Single-trial variability in event-related BOLD signals

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14∼62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Functional magnetic resonance imaging (fMRI) using a blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function noninvasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the spatial signal specificity can be degraded due to signal leakage from activated lower layers to superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using the VAscular-Space-Occupancy (VASO) contrast have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms such as blood volume and oxygenation changes and to interpret the measured depth-dependent responses, models are needed which reflect vascular properties at this scale. For this purpose, we extended and modified the "cortical vascular model" previously developed to predict layer-specific BOLD signal changes in human primary visual cortex to also predict a layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental data provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV change mainly occurs in small arterioles, capillaries, and intracortical arteries and that the contribution from venules and ICVs is smaller. Our results confirm that VASO is less susceptible to large vessel effects compared to BOLD, as blood volume changes in intracortical arteries did not substantially affect the resulting depth-dependent VASO profiles, whereas depth-dependent BOLD profiles showed a bias towards signal contributions from intracortical veins.

Sources of independent mu components reveal different brain areas involved in motor imagery, motor execution, and movement observation

To answer the question of whether the same brain circuit(s) facilitates motor imagery (MI), motor execution (ME), and movement observation (MO), we conducted electroencephalography (EEG) experiment combining the three motor conditions in the same experimental runs. The EEG data were analyzed using two different independent component analysis (ICA) decomposition approaches: a single ICA decomposition on all EEG data combined and separate ICA decomposition on the EEG data obtained from the separate conditions. The results indicated that the separate ICA approach may provide a better fit to the EEG data obtained from the separate conditions to deliver specific independent right mu components with distinct topographies for each of the motor conditions. The topography of the MI condition covered the brain regions posterior to the central sulcus (P4 EEG channel); the ME condition covered the brain regions anterior to the central sulcus (C4 EEG channel), and the MO condition had broader coverage with the main activation in the premotor region (CP4 EEG channel). The source localization results also exhibited significant differences among the motor conditions. In addition, the result of single ICA decomposition resembled the result of separate ICA decomposition on the EEG data of ME with similar topographies and closely located EEG sources. This finding may further indicate that the result of single ICA decomposition may be dominated by the ME motor condition because it manifests higher data variance than the other two motor conditions.

The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI

Holding a conversation means that speech must be started, maintained, and stopped continuously. The brain networks that underlie these aspects of speech motor control remain poorly understood. Here we collected functional magnetic resonance imaging (fMRI) data while participants produced normal and fast rate speech in response to sequences of visually presented objects. We took a non-conventional approach to fMRI data analysis that allowed us to study speech motor behavior as it unfolded over time. To this end, whole-brain fMRI signals were extracted in stimulus-locked epochs using slice-based fMRI. These data were then subjected to group independent component analysis to discover spatially independent networks that were associated with different temporal activation profiles. The results revealed two basic brain networks with different temporal dynamics: a cortical network that was activated continuously during speech production, and a second cortico-subcortical network that increased in activity during the initiation and suppression of speech production. Additional analyses explored whether key areas involved in motor suppression such as the right inferior frontal gyrus, sub-thalamic nucleus and pre-supplementary motor area provide first-order signals to stop speech. The results reveal for the first time the brain networks associated with the initiation, maintenance, and suppression of speech motor behavior.

Effects of rTMS on the brain: is there value in variability?

The ability of repetitive transcranial magnetic stimulation (rTMS) to non-invasively induce neuroplasticity in the human cortex has opened exciting possibilities for its application in both basic and clinical research. Changes in the amplitude of motor evoked potentials (MEPs) elicited by single-pulse transcranial magnetic stimulation has so far provided a convenient model for exploring the neurophysiology of rTMS effects on the brain, influencing the ways in which these stimulation protocols have been applied therapeutically. However, a growing number of studies have reported large inter-individual variability in the mean MEP response to rTMS, raising legitimate questions about the usefulness of this model for guiding therapy. Although the increasing application of different neuroimaging approaches has made it possible to probe rTMS-induced neuroplasticity outside the motor cortex to measure changes in neural activity that impact other aspects of human behaviour, the high variability of rTMS effects on these measurements remains an important issue for the field to address. In this review, we seek to move away from the conventional facilitation/inhibition dichotomy that permeates much of the rTMS literature, presenting a non-standard approach for measuring rTMS-induced neuroplasticity. We consider the evidence that rTMS is able to modulate an individual's moment-to-moment variability of neural activity, and whether this could have implications for guiding the therapeutic application of rTMS.

