Comparison of hemodynamic response nonlinearity across primary cortical areas

Weak Task Synchronization of Default Mode Network in Task Based Paradigms

The brains Default mode network (DMN) is generally characterized by brain areas that gets deactivated upon the presentation of a wide variety of externally focused, attention demanding tasks. These areas also exhibit significant intra-DMN functional connectivity and significant negative functional connectivity with other brain areas, especially with attention networks, in both resting state and task conditions. Therefore, the DMN has been hypothesized to be involved in internally directed cognitive activities such as autobiographical recall of the past, future planning and mind wandering. Recent research has discovered the role of bottom-up attention in modulating the behaviour of DMN. We hypothesize that the de-engagement of the DMN regions upon the presentation of an externally-focused attention-demanding stimulus may not be strictly stimulus locked and may exhibit significant trial-to-trial as well as subject-to-subject variability. Due to the involvement of frontoparietal control network in modulating the anticorrelations between DMN and dorsal attention network (DAN), we expect the DMN regions to have lower inter-trial and inter-subject synchronization in their fMRI BOLD responses as compared to the bottom-up early-sensory task-positive regions. To test this hypothesis, we designed new statistical methods called Inter Trial Temporal Synchronization Analysis (IT-TSA) and Inter Subject TSA (IS-TSA) to analyse variability across trials and subjects respectively. We analysed four publicly available datasets (total 223 subjects) across seven tasks related to different cognitive modalities and found out that there is significantly low stimulus-locked synchronization across trials and subjects in the DMN regions as compared to early sensory task positive regions. Our study challenges the understanding of DMN as a strictly task-negative region and supports the recent findings that DMN acts as an active component associated with intrinsic processing which deactivates differentially and non-linearly across trials and subjects in the presence of extrinsic processes.

Most favorable stimulation duration in the sensorimotor cortex for fNIRS-based BCI

One of the primary objectives of the brain-computer interface (BCI) is to obtain a command with higher classification accuracy within the shortest possible time duration. Therefore, this study evaluates several stimulation durations to propose a duration that can yield the highest classification accuracy. Furthermore, this study aims to address the inherent delay in the hemodynamic responses (HRs) for the command generation time. To this end, HRs in the sensorimotor cortex were evaluated for the functional near-infrared spectroscopy (fNIRS)-based BCI. To evoke brain activity, right-hand-index finger poking and tapping tasks were used. In this study, six different stimulation durations (i.e., 1, 3, 5, 7, 10, and 15 s) were tested on 10 healthy male subjects. Upon stimulation, different temporal features and multiple time windows were utilized to extract temporal features. The extracted features were then classified using linear discriminant analysis. The classification results using the main HR showed that a 5 s stimulation duration could yield the highest classification accuracy, i.e., 74%, with a combination of the mean and maximum value features. However, the results were not significantly different from the classification accuracy obtained using the 15 s stimulation. To further validate the results, a classification using the initial dip was performed. The results obtained endorsed the finding with an average classification accuracy of 73.5% using the features of minimum peak and skewness in the 5 s window. The results based on classification using the initial dip for 5 s were significantly different from all other tested stimulation durations (p < 0.05) for all feature combinations. Moreover, from the visual inspection of the HRs, it is observed that the initial dip occurred as soon as the task started, but the main HR had a delay of more than 2 s. Another interesting finding is that impulsive stimulation in the sensorimotor cortex can result in the generation of a clearer initial dip phenomenon. The results reveal that the command for the fNIRS-based BCI can be generated using the 5 s stimulation duration. In conclusion, the use of the initial dip can reduce the time taken for the generation of commands and can be used to achieve a higher classification accuracy for the fNIRS-BCI within a 5 s task duration rather than relying on longer durations.

Voxel-Wise Linearity Analysis of Increments and Decrements in BOLD Responses in Human Visual Cortex Using a Contrast Adaptation Paradigm

The linearity of BOLD responses is a fundamental presumption in most analysis procedures for BOLD fMRI studies. Previous studies have examined the linearity of BOLD signal increments, but less is known about the linearity of BOLD signal decrements. The present study assessed the linearity of both BOLD signal increments and decrements in the human primary visual cortex using a contrast adaptation paradigm. Results showed that both BOLD signal increments and decrements kept linearity to long stimuli (e.g., 3 s, 6 s), yet, deviated from linearity to transient stimuli (e.g., 1 s). Furthermore, a voxel-wise analysis showed that the deviation patterns were different for BOLD signal increments and decrements: while the BOLD signal increments demonstrated a consistent overestimation pattern, the patterns for BOLD signal decrements varied from overestimation to underestimation. Our results suggested that corrections to deviations from linearity of transient responses should consider the different effects of BOLD signal increments and decrements.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Analysis methods for measuring passive auditory fNIRS responses generated by a block-design paradigm

Significance: Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool in auditory research, but the range of analysis procedures employed across studies may complicate the interpretation of data. Aim: We aim to assess the impact of different analysis procedures on the morphology, detection, and lateralization of auditory responses in fNIRS. Specifically, we determine whether averaging or generalized linear model (GLM)-based analysis generates different experimental conclusions when applied to a block-protocol design. The impact of parameter selection of GLMs on detecting auditory-evoked responses was also quantified. Approach: 17 listeners were exposed to three commonly employed auditory stimuli: noise, speech, and silence. A block design, comprising sounds of 5 s duration and 10 to 20 s silent intervals, was employed. Results: Both analysis procedures generated similar response morphologies and amplitude estimates, and both indicated that responses to speech were significantly greater than to noise or silence. Neither approach indicated a significant effect of brain hemisphere on responses to speech. Methods to correct for systemic hemodynamic responses using short channels improved detection at the individual level. Conclusions: Consistent with theoretical considerations, simulations, and other experimental domains, GLM and averaging analyses generate the same group-level experimental conclusions. We release this dataset publicly for use in future development and optimization of algorithms.

Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Comparing hand movement rate dependence of cerebral blood volume and BOLD responses at 7T

Functional magnetic resonance imaging (fMRI) based on the Blood Oxygenation Level Dependent (BOLD) contrast takes advantage of the coupling between neuronal activity and the hemodynamics to allow a non-invasive localisation of the neuronal activity. In general, fMRI experiments assume a linear relationship between neuronal activation and the observed hemodynamics. However, the relationship between BOLD responses, neuronal activity, and behaviour are often nonlinear. In addition, the nonlinearity between BOLD responses and behaviour may be related to neuronal process rather than a neurovascular uncoupling. Further, part of the nonlinearity may be driven by vascular nonlinearity effects in particular from large vessel contributions. fMRI based on cerebral blood volume (CBV), promises a higher microvascular specificity, potentially without vascular nonlinearity effects and reduced contamination of the large draining vessels compared to BOLD. In this study, we aimed to investigate differences in BOLD and VASO-CBV signal changes during a hand movement task over a broad range of movement rates. We used a double readout 3D-EPI sequence at 7T to simultaneously measure VASO-CBV and BOLD responses in the sensorimotor cortex. The measured BOLD and VASO-CBV responses increased very similarly in a nonlinear fashion, plateauing for movement rates larger than 1 Hz. Our findings show a tight relationship between BOLD and VASO-CBV responses, indicating that the overall interplay of CBV and BOLD responses are similar for the assessed range of movement rates. These results suggest that the observed nonlinearity of neuronal origin is already present in VASO-CBV measurements, and consequently shows relatively unchanged BOLD responses.

Hemodynamic response varies across tactile stimuli with different temporal structures

Tactile stimuli can be distinguished based on their temporal features (e.g., duration, local frequency, and number of pulses), which are fundamental for vibrotactile frequency perception. Characterizing how the hemodynamic response changes in shape across experimental conditions is important for designing and interpreting fMRI studies on tactile information processing. In this study, we focused on periodic tactile stimuli with different temporal structures and explored the hemodynamic response function (HRF) induced by these stimuli. We found that HRFs were stimulus‐dependent in tactile‐related brain areas. Continuous stimuli induced a greater area of activation and a stronger and narrower hemodynamic response than intermittent stimuli with the same duration. The magnitude of the HRF increased with increasing stimulus duration. By normalizing the characteristics into topographic matrix, nonlinearity was obvious. These results suggested that stimulation patterns and duration within a cycle may be key characters for distinguishing different stimuli. We conclude that different temporal structures of tactile stimuli induced different HRFs, which are essential for vibrotactile perception and should be considered in fMRI experimental designs and analyses.

Linear systems analysis for laminar fMRI: Evaluating BOLD amplitude scaling for luminance contrast manipulations

A fundamental assumption of nearly all functional magnetic resonance imaging (fMRI) analyses is that the relationship between local neuronal activity and the blood oxygenation level dependent (BOLD) signal can be described as following linear systems theory. With the advent of ultra-high field (7T and higher) MRI scanners, it has become possible to perform sub-millimeter resolution fMRI in humans. A novel and promising application of sub-millimeter fMRI is measuring responses across cortical depth, i.e. laminar imaging. However, the cortical vasculature and associated directional blood pooling towards the pial surface strongly influence the cortical depth-dependent BOLD signal, particularly for gradient-echo BOLD. This directional pooling may potentially affect BOLD linearity across cortical depth. Here we assess whether the amplitude scaling assumption for linear systems theory holds across cortical depth. For this, we use stimuli with different luminance contrasts to elicit different BOLD response amplitudes. We find that BOLD amplitude across cortical depth scales with luminance contrast, and that this scaling is identical across cortical depth. Although nonlinearities may be present for different stimulus configurations and acquisition protocols, our results suggest that the amplitude scaling assumption for linear systems theory across cortical depth holds for luminance contrast manipulations in sub-millimeter laminar BOLD fMRI.

Measuring Cortical Activity During Auditory Processing with Functional Near-Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) is an optical, non-invasive neuroimaging technique that investigates human brain activity by calculating concentrations of oxy- and deoxyhemoglobin. The aim of this publication is to review the current state of the art as to how fNIRS has been used to study auditory function. We address temporal and spatial characteristics of the hemodynamic response to auditory stimulation as well as experimental factors that affect fNIRS data such as acoustic and stimulus-driven effects. The rising importance that fNIRS is generating in auditory neuroscience underlines the strong potential of the technology, and it seems likely that fNIRS will become a useful clinical tool.

Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer's Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis

Alzheimer's disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales. In this research, we applied MSE analysis to investigate the abnormal complexity of BOLD signals using the rs-fMRI data from the Alzheimer's disease neuroimaging initiative (ADNI) database. There were 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients. Following preprocessing of the BOLD signals, whole-brain MSE maps across six time scales were generated using the Complexity Toolbox. One-way analysis of variance (ANOVA) analysis on the MSE maps of four groups revealed significant differences in the thalamus, insula, lingual gyrus and inferior occipital gyrus, superior frontal gyrus and olfactory cortex, supramarginal gyrus, superior temporal gyrus, and middle temporal gyrus on multiple time scales. Compared with the NC group, MCI and AD patients had significant reductions in the complexity of BOLD signals and AD patients demonstrated lower complexity than that of the MCI subjects. Additionally, the complexity of BOLD signals from the regions of interest (ROIs) was found to be significantly associated with cognitive decline in patient groups on multiple time scales. Consequently, the complexity or MSE of BOLD signals may provide an imaging biomarker of cognitive impairments in MCI and AD.

Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex

The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain–computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75–135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.

Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI

Recent developments in fMRI acquisition techniques now enable fast sampling with whole-brain coverage, suggesting fMRI can be used to track changes in neural activity at increasingly rapid timescales. When images are acquired at fast rates, the limiting factor for fMRI temporal resolution is the speed of the hemodynamic response. Given that HRFs may vary substantially in subcortical structures, characterizing the speed of subcortical hemodynamic responses, and how the hemodynamic response shape changes with stimulus duration (i.e. the hemodynamic nonlinearity), is needed for designing and interpreting fast fMRI studies of these regions. We studied the temporal properties and nonlinearities of the hemodynamic response function (HRF) across the human subcortical visual system, imaging superior colliculus (SC), lateral geniculate nucleus of the thalamus (LGN) and primary visual cortex (V1) with high spatiotemporal resolution 7 Tesla fMRI. By presenting stimuli of varying durations, we mapped the timing and nonlinearity of hemodynamic responses in these structures at high spatiotemporal resolution. We found that the hemodynamic response is consistently faster and narrower in subcortical structures than in cortex. However, the nonlinearity in LGN is similar to that in cortex, with shorter duration stimuli eliciting larger and faster responses than would have been predicted by a linear model. Using oscillatory visual stimuli, we tested the frequency response in LGN and found that its BOLD response tracked high-frequency (0.5 Hz) oscillations. The LGN response magnitudes were comparable to V1, allowing oscillatory BOLD signals to be detected in LGN despite the small size of this structure. These results suggest that the increase in the speed and amplitude of the hemodynamic response when neural activity is brief may be the key physiological driver of fast fMRI signals, enabling detection of high-frequency oscillations with fMRI. We conclude that subcortical visual structures exhibit fast and nonlinear hemodynamic responses, and that these dynamics enable detection of fast BOLD signals even within small deep brain structures when imaging is performed at ultra-high field.

Multivariate Granger causality unveils directed parietal to prefrontal cortex connectivity during task-free MRI

While a large body of research has focused on the study of functional brain "connectivity", few investigators have focused on directionality of brain-brain interactions which, in spite of the mostly bidirectional anatomical substrates, cannot be assumed to be symmetrical. We employ a multivariate Granger Causality-based approach to estimating directed in-network interactions and quantify its advantages using extensive realistic synthetic BOLD data simulations to match Human Connectome Project (HCP) data specification. We then apply our framework to resting state functional MRI (rs-fMRI) data provided by the HCP to estimate the directed connectome of the human brain. We show that the functional interactions between parietal and prefrontal cortices commonly observed in rs-fMRI studies are not symmetrical, but consists of directional connectivity from parietal areas to prefrontal cortices rather than vice versa. These effects are localized within the same hemisphere and do not generalize to cross-hemispheric functional interactions. Our data are consistent with neurophysiological evidence that posterior parietal cortices involved in processing and integration of multi-sensory information modulate the function of more anterior prefrontal regions implicated in action control and goal-directed behaviour. The directionality of functional connectivity can provide an additional layer of information in interpreting rs-fMRI studies both in health and disease.

The hemodynamic signal as a first-order low-pass temporal filter: Evidence and implications for neuroimaging studies

Neuronal activation triggers local changes in blood flow and hemoglobin oxygenation. These hemodynamic signals can be recorded through functional magnetic resonance imaging or intrinsic optical imaging, and allows inferring neural activity in response to stimuli. These techniques are widely used to uncover functional brain architectures. However, their accuracy suffers from distortions inherent to hemodynamic responses and noise. The analysis of these signals currently relies on models of impulse hemodynamic responses to brief stimuli. Here, in order to infer precise functional architectures, we focused on integrated signals associated to the dynamic response of functional maps. To this end, we recorded orientation and direction maps in cat primary visual cortex and compared two protocols: the conventional episodic stimulation technique and a continuous, periodic stimulation paradigm. Conventional methods show that the dynamics of activation and deactivation of the functional maps follows a linear first-order differential equation representing a low-pass filter. Comparison with the periodic stimulation methods confirmed this observation: the phase shifts and magnitude attenuations extracted at various frequencies were consistent with a low-pass filter with a 5s time constant. This dynamics presumably reflects the variations in deoxyhemoglobin mediated by arterial dilations. This dynamics open new avenues in the analysis of neuroimaging data that differs from common methods based on the hemodynamic response function. In particular, we demonstrate that inverting this first-order low-pass filter minimized the distortions of the signal and enabled a much faster and accurate reconstruction of functional maps.

Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting central modulation of ANS outflow.

Population receptive field estimates of human auditory cortex

Here we describe a method for measuring tonotopic maps and estimating bandwidth for voxels in human primary auditory cortex (PAC) using a modification of the population Receptive Field (pRF) model, developed for retinotopic mapping in visual cortex by Dumoulin and Wandell (2008). The pRF method reliably estimates tonotopic maps in the presence of acoustic scanner noise, and has two advantages over phase-encoding techniques. First, the stimulus design is flexible and need not be a frequency progression, thereby reducing biases due to habituation, expectation, and estimation artifacts, as well as reducing the effects of spatio-temporal BOLD nonlinearities. Second, the pRF method can provide estimates of bandwidth as a function of frequency. We find that bandwidth estimates are narrower for voxels within the PAC than in surrounding auditory responsive regions (non-PAC).

