac pulsation signal

PURPOSE

Recently developed simultaneous multislice echo-planar imaging (EPI) sequences permit imaging of the whole brain at short repetition time (TR), allowing the cardiac fluctuations to be fully sampled in blood-oxygen-level dependent functional MRI (BOLD fMRI). A novel low computational analytical method was developed to dynamically map the passage of the pulsation signal through the brain and visualize the whole cerebral vasculature affected by the pulse signal. This algorithm is based on a simple combination of fast BOLD fMRI and the scanner's own built-in pulse oximeter.

METHODS

Multiple, temporally shifted copies of the pulse oximeter data (with 0.08 s shifting step and coverage of a 1-s span) were downsampled and used as cardiac pulsation regressors in a general linear model based analyses (FSL) of the fMRI data. The resulting concatenated z-statistics maps show the voxels that are affected as the cardiac signal travels through the brain.

RESULTS

Many voxels were highly correlated with the pulsation regressor or its temporally shifted version. The dynamic and static cardiac pulsation maps obtained from both the task and resting state scans, resembled cerebral vasculature.

CONCLUSION

The results demonstrated: (i) cardiac pulsation significantly affects most voxels in the brain; (ii) combining fast fMRI and this analytical method can reveal additional clinical information to functional studies.

The BOLD signal and neurovascular coupling in autism

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Neurovascular coupling and energy use may be changed in autism.

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The relationship between neural activity and the BOLD signal may be altered in autism.

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Simply comparing the BOLD signal of control and autistic people may not be meaningful.

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Combined techniques will aid the interpretation of group differences in the BOLD signal.

BOLD (blood oxygen level dependent) fMRI (functional magnetic resonance imaging) is commonly used to study differences in neuronal activity between human populations. As the BOLD response is an indirect measure of neuronal activity, meaningful interpretation of differences in BOLD responses between groups relies upon a stable relationship existing between neuronal activity and the BOLD response across these groups. However, this relationship can be altered by changes in neurovascular coupling or energy consumption, which would lead to problems in identifying differences in neuronal activity. In this review, we focus on fMRI studies of people with autism, and comparisons that are made of their BOLD responses with those of control groups. We examine neurophysiological differences in autism that may alter neurovascular coupling or energy use, discuss recent studies that have used fMRI to identify differences between participants with autism and control participants, and explore experimental approaches that could help attribute between-group differences in BOLD signals to either neuronal or neurovascular factors.

Time-on-task and sleep deprivation effects are evidenced in overlapping brain areas

Both sleep deprivation and extended task engagement (time-on-task) have been shown to degrade performance in tasks evaluating sustained attention. Here we used pulsed arterial spin labeling (pASL) to study participants engaged in a demanding selective attention task. The participants were imaged twice, once after a normal night of sleep and once after approximately 24h of total sleep deprivation. We compared task-related changes in BOLD signal alongside ASL-based cerebral blood flow (CBF) changes. We also collected resting baseline CBF data prior to and following task performance. Both BOLD fMRI and ASL identified spatially congruent task activation in ventral visual cortex and fronto-parietal regions. Sleep deprivation and time-on-task caused a decline of both measures in ventral visual cortex. BOLD fMRI also revealed such declines in fronto-parietal cortex. Only early visual cortex showed a significant upward shift in resting baseline CBF following sleep deprivation, suggesting that the neural consequences of both SD and ToT are primarily evident in task-evoked signals. We conclude that BOLD fMRI is preferable to pASL in studies evaluating sleep deprivation given its better signal to noise characteristics and the relative paucity of state differences in baseline CBF.

PHYCAA+: an optimized, adaptive procedure for measuring and controlling physiological noise in BOLD fMRI

The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al., 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.

Sampling rate dependence of correlation at long time lags in BOLD fMRI measurements on humans and gel phantoms

The aim of this study is to investigate the effects of sampling rate on Hurst exponents derived from Blood Oxygenation Level Dependent functional Magnetic Resonance Imaging (BOLD fMRI) resting state time series. fMRI measurements were performed on 2 human subjects and a selection of gel phantoms. From these, Hurst exponents were calculated. It was found that low sampling rates induced non-trivial exponents at sharp material transitions, and that Hurst exponents of human measurements had a strong TR-dependence. The findings are compared to theoretical considerations regarding the fractional Gaussian noise model and resampling, and it is found that the implications are problematic. This result should have a direct influence on the way future studies of low-frequency variation in BOLD fMRI data are conducted, especially if the fractional Gaussian noise model is considered. We recommend either using a different model (examples of such are referenced in the conclusion), or standardizing experimental procedures along an optimal sampling rate.