N400 amplitude, latency, and variability reflect temporal integration of beat gesture and pitch accent during language processing

This study examines how across-trial (average) and trial-by-trial (variability in) amplitude and latency of the N400 event-related potential (ERP) reflect temporal integration of pitch accent and beat gesture. Thirty native English speakers viewed videos of a talker producing sentences with beat gesture co-occurring with a pitch accented focus word (synchronous), beat gesture co-occurring with the onset of a subsequent non-focused word (asynchronous), or the absence of beat gesture (no beat). Across trials, increased amplitude and earlier latency were observed when beat gesture was temporally asynchronous with pitch accenting than when it was temporally synchronous with pitch accenting or absent. Moreover, temporal asynchrony of beat gesture relative to pitch accent increased trial-by-trial variability of N400 amplitude and latency and influenced the relationship between across-trial and trial-by-trial N400 latency. These results indicate that across-trial and trial-by-trial amplitude and latency of the N400 ERP reflect temporal integration of beat gesture and pitch accent during language comprehension, supporting extension of the integrated systems hypothesis of gesture-speech processing and neural noise theories to focus processing in typical adult populations.

Noxious pressure stimulation demonstrates robust, reliable estimates of brain activity and self-reported pain

Functional neuroimaging techniques have provided great insight in the field of pain. Utilising these techniques, we have characterised pain-induced responses in the brain and improved our understanding of key pain-related phenomena. Despite the utility of these methods, there remains a need to assess the test retest reliability of pain modulated blood-oxygen-level-dependant (BOLD) MR signal across repeated sessions. This is especially the case for more novel yet increasingly implemented stimulation modalities, such as noxious pressure, and it is acutely important for multi-session studies considering treatment efficacy. In the present investigation, BOLD signal responses were estimated for noxious-pressure stimulation in a group of healthy participants, across two separate sessions. Test retest reliability of functional magnetic resonance imaging (fMRI) data and self-reported visual analogue scale measures were determined by the intra-class correlation coefficient. High levels of reliability were observed in several key brain regions known to underpin the pain experience, including in the thalamus, insula, somatosensory cortices, and inferior frontal regions, alongside "excellent" reliability of self-reported pain measures. These data demonstrate that BOLD-fMRI derived signals are a valuable tool for quantifying noxious responses pertaining to pressure stimulation. We further recommend the implementation of pressure as a stimulation modality in experimental applications.

Diffuse optical tomography to measure functional changes during motor tasks: a motor imagery study

The present work shows the spatial reliability of the diffuse optical tomography (DOT) system in a group of healthy subjects during a motor imagery task. Prior to imagery task performance, the subjects executed a motor task based on the finger to thumb opposition for motor training, and to corroborate the DOT spatial localization during the motor execution. DOT technology and data treatment allows us to distinguish oxy- and deoxyhemoglobin at the cerebral gyri level unlike the cerebral activations provided by fMRI series that were processed using different approaches. Here we show the DOT reliability showing functional activations at the cerebral gyri level during motor execution and motor imagery, which provide subtler cerebral activations than the motor execution. These results will allow the use of the DOT system as a monitoring device in a brain computer interface.

Dynamic brain connectivity attuned to the complexity of relative clause sentences revealed by a single-trial analysis

To explore the issue of how the human brain processes sentences with different levels of complexity, we sought to compare the neural substrates underlying the processing of Chinese subject-extracted relative clause (SRC) and object-extracted relative clause (ORC) sentences in a trial-by-trial fashion. Previous neuroimaging studies have demonstrated that the involvement of the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) is critical for the processing of relative clause (RC) sentences. In this study, we employed independent component analysis (ICA) to decompose brain activity into a set of independent components. Then, the independent component maps were spatially normalized using a surface-based approach in order to further spatially correlate and match the equivalent components from individual participants. The selected equivalent components indicated that the LIFG and the LSTG were consistently engaged in sentence processing among the participants. Subsequently, we observed alterations in the functional coupling between the LIFG and the LSTG in response to SRCs and ORCs using a Granger causality analysis. Specifically, comprehending Chinese ORCs with a canonical word order only involved a unidirectional connection from the LIFG to the LSTG for the integration of lexical-syntactic information. On the other hand, comprehending Chinese SRCs required bi-directional connectivity between the LIFG and the LSTG to fulfill increased integration demands in reconstructing the argument hierarchy due to a non-canonical word order. Furthermore, through a single-trial analysis, the strength of the connectivity from the LIFG to the LSTG was found to be significantly correlated with the complexity of the SRC sentences as quantified by eye-tracking measures. These findings indicated that the effective connectivity from the LIFG to the LSTG played an important role in the comprehension of complex sentences and that enhanced strength of this connectivity might reflect increased integration demands and restructuring attempts during sentence processing. Taken together, the results of the present study reveal that interregional interaction in the brain network for sentence processing can be dynamically engaged in response to different levels of complexity and also shed some light on the interpretation of neuroimaging and behavioral evidence when accounting for the nature of sentence complexity during reading.