A semi-parametric nonlinear model for event-related fMRI

Nonlinearity in evoked hemodynamic responses often presents in event-related fMRI studies. Volterra series, a higher-order extension of linear convolution, has been used in the literature to construct a nonlinear characterization of hemodynamic responses. Estimation of the Volterra kernel coefficients in these models is usually challenging due to the large number of parameters. We propose a new semi-parametric model based on Volterra series for the hemodynamic responses that greatly reduces the number of parameters and enables "information borrowing" among subjects. This model assumes that in the same brain region and under the same stimulus, the hemodynamic responses across subjects share a common but unknown functional shape that can differ in magnitude, latency and degree of interaction. We develop a computationally-efficient strategy based on splines to estimate the model parameters, and a hypothesis test on nonlinearity. The proposed method is compared with several existing methods via extensive simulations, and is applied to a real event-related fMRI study.

An investigation of positive and inverted hemodynamic response functions across multiple visual areas

Recent studies have demonstrated significant regional variability in the hemodynamic response function (HRF), highlighting the difficulty of correctly interpreting functional MRI (fMRI) data without proper modeling of the HRF. The focus of this study was to investigate the HRF variability within visual cortex. The HRF was estimated for a number of cortical visual areas by deconvolution of fMRI blood oxygenation level dependent (BOLD) responses to brief, large-field visual stimulation. Significant HRF variation was found across visual areas V1, V2, V3, V4, VO-1,2, V3AB, IPS-0,1,2,3, LO-1,2, and TO-1,2. Additionally, a subpopulation of voxels was identified that exhibited an impulse response waveform that was similar, but not identical, to an inverted version of the commonly described and modeled positive HRF. These voxels were found within the retinotopic confines of the stimulus and were intermixed with those showing positive responses. The spatial distribution and variability of these HRFs suggest a vascular origin for the inverted waveforms. We suggest that the polarity of the HRF is a separate factor that is independent of the suppressive or activating nature of the underlying neuronal activity. Correctly modeling the polarity of the HRF allows one to recover an estimate of the underlying neuronal activity rather than discard the responses from these voxels on the assumption that they are artifactual. We demonstrate this approach on phase-encoded retinotopic mapping data as an example of the benefits of accurately modeling the HRF during the analysis of fMRI data.

Cortical depth dependence of the BOLD initial dip and poststimulus undershoot in human visual cortex at 7 Tesla

PURPOSE

Owing to variability in vascular dynamics across cerebral cortex, blood-oxygenation-level-dependent (BOLD) spatial and temporal characteristics should vary as a function of cortical-depth. Here, the positive response, initial dip (ID), and post-stimulus undershoot (PSU) of the BOLD response in human visual cortex are investigated as a function of cortical depth and stimulus duration at 7 Tesla (T).

METHODS

Gradient-echo echo-planar-imaging BOLD fMRI with high spatial and temporal resolution was performed in 7 healthy volunteers and measurements of the ID, PSU, and positive BOLD response were made as a function of cortical depth and stimulus duration (0.5-8 s). Exploratory analyses were applied to understand whether functional mapping could be achieved using the ID, rather than positive, BOLD signal characteristics

RESULTS

The ID was largest in outer cortical layers, consistent with previously reported upstream propagation of vasodilation along the diving arterioles in animals. The positive BOLD signal and PSU showed different relationships across the cortical depth with respect to stimulus duration.

CONCLUSION

The ID and PSU were measured in humans at 7T and exhibited similar trends to those recently reported in animals. Furthermore, while evidence is provided for the ID being a potentially useful feature for better understanding BOLD signal dynamics, such as laminar neurovascular coupling, functional mapping based on the ID is extremely difficult.

BOLD consistently matches electrophysiology in human sensorimotor cortex at increasing movement rates: a combined 7T fMRI and ECoG study on neurovascular coupling

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to measure human brain function and relies on the assumption that hemodynamic changes mirror the underlying neuronal activity. However, an often reported saturation of the BOLD response at high movement rates has led to the notion of a mismatch in neurovascular coupling. We combined BOLD fMRI at 7T and intracranial electrocorticography (ECoG) to assess the relationship between BOLD and neuronal population activity in human sensorimotor cortex using a motor task with increasing movement rates. Though linear models failed to predict BOLD responses from the task, the measured BOLD and ECoG responses from the same tissue were in good agreement. Electrocorticography explained almost 80% of the mismatch between measured- and model-predicted BOLD responses, indicating that in human sensorimotor cortex, a large portion of the BOLD nonlinearity with respect to behavior (movement rate) is well predicted by electrophysiology. The results further suggest that other reported examples of BOLD mismatch may be related to neuronal processes, rather than to neurovascular uncoupling.

Perceptual experience modulates cortical circuits involved in visual awareness

Successful interactions with the environment entail interpreting ambiguous sensory information. To address this challenge it has been suggested that the brain optimizes performance through experience. Here we used functional magnetic resonance imaging (fMRI) to investigate whether perceptual experience modulates the cortical circuits involved in visual awareness. Using ambiguous visual stimuli (binocular rivalry or ambiguous structure-from-motion) we were able to disentangle the co-occurring influences of stimulus repetition and perceptual repetition. For both types of ambiguous stimuli we observed that the mere repetition of the stimulus evoked an entirely different pattern of activity modulations than the repetition of a particular perceptual interpretation of the stimulus. Regarding stimulus repetition, decreased fMRI responses were evident during binocular rivalry but weaker during 3-D motion rivalry. Perceptual repetition, on the other hand, entailed increased activity in stimulus-specific visual brain regions - for binocular rivalry in the early visual regions and for ambiguous structure-from-motion in both early as well as higher visual regions. This indicates that the repeated activation of a visual network mediating a particular percept facilitated its later reactivation. Perceptual repetition was also associated with a response change in the parietal cortex that was similar for the two types of ambiguous stimuli, possibly relating to the temporal integration of perceptual information. We suggest that perceptual repetition is associated with a facilitation of neural activity within and between percept-specific visual networks and parietal networks involved in the temporal integration of perceptual information, thereby enhancing the stability of previously experienced percepts.