A multimodal perspective on the composition of cortical oscillations

An expanding corpus of research details the relationship between functional magnetic resonance imaging (fMRI) measures and neuronal network oscillations. Typically, integrated electroencephalography and fMRI, or parallel magnetoencephalography (MEG) and fMRI are used to draw inference about the consanguinity of BOLD and electrical measurements. However, there is a relative dearth of information about the relationship between E/MEG and the focal networks from which these signals emanate. Consequently, the genesis and composition of E/MEG oscillations requires further clarification. Here we aim to contribute to understanding through a series of parallel measurements of primary motor cortex (M1) oscillations, using human MEG and in vitro rodent local field potentials. We compare spontaneous activity in the ∼10 Hz mu and 15–30 Hz beta frequency ranges and compare MEG signals with independent and integrated layers III and V (LIII/LV) from in vitro recordings. We explore the mechanisms of oscillatory generation, using specific pharmacological modulation with the GABA-A alpha-1 subunit modulator zolpidem. Finally, to determine the contribution of cortico-cortical connectivity, we recorded in vitro M1, during an incision to sever lateral connections between M1 and S1 cortices. We demonstrate that frequency distribution of MEG signals appear have closer statistically similarity with signals from integrated rather than independent LIII/LV laminae. GABAergic modulation in both modalities elicited comparable changes in the power of the beta band. Finally, cortico-cortical connectivity in sensorimotor cortex (SMC) appears to directly influence the power of the mu rhythm in LIII. These findings suggest that the MEG signal is an amalgam of outputs from LIII and LV, that multiple frequencies can arise from the same cortical area and that in vitro and MEG M1 oscillations are driven by comparable mechanisms. Finally, cortico-cortical connectivity is reflected in the power of the SMC mu rhythm.

A comparison of physiologic modulators of fMRI signals

One of the main obstacles in quantitative interpretation of functional magnetic resonance imaging (fMRI) signal is that this signal is influenced by non-neural factors such as vascular properties of the brain, which effectively increases signal variability. One approach to account for non-neural components is to identify and measure these confounding factors and to include them as covariates in data analysis or interpretation. Previously, several research groups have independently identified four potential physiologic modulators of fMRI signals, including baseline venous oxygenation (Yv ), cerebrovascular reactivity (CVR), resting state BOLD fluctuation amplitude (RSFA), and baseline cerebral blood flow (CBF). This study sought to directly compare the modulation effects of these indices in the same fMRI session. The physiologic parameters were measured with techniques comparable with those used in the previous studies except for CBF, which was determined globally with a velocity-based phase-contrast MRI (instead of arterial-spin-labeling MRI). Using an event-related, scene-categorization fMRI task, we showed that the fMRI signal amplitude was positively correlated with CVR (P < 0.0001) and RSFA (P = 0.002), while negatively correlated with baseline Yv (P < 0.0001). The fMRI-CBF correlation did not reach significance, although the (negative) sign of the correlation was consistent with the earlier study. Furthermore, among the physiologic modulators themselves, significant correlations were observed between baseline Yv and baseline CBF (P = 0.01), and between CVR and RSFA (P = 0.05), suggesting that some of the modulators may partly be of similar physiologic origins. These observations as well as findings in recent literature suggest that additional measurement of physiologic modulator(s) in an fMRI session may provide a practical approach to control for inter-subject variations and to improve the ability of fMRI in detecting disease or medication related differences. Hum Brain Mapp 34:2078-2088, 2013. © 2011 Wiley Periodicals, Inc.

Advances in High-Field BOLD fMRI

This review article examines the current state of BOLD fMRI at a high magnetic field strength of 7 Tesla. The following aspects are covered: a short description of the BOLD contrast, spatial and temporal resolution, BOLD sensitivity, localization and spatial specificity, technical challenges as well as an outlook on future developments are given. It is shown that the main technical challenges of performing BOLD fMRI at high magnetic field strengths-namely development of array coils, imaging sequences and parallel imaging reconstruction-have been solved successfully. The combination of these developments has lead to the availability of high-resolution BOLD fMRI protocols that are able to cover the whole brain with a repetition time (TR) shorter than 3 s. The structural information available from these high-resolution fMRI images itself is already very detailed, which helps to co-localize structure and function. Potential future applications include whole-brain connectivity analysis on a laminar resolution and single subject examinations.

Neural substrates of reliability-weighted visual-tactile multisensory integration

As sensory systems deteriorate in aging or disease, the brain must relearn the appropriate weights to assign each modality during multisensory integration. Using blood-oxygen level dependent functional magnetic resonance imaging of human subjects, we tested a model for the neural mechanisms of sensory weighting, termed “weighted connections.” This model holds that the connection weights between early and late areas vary depending on the reliability of the modality, independent of the level of early sensory cortex activity. When subjects detected viewed and felt touches to the hand, a network of brain areas was active, including visual areas in lateral occipital cortex, somatosensory areas in inferior parietal lobe, and multisensory areas in the intraparietal sulcus (IPS). In agreement with the weighted connection model, the connection weight measured with structural equation modeling between somatosensory cortex and IPS increased for somatosensory-reliable stimuli, and the connection weight between visual cortex and IPS increased for visual-reliable stimuli. This double dissociation of connection strengths was similar to the pattern of behavioral responses during incongruent multisensory stimulation, suggesting that weighted connections may be a neural mechanism for behavioral reliability weighting.