Interactions between stimuli-evoked cortical activity and spontaneous low frequency oscillations measured with neuronal calcium

Spontaneous brain activity has been widely used to map brain connectivity. The interactions between task-evoked brain responses and the spontaneous cortical oscillations, especially within the low frequency range of ~0.1 ​Hz, are not fully understood. Trial-to-trial variabilities in brain's response to sensory stimuli and the ability for brain to detect under noisy conditions suggest an appreciable impact of the brain state. Using a multimodality imaging platform, we simultaneously imaged neuronal Ca²⁺ and cerebral hemodynamics at baseline and in response to single-pulse forepaw stimuli in rat's somatosensory cortex. The high sensitivity of this system enables detection of responses to very weak and strong stimuli and real time determination of low frequency oscillations without averaging. Results show that the ongoing neuronal oscillations inversely modulate Ca²⁺ transients evoked by sensory stimuli. High intensity stimuli reset the spontaneous neuronal oscillations to an unpreferable excitability following the stimulus. Cerebral hemodynamic responses also inversely interact with the spontaneous hemodynamic oscillations, correlating with the neuronal Ca²⁺ transient changes. The results reveal competing interactions between spontaneous oscillations and stimulation-evoked brain activities in somatosensory cortex and the resultant hemodynamics.

Reduced occipital GABA in Parkinson disease with visual hallucinations

Objective

To investigate the relationship between visual hallucinations in Parkinson disease (PD) and levels of γ-aminobutyric acid (GABA) in the primary visual cortex.

Methods

We utilized magnetic resonance spectroscopy to investigate occipital GABA levels in 36 participants with PD, 19 with and 17 without complex visual hallucinations, together with 20 healthy controls without hallucinations. In addition, we acquired T1-weighted MRI, whole-brain fMRI during a visual task, and diffusion tensor imaging.

Results

We found lower GABA+/creatine in PD with visual hallucinations (0.091 ± 0.010) vs those without (0.101 ± 0.010) and controls (0.099 ± 0.010) (F_2,49 = 4.5; p = 0.016). Reduced gray matter in the hallucinations group was also observed in the anterior temporal lobe. Although there were widespread reductions in white matter integrity in the visual hallucinations group, this was no longer significant after controlling for cognitive function.

Conclusions

The data suggest that reduced levels of GABA are associated with visual hallucinations in PD and implicate changes to the ventral visual stream in the genesis of visual hallucinations. Modulation of visual cortical excitability through, for example, pharmacologic intervention, may be a promising treatment avenue to explore.

Improving the signal detection accuracy of functional Magnetic Resonance Imaging

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.

How context alters value: The brain's valuation and affective regulation system link price cues to experienced taste pleasantness

Informational cues such as the price of a wine can trigger expectations about its taste quality and thereby modulate the sensory experience on a reported and neural level. Yet it is unclear how the brain translates such expectations into sensory pleasantness. We used a whole-brain multilevel mediation approach with healthy participants who tasted identical wines cued with different prices while their brains were scanned using fMRI. We found that the brain’s valuation system (BVS) in concert with the anterior prefrontal cortex played a key role in implementing the effect of price cues on taste pleasantness ratings. The sensitivity of the BVS to monetary rewards outside the taste domain moderated the strength of these effects. These findings provide novel evidence for the fundamental role that neural pathways linked to motivation and affective regulation play for the effect of informational cues on sensory experiences.

Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex

The blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal is a robust surrogate for local neuronal activity. However, it has been shown to vary substantially across subjects, brain regions, and repetitive measurements. This variability represents a limit to the precision of the BOLD response and the ability to reliably discriminate brain hemodynamic responses elicited by external stimuli or behavior that are nearby in time. While the temporal variability of the BOLD signal at human visual cortex has been found in the range of a few hundreds of milliseconds, the spatial distributions of the average and standard deviation of this temporal variability have not been quantitatively characterized. Here we use fMRI measurements with a high sampling rate (10Hz) to map the latency, intra- and inter-subject variability of the evoked BOLD signal in human primary (V1) visual cortices using an event-related fMRI paradigm. The latency relative to the average BOLD signal evoked by 30 stimuli was estimated to be 0.03±0.20s. Within V1, the absolute value of the relative BOLD latency was found correlated to intra- and inter-subject temporal variability. After comparing these measures to retinotopic maps, we found that locations with V1 areas sensitive to smaller eccentricity have later responses and smaller inter-subject variabilities. These correlations were found from data with either short inter-stimulus interval (ISI; average 4s) or long ISI (average 30s). Maps of the relative latency as well as inter-/intra-subject variability were found visually asymmetric between hemispheres. Our results suggest that the latency and variability of regional BOLD signal measured with high spatiotemporal resolution may be used to detect regional differences in hemodynamics to inform fMRI studies. However, the physiological origins of timing index distributions and their hemispheric asymmetry remain to be investigated.