Cortical neurovascular coupling driven by stimulation of channelrhodopsin-2

While functional imaging is widely used in studies of the brain, how well the hemodynamic signal represents the underlying neural activity is still unclear. And there is a debate on whether hemodynamic signal is more tightly related to synaptic activity or action potentials. This study intends to address these questions by examining neurovascular coupling driven by pyramidal cells in the motor cortex of rats. Pyramidal cells in the motor cortex of rats were selectively transduced with the light sensitive cation channel channelrhodopsin-2 (ChR2). Electrophysiological recordings and optical intrinsic signal imaging were performed simultaneously and synchronously to capture the neural activity and hemodynamics induced by optical stimulation of ChR2-expressing pyramidal cells. Our results indicate that both synaptic activity (local field potential, LFP) and action potentials (multi-unit activity, MUA) are tightly related to hemodynamic signals. While LFPs in γ band are better in predicting hemodynamic signals elicited by short stimuli, MUA has better predictions to hemodynamic signals elicited by long stimuli. Our results also indicate that strong nonlinearity exists in neurovascular coupling.

Spatial summation of neurometabolic coupling in the central visual pathway

Noninvasive neural imaging has become an important tool in both applied and theoretical applications. The hemodynamic properties that are measured in functional magnetic resonance imaging (fMRI), for example, are generally used to infer neuronal characteristics. In an attempt to provide empirical data to connect the hemodynamic measurements with neural function, we have conducted previous studies in which neural activity and tissue oxygen metabolic functions are determined together in co-localized regions of the central visual pathway. A basic question in this procedure is whether oxygen responses are coupled linearly in space and time with neural activity. We have previously examined temporal factors, and in the current study, spatial characteristics are addressed. We have recorded from neurons in the lateral geniculate nucleus (LGN) and striate cortex in anesthetized cats. In both structures, there is a classical receptive field (CRF) within which a neuron can be activated. There is also a region outside the CRF from which stimulation cannot activate the cell directly but can influence the response elicited from the CRF. In this investigation we have used several specific spatial stimulus patterns presented to either the CRF or the surrounding region or to both areas together in order to determine spatial response patterns. Within the CRF, we find that neural and metabolic responses sum in a nonlinear fashion but changes in these two measurements are closely coupled. For stimuli that extend beyond the CRF, neural activity is generally reduced while oxygen response exhibits uncoupled changes.

Transient and sustained components of the sensorimotor BOLD response in fMRI

Blood oxygenation level-dependent (BOLD) signal time courses in functional magnetic resonance imaging are estimated within the framework of general linear modeling by convolving an input function, that represents neural activity, with a canonical hemodynamic response function (HRF). Here we investigate the performance of different neural input functions and latency-optimized HRFs for modeling BOLD signals in response to vibrotactile somatosensory stimuli of variable durations (0.5, 1, 4, 7 s) in 14 young, healthy adults who were required to make button press responses at each stimulus cessation. Informed by electrophysiology and the behavioral task, three nested models with an increasing number of parameters were considered: a boxcar; boxcar and offset transient; and onset transient, boxcar and offset transient (TBT). The TBT model provided the best fit of the group-averaged BOLD time courses based on χ(2) and F statistics. Only the TBT model was capable of fitting the bimodal shape of the BOLD response to the 7-s stimulus and the relative peak amplitudes for all stimulus lengths in key somatosensory and motor areas. This suggests that the TBT model provides a more comprehensive description of brain sensorimotor responses in this experiment than provided by the simple boxcar model. Work comparing the activation maps obtained with the TBT model with magnetoencephalography data is under way.

Nonlinear hemodynamic responses in human epilepsy: a multimodal analysis with fNIRS-EEG and fMRI-EEG

Functional magnetic resonance imaging (fMRI) combined with electroencephalography (fMRI-EEG) is a neuroimaging technique based on the blood oxygenation level dependent (BOLD) signal which has been shown to be useful in the study of epilepsy for the localization of the epileptogenic focus. Functional near-infrared spectroscopy (fNIRS) combined with EEG (fNIRS-EEG) is another imaging technique based on the measurement of oxygenated and deoxygenated hemoglobin with complementary clinical potential in epilepsy, for continuous patient monitoring, language lateralization, and focus localization. In this work fMRI-EEG and fNIRS-EEG are used to quantify nonlinear hemodynamic responses in three cases of human refractory focal epilepsy, by using the Volterra kernel expansion up to second order. Prior to analyzing real data, extensive simulations are carried out to show that nonlinearities are estimable. The Volterra methodology is then applied to multimodal data recorded from 3 epileptic patients selected for their frequent spiking activity. Care is taken to account for variability of hemodynamic responses due to other causes than Volterra nonlinearities. Statistically significant nonlinearities are observed for all patients and all modalities. Good concordance between fNIRS and fMRI is found for both the amplitude of the Volterra responses, and, with limitations, in the localization of the epileptic focus and regions of inverted responses (negative BOLD signals). In one patient, Volterra nonlinearities allowed epileptic focus identification with fMRI, while analyses without nonlinearities failed to see it. In simulations when nonlinearities were included, analysis without Volterra nonlinearities performed poorly. These two observations suggest routinely checking for nonlinearities in functional imaging of patients presenting with frequent spikes.