Rethinking the Use of Neutral Faces as a Baseline in fMRI Neuroimaging Studies of Axis-I Psychiatric Disorders

Major Axis-I disorders including major depressive disorder (MDD), bipolar disorder (BD), anxiety disorder, and schizophrenia are associated with a host of aberrations in the way social stimuli are processed. Face perception tasks are often used in neuroimaging research of emotion processing in both healthy and patient populations, and to date, there exists a mounting body of evidence, both behavioral and within the brain, indicating that emotional faces compared to neutral faces are processed abnormally by those with Axis-I disorders relative to healthy control (HC) groups. The use of neutral faces as a "baseline control condition" is predicated on the assumption that neutral faces are processed in the same way HCs and individuals with major Axis-I disorders. In this paper, existing fMRI studies examining the way neutral faces are processed in groups with Axis-I disorders involving socioaffective perception are reviewed. In reviewing available studies, a consistent pattern of results demonstrated that these disorders are associated with abnormal frontolimbic activity in response to neutral faces and in particular within the amygdala and prefrontal regions such as the dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC) compared to HC groups. Specifically, increased amygdala activation was consistently reported in response to neutral faces in anxiety disorders and schizophrenia. Abnormal medial PFC activity was reported in patients with MDD, and patients with BD exhibit decreased activity in the DLPFC and ACC relative to HCs. In addition, specific suggestions to overcome these obstacles with new research and additional analyses are discussed.

Pros and Cons of Using the Informed Basis Set to Account for Hemodynamic Response Variability with Developmental Data

Conventional analysis of functional magnetic resonance imaging (fMRI) data using the general linear model (GLM) employs a neural model convolved with a canonical hemodynamic response function (HRF) peaking 5 s after stimulation. Incorporation of a further basis function, namely the canonical HRF temporal derivative, accounts for delays in the hemodynamic response to neural activity. A population that may benefit from this flexible approach is children whose hemodynamic response is not yet mature. Here, we examined the effects of using the set based on the canonical HRF plus its temporal derivative on both first- and second-level GLM analyses, through simulations and using developmental data (an fMRI dataset on proprioceptive mapping in children and adults). Simulations of delayed fMRI first-level data emphasized the benefit of carrying forward to the second-level a derivative boost that combines derivative and nonderivative beta estimates. In the experimental data, second-level analysis using a paired t-test showed increased mean amplitude estimate (i.e., increased group contrast mean) in several brain regions related to proprioceptive processing when using the derivative boost compared to using only the nonderivative term. This was true especially in children. However, carrying forward to the second-level the individual derivative boosts had adverse consequences on random-effects analysis that implemented one-sample t-test, yielding increased between-subject variance, thus affecting group-level statistic. Boosted data also presented a lower level of smoothness that had implication for the detection of group average activation. Imposing soft constraints on the derivative boost by limiting the time-to-peak range of the modeled response within a specified range (i.e., 4–6 s) mitigated these issues. These findings support the notion that there are pros and cons to using the informed basis set with developmental data.

A novel method for classifying cortical state to identify the accompanying changes in cerebral hemodynamics

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We classified brain state using a vector-based categorisation of neural frequencies.

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Changes in cerebral blood volume (CBV) were observed when brain state altered.

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During these state alterations, changes in blood oxygenation were also found.

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State dependent haemodynamic changes could affect blood based brain imaging.

Background

Many brain imaging techniques interpret the haemodynamic response as an indirect indicator of underlying neural activity. However, a challenge when interpreting this blood based signal is how changes in brain state may affect both baseline and stimulus evoked haemodynamics.

New method

We developed an Automatic Brain State Classifier (ABSC), validated on data from anaesthetised rodents. It uses vectorised information obtained from the windowed spectral frequency power of the Local Field Potential. Current state is then classified by comparing this vectorised information against that calculated from state specific training datasets.

Results

The ABSC identified two user defined brain states (synchronised and desynchronised), with high accuracy (∼90%). Baseline haemodynamics were found to be significantly different in the two identified states. During state defined periods of elevated baseline haemodynamics we found significant decreases in evoked haemodynamic responses to somatosensory stimuli.

Comparison to existing methods

State classification – The ABSC (∼90%) demonstrated greater accuracy than clustering (∼66%) or ‘power threshold’ (∼64%) methods of comparison.

Haemodynamic averaging – Our novel approach of selectively averaging stimulus evoked haemodynamic trials by brain state yields higher quality data than creating a single average from all trials.

Conclusions

The ABSC can account for some of the commonly observed trial-to-trial variability in haemodynamic responses which arises from changes in cortical state. This variability might otherwise be incorrectly attributed to alternative interpretations. A greater understanding of the effects of cortical state on haemodynamic changes could be used to inform techniques such as general linear modelling (GLM), commonly used in fMRI.

Exploiting Complexity Information for Brain Activation Detection

We present a complexity-based approach for the analysis of fMRI time series, in which sample entropy (SampEn) is introduced as a quantification of the voxel complexity. Under this hypothesis the voxel complexity could be modulated in pertinent cognitive tasks, and it changes through experimental paradigms. We calculate the complexity of sequential fMRI data for each voxel in two distinct experimental paradigms and use a nonparametric statistical strategy, the Wilcoxon signed rank test, to evaluate the difference in complexity between them. The results are compared with the well known general linear model based Statistical Parametric Mapping package (SPM12), where a decided difference has been observed. This is because SampEn method detects brain complexity changes in two experiments of different conditions and the data-driven method SampEn evaluates just the complexity of specific sequential fMRI data. Also, the larger and smaller SampEn values correspond to different meanings, and the neutral-blank design produces higher predictability than threat-neutral. Complexity information can be considered as a complementary method to the existing fMRI analysis strategies, and it may help improving the understanding of human brain functions from a different perspective.

Effect of trial-to-trial variability on optimal event-related fMRI design: Implications for Beta-series correlation and multi-voxel pattern analysis

Functional magnetic resonance imaging (fMRI) studies typically employ rapid, event-related designs for behavioral reasons and for reasons associated with statistical efficiency. Efficiency is calculated from the precision of the parameters (Betas) estimated from a General Linear Model (GLM) in which trial onsets are convolved with a Hemodynamic Response Function (HRF). However, previous calculations of efficiency have ignored likely variability in the neural response from trial to trial, for example due to attentional fluctuations, or different stimuli across trials. Here we compare three GLMs in their efficiency for estimating average and individual Betas across trials as a function of trial variability, scan noise and Stimulus Onset Asynchrony (SOA): “Least Squares All” (LSA), “Least Squares Separate” (LSS) and “Least Squares Unitary” (LSU). Estimation of responses to individual trials in particular is important for both functional connectivity using “Beta-series correlation” and “multi-voxel pattern analysis” (MVPA). Our simulations show that the ratio of trial-to-trial variability to scan noise impacts both the optimal SOA and optimal GLM, especially for short SOAs < 5 s: LSA is better when this ratio is high, whereas LSS and LSU are better when the ratio is low. For MVPA, the consistency across voxels of trial variability and of scan noise is also critical. These findings not only have important implications for design of experiments using Beta-series regression and MVPA, but also statistical parametric mapping studies that seek only efficient estimation of the mean response across trials.

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Trial-wise connectivity and multi-voxel pattern analyses can use different GLMs.

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Trial-to-trial variability affects both the optimal GLM and optimal SOA.

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Optimal GLM and SOA depend on estimation of population or sample mean.

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Optimal GLM for single voxel analysis depends on ratio of trial- to scan-variability.

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Optimal GLM for pattern analysis depends on coherency of variability across voxels.

Rapidly acquired multisensory association in the olfactory cortex

BACKGROUND

The formation of an odor percept in humans is strongly associated with visual information. However, much less is known about the roles of learning and memory in shaping the multisensory nature of odor representations in the brain.

METHOD

The dynamics of odor and visual association in olfaction was investigated using three functional magnetic resonance imaging (fMRI) paradigms. In two paradigms, a visual cue was paired with an odor. In the third, the same visual cue was never paired with an odor. In this experimental design, if the visual cue was not influenced by odor-visual pairing, then the blood-oxygen-level-dependent (BOLD) signal elicited by subsequent visual cues should be similar across all three paradigms. Additionally, intensity, a major dimension of odor perception, was used as a modulator of associative learning which was characterized in terms of the spatiotemporal behavior of the BOLD signal in olfactory structures.

RESULTS

A single odor-visual pairing cue could subsequently induce primary olfactory cortex activity when only the visual cue was presented. This activity was intensity dependent and was also detected in secondary olfactory structures and hippocampus.

CONCLUSION

This study provides evidence for a rapid learning response in the olfactory system by a visual cue following odor and visual cue pairing. The novel data and paradigms suggest new avenues to explore the dynamics of odor learning and multisensory representations that contribute to the construction of a unified odor percept in the human brain.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.

Nonlinear Bayesian estimation of BOLD signal under non-Gaussian noise

Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF.

Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging

BACKGROUND

Recent advances in genetically engineered calcium and membrane potential indicators provide the potential to estimate the activation dynamics of individual neurons within larger, mesoscale networks (100s-1000+neurons). However, a fully integrated automated workflow for the analysis and visualization of neural microcircuits from high speed fluorescence imaging data is lacking.

NEW METHOD

Here we introduce FluoroSNNAP, Fluorescence Single Neuron and Network Analysis Package. FluoroSNNAP is an open-source, interactive software developed in MATLAB for automated quantification of numerous biologically relevant features of both the calcium dynamics of single-cells and network activity patterns. FluoroSNNAP integrates and improves upon existing tools for spike detection, synchronization analysis, and inference of functional connectivity, making it most useful to experimentalists with little or no programming knowledge.

RESULTS

We apply FluoroSNNAP to characterize the activity patterns of neuronal microcircuits undergoing developmental maturation in vitro. Separately, we highlight the utility of single-cell analysis for phenotyping a mixed population of neurons expressing a human mutant variant of the microtubule associated protein tau and wild-type tau.

COMPARISON WITH EXISTING METHOD(S)

We show the performance of semi-automated cell segmentation using spatiotemporal independent component analysis and significant improvement in detecting calcium transients using a template-based algorithm in comparison to peak-based or wavelet-based detection methods. Our software further enables automated analysis of microcircuits, which is an improvement over existing methods.

CONCLUSIONS

We expect the dissemination of this software will facilitate a comprehensive analysis of neuronal networks, promoting the rapid interrogation of circuits in health and disease.