Quantifying hemodynamic refractory bold effects in normal subjects at the single-subject level using an inverse logit fitting procedure

PURPOSE

To evaluate whether hemodynamic refractory effects provoked by repeated visual stimulation can be detected and quantified at the single-subject level using a recently described hemodynamic response function (HRF) fitting algorithm.

MATERIALS AND METHODS

Hemodynamic refractory effects were induced with an easily applicable functional MRI (fMRI) paradigm. A fitting method with inverse logit (IL) functions was applied to quantify net HRFs at the single-subject level with three interstimulus intervals (ISI; 1, 2, and 6 s). The model yielded amplitude, latencies, and width for each HRF.

RESULTS

HRF fitting was possible in 44 of 51 healthy volunteers, with excellent goodness-of-fit (R(2) = 0.9745 ± 0.0241). Refractory effects were most pronounced for the 1-s ISI (P < 0.001) and had nearly disappeared for the 6-s ISI.

CONCLUSION

Quantifying refractory effects in individuals was possible in 86.3% of normal subjects using the IL fitting algorithm. This setup may be suitable to explore such effects in individual patients.

Neurovascular coupling is brain region-dependent

Despite recent advances in alternative brain imaging technologies, functional magnetic resonance imaging (fMRI) remains the workhorse for both medical diagnosis and primary research. Indeed, the number of research articles that utilise fMRI have continued to rise unabated since its conception in 1991, despite the limitation that recorded signals originate from the cerebral vasculature rather than neural tissue. Consequently, understanding the relationship between brain activity and the resultant changes in metabolism and blood flow (neurovascular coupling) remains a vital area of research. In the past, technical constraints have restricted investigations of neurovascular coupling to cortical sites and have led to the assumption that coupling in non-cortical structures is the same as in the cortex, despite the lack of any evidence. The current study investigated neurovascular coupling in the rat using whole-brain blood oxygenation level-dependent (BOLD) fMRI and multi-channel electrophysiological recordings and measured the response to a sensory stimulus as it proceeded through brainstem, thalamic and cortical processing sites - the so-called whisker-to-barrel pathway. We found marked regional differences in the amplitude of BOLD activation in the pathway and non-linear neurovascular coupling relationships in non-cortical sites. The findings have important implications for studies that use functional brain imaging to investigate sub-cortical function and caution against the use of simple, linear mapping of imaging signals onto neural activity.

Variability comparison of simultaneous brain near-infrared spectroscopy and functional magnetic resonance imaging during visual stimulation

Brain near-infrared spectroscopy (NIRS) is emerging as a potential alternative to functional magnetic resonance imaging (fMRI). To date, no study has explicitly compared the two techniques in terms of measurement variability, a key parameter dictating attainable statistical power. Here, NIRS and fMRI were simultaneously recorded during event-related visual stimulation. Inter-subject coefficients of variation (CVs) for peak response amplitude were considerably larger for NIRS than fMRI, but inter-subject CVs for response latency and intra-subject CVs for response amplitude were overall comparable. Our results may represent an optimistic estimate of the CVs of NIRS measurements, as optode positioning was guided by structural MRI, which is normally unavailable. We concluded that fMRI may be preferable to NIRS for group comparisons, but NIRS is equally powerful when comparing conditions within participants. The discrepancy between inter- and intra-subject CVs is likely related to variability in head anatomy and tissue properties, which may be better accounted for by emerging NIRS technology.

Assessment of temporal state-dependent interactions between auditory fMRI responses to desired and undesired acoustic sources

A confounding factor in auditory functional magnetic resonance imaging (fMRI) experiments is the presence of the acoustic noise inherently associated with the echo planar imaging acquisition technique. Previous studies have demonstrated that this noise can induce unwanted neuronal responses that can mask stimulus-induced responses. Similarly, activation accumulated over multiple stimuli has been demonstrated to elevate the baseline, thus reducing the dynamic range available for subsequent responses. To best evaluate responses to auditory stimuli, it is necessary to account for the presence of all recent acoustic stimulation, beginning with an understanding of the attenuating effects brought about by interaction between and among induced unwanted neuronal responses, and responses to desired auditory stimuli. This study focuses on the characterization of the duration of this temporal memory and qualitative assessment of the associated response attenuation. Two experimental parameters--inter-stimulus interval (ISI) and repetition time (TR)--were varied during an fMRI experiment in which participants were asked to passively attend to an auditory stimulus. Results present evidence of a state-dependent interaction between induced responses. As expected, attenuating effects of these interactions become less significant as TR and ISI increase and in contrast to previous work, persist up to 18s after a stimulus presentation.

Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making.