Aberrant error processing in relation to symptom severity in obsessive-compulsive disorder: A multimodal neuroimaging study

BACKGROUND

Obsessive-compulsive disorder (OCD) is characterized by maladaptive repetitive behaviors that persist despite feedback. Using multimodal neuroimaging, we tested the hypothesis that this behavioral rigidity reflects impaired use of behavioral outcomes (here, errors) to adaptively adjust responses. We measured both neural responses to errors and adjustments in the subsequent trial to determine whether abnormalities correlate with symptom severity. Since error processing depends on communication between the anterior and the posterior cingulate cortex, we also examined the integrity of the cingulum bundle with diffusion tensor imaging.

METHODS

Participants performed the same antisaccade task during functional MRI and electroencephalography sessions. We measured error-related activation of the anterior cingulate cortex (ACC) and the error-related negativity (ERN). We also examined post-error adjustments, indexed by changes in activation of the default network in trials surrounding errors.

RESULTS

OCD patients showed intact error-related ACC activation and ERN, but abnormal adjustments in the post- vs. pre-error trial. Relative to controls, who responded to errors by deactivating the default network, OCD patients showed increased default network activation including in the rostral ACC (rACC). Greater rACC activation in the post-error trial correlated with more severe compulsions. Patients also showed increased fractional anisotropy (FA) in the white matter underlying rACC.

CONCLUSIONS

Impaired use of behavioral outcomes to adaptively adjust neural responses may contribute to symptoms in OCD. The rACC locus of abnormal adjustment and relations with symptoms suggests difficulty suppressing emotional responses to aversive, unexpected events (e.g., errors). Increased structural connectivity of this paralimbic default network region may contribute to this impairment.

ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

Poststimulus undershoots in cerebral blood flow and BOLD fMRI responses are modulated by poststimulus neuronal activity

fMRI is the foremost technique for noninvasive measurement of human brain function. However, its utility is limited by an incomplete understanding of the relationship between neuronal activity and the hemodynamic response. Though the primary peak of the hemodynamic response is modulated by neuronal activity, the origin of the typically negative poststimulus signal is poorly understood and its amplitude assumed to covary with the primary response. We use simultaneous recordings of EEG with blood oxygenation level-dependent (BOLD) and cerebral blood flow (CBF) fMRI during unilateral median nerve stimulation to show that the poststimulus fMRI signal is neuronally modulated. We observe high spatial agreement between concurrent BOLD and CBF responses to median nerve stimulation, with primary signal increases in contralateral sensorimotor cortex and primary signal decreases in ipsilateral sensorimotor cortex. During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8-13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism. Therefore we present conclusive evidence that the fMRI time course represents a hemodynamic signature of at least two distinct temporal phases of neuronal activity, substantially improving understanding of the origin of the BOLD response and increasing the potential measurements of brain function provided by fMRI. We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.

Neural markers of errors as endophenotypes in neuropsychiatric disorders

Learning from errors is fundamental to adaptive human behavior. It requires detecting errors, evaluating what went wrong, and adjusting behavior accordingly. These dynamic adjustments are at the heart of behavioral flexibility and accumulating evidence suggests that deficient error processing contributes to maladaptively rigid and repetitive behavior in a range of neuropsychiatric disorders. Neuroimaging and electrophysiological studies reveal highly reliable neural markers of error processing. In this review, we evaluate the evidence that abnormalities in these neural markers can serve as sensitive endophenotypes of neuropsychiatric disorders. We describe the behavioral and neural hallmarks of error processing, their mediation by common genetic polymorphisms, and impairments in schizophrenia, obsessive-compulsive disorder, and autism spectrum disorders. We conclude that neural markers of errors meet several important criteria as endophenotypes including heritability, established neuroanatomical and neurochemical substrates, association with neuropsychiatric disorders, presence in syndromally-unaffected family members, and evidence of genetic mediation. Understanding the mechanisms of error processing deficits in neuropsychiatric disorders may provide novel neural and behavioral targets for treatment and sensitive surrogate markers of treatment response. Treating error processing deficits may improve functional outcome since error signals provide crucial information for flexible adaptation to changing environments. Given the dearth of effective interventions for cognitive deficits in neuropsychiatric disorders, this represents a potentially promising approach.

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.

Massive modulation of brain areas after mechanical pain stimulation: a time-resolved FMRI study

To date, relatively little is known about the spatiotemporal aspects of whole-brain blood oxygenation level-dependent (BOLD) responses to brief nociceptive stimuli. It is known that the majority of brain areas show a stimulus-locked response, whereas only some are characterized by a canonical hemodynamic response function. Here, we investigated the time course of brain activations in response to mechanical pain stimulation applied to participants' hands while they were undergoing functional magnetic resonance imaging (fMRI) scanning. To avoid any assumption about the shape of BOLD response, we used an unsupervised data-driven method to group voxels sharing a time course similar to the BOLD response to the stimulus and found that whole-brain BOLD responses to painful mechanical stimuli elicit massive activation of stimulus-locked brain areas. This pattern of activations can be segregated into 5 clusters, each with a typical temporal profile. In conclusion, we show that an extensive activity of multiple networks is engaged at different time latencies after presentation of a noxious stimulus. These findings aim to motivate research on a controversial topic, such as the temporal profile of BOLD responses, the variability of these response profiles, and the interaction between the stimulus-related BOLD response and ongoing fluctuations in large-scale brain networks.

An improvement of independent component analysis with projection method applied to multi-task fMRI data

Independent Component Analysis with projection (ICAp) method proposed by Long et al. Hum. Brain Mapp. 30 (2009) 417-431, can solve the interaction among task-related components of multi-task functional magnetic resonance imaging (fMRI) data. However, the departure of the ideal homodynamic response function (HRF) for projection from the true HRF may worse the ICAp results. In order to improve the performance of ICAp, the deconvolved ICAp (DICAp) method is proposed. Both the simulated and real fMRI experiments demonstrate that DICAp can separate more accurate time course corresponding to each task-related components and is more powerful to detect regions activated by each task only than ICAp.

Repeated decompositions reveal the stability of infomax decomposition of fMRI data

In this study, we decomposed 12 fMRI data sets from six subjects each 101 times using the infomax algorithm. The first decomposition was taken as a reference decomposition; the others were used to form a component matrix of 100 by 100 components. Equivalence relations between components in this matrix, defined as maximum spatial correlations to the components of the reference decomposition, were found by the Hungarian sorting method and used to form 100 equivalence classes for each data set. We then tested the reproducibility of the matched components in the equivalence classes using uncertainty measures based on component distributions, time courses, and ROC curves. Infomax ICA rarely failed to derive nearly the same components in different decompositions. Very few components per data set were poorly reproduced, even using vector angle uncertainty measures stricter than correlation and detection theory measures.

ICA Denoising for Event-Related fMRI Studies

The poor SNR of fMRI data requires that many repetitive trials be performed during an event-related experiment to obtain statistically significant levels of inferred brain activity. This is costly in terms of scanner time, necessitates that subjects perform the behavioural task(s) for long durations which may induce fatigue, and vastly increases the amount of data generated. In this paper, we present a method to enhance the statistical effect size using ICA, so that the same level of significance can be obtained with shorter scanning times. We perform ICA on fMRI data from a simple event-related motor task by projecting the original data onto the linear subspace defined by the task-related ICA components. This essentially denoises the signal and results in significant improvement in the effect size. Using simulations we demonstrate that the proposed ICA-denoising procedure is robust to a variety of realistic noise models and enhances the performance of least squares estimates of the evoked hemodynamic response.

Decoding semantics across fMRI sessions with different stimulus modalities: a practical MVPA study

Both embodied and symbolic accounts of conceptual organization would predict partial sharing and partial differentiation between the neural activations seen for concepts activated via different stimulus modalities. But cross-participant and cross-session variability in BOLD activity patterns makes analyses of such patterns with MVPA methods challenging. Here, we examine the effect of cross-modal and individual variation on the machine learning analysis of fMRI data recorded during a word property generation task. We present the same set of living and non-living concepts (land-mammals, or work tools) to a cohort of Japanese participants in two sessions: the first using auditory presentation of spoken words; the second using visual presentation of words written in Japanese characters. Classification accuracies confirmed that these semantic categories could be detected in single trials, with within-session predictive accuracies of 80–90%. However cross-session prediction (learning from auditory-task data to classify data from the written-word-task, or vice versa) suffered from a performance penalty, achieving 65–75% (still individually significant at p « 0.05). We carried out several follow-on analyses to investigate the reason for this shortfall, concluding that distributional differences in neither time nor space alone could account for it. Rather, combined spatio-temporal patterns of activity need to be identified for successful cross-session learning, and this suggests that feature selection strategies could be modified to take advantage of this.

Loss of reliable temporal structure in event-related averaging of naturalistic stimuli

To separate neural signals from noise, brain responses measured in neuroimaging are routinely averaged across space and time. However, such procedures may obscure some properties of neural activity. Recently, multi-voxel pattern analysis methods have demonstrated that patterns of activity across voxels contain valuable information that is concealed by spatial averaging. Here we show that temporal patterns of neural activity contain information that can discriminate different stimuli, even within brain regions that show no net activation to that stimulus class. Furthermore, we find that in many brain regions, responses to natural stimuli are highly context dependent. In such cases, prototypical event-related responses do not even exist for individual stimuli, so that averaging responses to the same stimulus within different contexts may worsen the effective signal-to-noise. As a result, analysis of the temporal structures of single events can reveal aspects of neural dynamics which cannot be detected using standard event-related averaging methods.

Abnormally persistent fMRI activation during antisaccades in schizophrenia: a neural correlate of perseveration?

OBJECTIVE

Impaired antisaccade performance is a consistent cognitive finding in schizophrenia. Antisaccades require both response inhibition and volitional motor programming, functions that are essential to flexible responding. We investigated whether abnormal timing of hemodynamic responses (HDRs) to antisaccades might contribute to perseveration of ocular motor responses in schizophrenia. We focused on the frontal eye field (FEF), which has been implicated in the persistent effects of antisaccades on subsequent responses in healthy individuals.