Oscillatory response function: towards a parametric model of rhythmic brain activity

Rhythmic brain activity, measured by magnetoencephalography (MEG), is modulated during stimulation and task performance. Here, we introduce an oscillatory response function (ORF) to predict the dynamic suppression-rebound modulation of brain rhythms during a stimulus sequence. We derived a class of parametric models for the ORF in a generalized convolution framework. The model parameters were estimated from MEG data acquired from 10 subjects during bilateral tactile stimulation of fingers (stimulus rates of 4 Hz and 10 Hz in blocks of 0.5, 1, 2, and 4 s). The envelopes of the 17-23 Hz rhythmic activity, computed for sensors above the rolandic region, correlated 25%-43% better with the envelopes predicted by the models than by the stimulus time course (boxcar). A linear model with separate convolution kernels for onset and offset responses gave the best prediction. We studied the generalizability of this model with data from 5 different subjects during a separate bilateral tactile sequence by first identifying neural sources of the 17-23 Hz activity using cortically constrained minimum norm estimates. Both the model and the boxcar predicted strongest modulation in the primary motor cortex. For short-duration stimulus blocks, the model predicted the envelope of the cortical currents 20% better than the boxcar did. These results suggest that ORFs could concisely describe brain rhythms during different stimuli, tasks, and pathologies.

Modeling hemodynamic responses in auditory cortex at 1.5 T using variable duration imaging acoustic noise

A confound for functional magnetic resonance imaging (fMRI), especially for auditory studies, is the presence of imaging acoustic noise generated mainly as a byproduct of rapid gradient switching during volume acquisition and, to a lesser extent, the radiofrequency transmit. This work utilized a novel pulse sequence to present actual imaging acoustic noise for characterization of the induced hemodynamic responses and assessment of linearity in the primary auditory cortex with respect to noise duration. Results show that responses to brief duration (46 ms) imaging acoustic noise is highly nonlinear while responses to longer duration (>1 s) imaging acoustic noise becomes approximately linear, with the right primary auditory cortex exhibiting a higher degree of nonlinearity than the left for the investigated noise durations. This study also assessed the spatial extent of activation induced by imaging acoustic noise, showing that the use of modeled responses (specific to imaging acoustic noise) as the reference waveform revealed additional activations in the auditory cortex not observed with a canonical gamma variate reference waveform, suggesting an improvement in detection sensitivity for imaging acoustic noise-induced activity. Longer duration (1.5 s) imaging acoustic noise was observed to induce activity that expanded outwards from Heschl's gyrus to cover the superior temporal gyrus as well as parts of the middle temporal gyrus and insula, potentially affecting higher level acoustic processing.

Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting

The exact relationship between neural activity and BOLD fMRI is unknown. However, several recent findings, recorded invasively in both humans and monkeys, show a positive correlation of BOLD to high-frequency (30-150 Hz) oscillatory power changes and a negative correlation to low-frequency (8-30 Hz) power changes arising from cortical areas. In this study, we computed the time series correlation between BOLD GE-EPI fMRI at 7 T and neural activity measures from noninvasive MEG, using a time-frequency beam former for source localisation. A sinusoidal drifting grating was presented visually for 4 s followed by a 20 s rest period in both recording modalities. The MEG time series were convolved with either a measured or canonical haemodynamic response function (HRF) for comparison with the measured BOLD data, and the BOLD data were deconvolved with either a measured or a canonical HRF for comparison with the measured MEG. In the visual cortex, the higher frequencies (mid-gamma=52-75 Hz and high-gamma=75-98 Hz) were positively correlated with BOLD whilst the lower frequencies (alpha=8-12 Hz and beta=12-25 Hz) were negatively correlated with BOLD. Furthermore, regression including all frequency bands predicted BOLD better than stimulus timing alone, although no individual frequency band predicted BOLD as well as stimulus timing. For this paradigm, there was, in general, no difference between using the SPM canonical HRF compared to the subject-specific measured HRF. In conclusion, MEG replicates findings from invasive recordings with regard to time series correlations with BOLD data. Conversely, deconvolution of BOLD data provides a neural estimate which correlates well with measured neural effects as a function of neural oscillation frequency.

Investigation of the prefrontal cortex in response to duration-variable anagram tasks using functional near-infrared spectroscopy

We hypothesize that nonlinearity between short-term anagram tasks and corresponding hemodynamic responses can be observed by functional near-infrared spectroscopy (fNIRS) in the prefrontal cortex (PFC). The PFC of six human subjects in response to anagram tasks is investigated using multichannel fNIRS. Concentration changes of oxyhemoglobin and deoxyhemoglobin in the PFC are measured with variable anagram durations and at two difficulty levels (four- and six-letter anagrams). The durations to perform the selected anagram tasks range from several seconds to more than one minute. The dorsolateral PFC areas exhibit consistent and strong hemodynamic deactivation during and shortly after task execution. The superposition principle of a linear system is employed to investigate nonlinear hemodynamic features among three task duration subgroups: D1 = 2.0 sec, D2 = 4.0 sec, and D3 = 8.0 sec. Such analysis shows clear nonlinearity in hemodynamic responses on the PFC with task durations shorter than 4 sec. Our observation of significant deactivation in early hemodynamic responses in the PFC is consistent with multiple fNIRS studies and several reports given in the field of functional magnetic resonance imaging. A better understanding of nonlinearity in fNIRS signals will have potential for us to investigate brain adaptation and to extrapolate neuronal activities from hemodynamic signals.

Current trends in intraoperative optical imaging for functional brain mapping and delineation of lesions of language cortex

Resection of a cerebral arteriovenous malformation (AVM), epileptic focus, or glioma, ideally has a prerequisite of microscopic delineation of the lesion borders in relation to the normal gray and white matter that mediate critical functions. Currently, Wada testing and functional magnetic resonance imaging (fMRI) are used for preoperative mapping of critical function, whereas electrical stimulation mapping (ESM) is used for intraoperative mapping. For lesion delineation, MRI and positron emission tomography (PET) are used preoperatively, whereas microscopy and histological sectioning are used intraoperatively. However, for lesions near eloquent cortex, these imaging techniques may lack sufficient resolution to define the relationship between the lesion and language function, and thus not accurately determine which patients will benefit from neurosurgical resection of the lesion without iatrogenic aphasia. Optical techniques such as intraoperative optical imaging of intrinsic signals (iOIS) show great promise for the precise functional mapping of cortices, as well as delineation of the borders of AVMs, epileptic foci, and gliomas. Here we first review the physiology of neuroimaging, and then progress towards the validation and justification of using intraoperative optical techniques, especially in relation to neurosurgical planning of resection AVMs, epileptic foci, and gliomas near or in eloquent cortex. We conclude with a short description of potential novel intraoperative optical techniques.

Linearity of the fMRI response in category-selective regions of human visual cortex

The goal of this study was to determine the linearity of the blood oxygen level-dependent (BOLD) response, as measured by functional magnetic resonance imaging (fMRI), in category-selective regions of human visual cortex. We defined regions of the temporal lobe that were selective to faces (fusiform face area, FFA) and places (parahippocampal place area, PPA). We then determined the linearity of the BOLD response in these regions to their preferred and nonpreferred stimuli. First, we tested the principle of scaling. As we increased the visibility of the stimulus, there was a corresponding linear increase in the fMRI signal in the FFA and PPA to their preferred stimulus (face and place, respectively). In contrast, responses in the FFA and PPA to the nonpreferred stimulus did not conform to the principle of scaling. Next, we asked whether the fMRI response in these regions of visual cortex conformed to the principle of additivity. To assess this, we determined whether the response to a long stimulus block could be predicted by adding the response to multiple shorter duration blocks. Although the fMRI response in the FFA and PPA was generally linear to the preferred stimulus, a more nonlinear response was apparent to the nonpreferred stimulus. In conclusion, the linearity of the BOLD response in the human ventral visual pathway varied across cortical region and stimulus category. This suggests that measures of linearity may provide a useful indication of neural selectivity in the brain.

Oxidative neuroenergetics in event-related paradigms

Energetic basis of neural activity provides a solid foundation for noninvasive neuroimaging with calibrated functional magnetic resonance imaging (fMRI). Calculating dynamic changes in cerebral oxidative energy utilization (CMR(O(2))) is limited by uncertainties about whether or not the conventional blood oxygenation level-dependent (BOLD) model can be applied transiently using multimodal measurements of blood flow (CBF) and volume (CBV) that affect the BOLD signal. A prerequisite for dynamic calibrated fMRI is testing the linearity of multimodal signals within a temporal regimen, as assessed by signal strength (i.e., both intensity and width). If each hyperemic component (BOLD, CBV, CBF) is demonstrated to be linear with neural activity under various experimental conditions, then the respective transfer functions generated by deconvolution with neural activity should be time invariant and thus could potentially be used for calculating CMR(O(2)) transients. Hyperemic components were investigated at 11.7 T in alpha-chloralose-anesthetized rats and combined with electrophysiological recordings of local field potential (LFP) and multiunit activity (MUA) from the cortex during forepaw stimulation, in which stimulus number and frequency were varied. Although relationships between neural activity and stimulus features ranged from linear to nonlinear, associations between hyperemic components and neural activity were linear. Specific to each hyperemic component, a universal transfer function (with LFP or MUA) yielded predictions in agreement with experimental measurements. The results identified a component of the BOLD signal that can be attributed to significant changes in CMR(O(2)), even for temporal events separated by <200 ms.

Non-extensive entropy and the extraction of BOLD spatial information in event-related functional MRI

Functional magnetic resonance imaging (fMRI) data analysis has been carried out recently in the framework of information theory, by means of the Shannon entropy. As a natural extension, a method based on the generalized Tsallis entropy was developed to the analysis event-related (ER-fMRI), where a brief stimulus is presented, followed by a long period of rest. The new technique aims for spatial localization neuronal activity due to a specific task. This method does not require a priori hypothesis of the hemodynamic response function (HRF) shape and the linear relation between BOLD responses with the presented task. Numerical simulations were performed so as to determine the optimal values of the Tsallis q parameter and the number of levels, L. In order to avoid undesirable divergences of the Tsallis entropy, only positive q values were studied. Results from simulated data (with L = 3) indicated that, for q = 0.8, the active brain areas are detected with the highest performance. Moreover, the method was tested for an in vivo experiment and demonstrated the ability to discriminate active brain regions that selectively responded to a bilateral motor task.

Contributions of the medial temporal lobe to declarative memory retrieval: manipulating the amount of contextual retrieval

We investigated how the hippocampus and its adjacent mediotemporal structures contribute to contextual and noncontextual declarative memory retrieval by manipulating the amount of contextual information across two levels of the same contextual dimension in a source memory task. A first analysis identified medial temporal lobe (MTL) substructures mediating either contextual or noncontextual retrieval. A linearly weighted analysis elucidated which MTL substructures show a gradually increasing neural activity, depending on the amount of contextual information retrieved. A hippocampal engagement was found during both levels of source memory but not during item memory retrieval. The anterior MTL including the perirhinal cortex was only engaged during item memory retrieval by an activity decrease. Only the posterior parahippocampal cortex showed an activation increasing with the amount of contextual information retrieved. If one assumes a roughly linear relationship between the blood-oxygenation level-dependent (BOLD) signal and the associated cognitive process, our results suggest that the posterior parahippocampal cortex is involved in contextual retrieval on the basis of memory strength while the hippocampus processes representations of item-context binding. The anterior MTL including perirhinal cortex seems to be particularly engaged in familiarity-based item recognition. If one assumes departure from linearity, however, our results can also be explained by one-dimensional modulation of memory strength.