METHOD

Eighteen chronic, medicated schizophrenia outpatients and 15 healthy controls performed antisaccades and prosaccades during functional MRI. Finite impulse response models provided unbiased estimates of event-related HDRs. We compared groups on the peak amplitude, time-to-peak, and full-width half-max of the HDRs.

RESULTS

In patients, HDRs in bilateral FEF were delayed and prolonged but ultimately of similar amplitude to that of controls. These abnormalities were present for antisaccades, but not prosaccades, and were not seen in a control region. More prolonged HDRs predicted slower responses in trials that followed an antisaccade. This suggests that persistent FEF activity following an antisaccade contributes to inter-trial effects on latency.

CONCLUSIONS

Delayed and prolonged HDRs for antisaccades in schizophrenia suggest that the functions necessary for successful antisaccade performance take longer to implement and are more persistent. If abnormally persistent neural responses on cognitively demanding tasks are a more general feature of schizophrenia, they may contribute to response perseveration, a classic behavioral abnormality. These findings also underscore the importance of evaluating the temporal dynamics of neural activity to understand cognitive dysfunction in schizophrenia.

Predicting functional cortical ROIs via DTI-derived fiber shape models

Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.

A linear structural equation model for covert verb generation based on independent component analysis of FMRI data from children and adolescents

Human language is a complex and protean cognitive ability. Young children, following well defined developmental patterns learn language rapidly and effortlessly producing full sentences by the age of 3 years. However, the language circuitry continues to undergo significant neuroplastic changes extending well into teenage years. Evidence suggests that the developing brain adheres to two rudimentary principles of functional organization: functional integration and functional specialization. At a neurobiological level, this distinction can be identified with progressive specialization or focalization reflecting consolidation and synaptic reinforcement of a network (Lenneberg, 1967; Muller et al., 1998; Berl et al., 2006). In this paper, we used group independent component analysis and linear structural equation modeling (McIntosh and Gonzalez-Lima, 1994; Karunanayaka et al., 2007) to tease out the developmental trajectories of the language circuitry based on fMRI data from 336 children ages 5–18 years performing a blocked, covert verb generation task. The results are analyzed and presented in the framework of theoretical models for neurocognitive brain development. This study highlights the advantages of combining both modular and connectionist approaches to cognitive functions; from a methodological perspective, it demonstrates the feasibility of combining data-driven and hypothesis driven techniques to investigate the developmental shifts in the semantic network.

Full-brain auto-regressive modeling (FARM) using fMRI

In order to fully uncover the information potentially available in the fMRI signal, we model it as a multivariate auto-regressive process. To infer the model, we do not apply any form of clustering or dimensionality reduction, and solve the problem of under-determinacy using sparse regression. We find that only a few small clusters (with average size of 3-4 voxels) are useful in predicting the activity of other voxels, and demonstrate remarkable consistency within a subject as well as across multiple subjects. Moreover, we find that: (a) the areas that can predict activity of other voxels are consistent with previous results related to networks activated by the specific somatosensory task, as well as networks related to the default mode activity; (b) there is a global dynamical state dominated by two prominent (although not unique) streams, originating in the posterior parietal cortex and the posterior cingulate/precuneus cortex; (c) these streams span default mode and task-specific networks, and interact in several regions, notably the insula; and (d) the posterior cingulate is a central node of the default mode network, in terms of its ability to determine the future evolution of the rest of the nodes.

Functional networks for cognitive control in a stop signal task: independent component analysis

Cognitive control is a critical executive function of the human brain. Many studies have combined general linear modeling and the stop signal task (SST) to delineate the component processes of cognitive control. For instance, by contrasting stop success (SS) and stop error (SE) trials in the SST, investigators examined the neural processes underlying stop signal inhibition (SS > SE) and error processing (SE > SS). To complement this parameterized approach, here, we employed a data-driven method--independent component analysis (ICA)--to elucidate neural networks and the relationship between neural networks subserving cognitive control. In 59 adults performing the SST during fMRI, we characterized six independent components with ICA. These functional networks, temporally sorted for go success, SS, and SE trials as the events of interest, included a motor cortical network for motor preparation and execution; a right fronto-parietal network for attentional monitoring; a left fronto-parietal network for response inhibition; a midline cortico-subcortical network for error processing; a cuneus-precuneus network for behavioral engagement; and a "default" network for self-referential processing. Across subjects the event-associated weights of these functional networks showed a distinct pattern of correlation. These results provide new insight into the component processes of cognitive control.

Brain mediators of predictive cue effects on perceived pain

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for barely painful or highly painful thermal stimulation, and we assessed how cues influenced human subjects' pain reports and brain responses to matched levels of noxious heat using functional magnetic resonance imaging. We used multilevel mediation analysis to identify brain regions that (1) are modulated by predictive cues, (2) predict trial-to-trial variations in pain reports, and (3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pretask expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing.