Gopikrishna Deshpande

Multivariate Granger causality analysis of fMRI data.

This article describes the combination of multivariate Granger causality analysis, temporal down‐sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch‐to‐epoch changes in a hand‐gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network. Hum Brain Mapp, 2009.

Effect of hemodynamic variability on Granger causality analysis of fMRI.

In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger causality (GC) analysis of functional magnetic resonance imaging (fMRI) data to neuronal causal influences. We simulated fMRI data by convolving a standard canonical hemodynamic response function (HRF) with local field potentials (LFPs) acquired from the macaque cortex and manipulated the causal influence and neuronal delays between the LFPs, the hemodynamic delays between the HRFs, the signal-to-noise ratio (SNR), and the sampling period (TR) to assess the effect of each of these factors on the detectability of the neuronal delays from GC analysis of fMRI. In our first bivariate implementation, we assumed the worst-case scenario of the hemodynamic delay being at the empirical upper limit of its normal physiological range and opposing the direction of neuronal delay. We found that, in the absence of HRF confounds, even tens of milliseconds of neuronal delays can be inferred from fMRI. However, in the presence of HRF delays which opposed neuronal delays, the minimum detectable neuronal delay was hundreds of milliseconds. In our second multivariate simulation, we mimicked the real situation more closely by using a multivariate network of four time series and assumed the hemodynamic and neuronal delays to be unknown and drawn from a uniform random distribution. The resulting accuracy of detecting the correct multivariate network from fMRI was well above chance and was up to 90% with faster sampling. Generically, under all conditions, faster sampling and low measurement noise improved the sensitivity of GC analysis of fMRI data to neuronal causality.

Effective Connectivity During Haptic Perception: A Study Using Granger Causality Analysis Of Functional Magnetic Resonance Imaging Data

Although it is accepted that visual cortical areas are recruited during touch, it remains uncertain whether this depends on top-down inputs mediating visual imagery or engagement of modality-independent representations by bottom-up somatosensory inputs. Here we addressed this by examining effective connectivity in humans during haptic perception of shape and texture with the right hand. Multivariate Granger causality analysis of functional magnetic resonance imaging (fMRI) data was conducted on a network of regions that were shape- or texture-selective. A novel network reduction procedure was employed to eliminate connections that did not contribute significantly to overall connectivity. Effective connectivity during haptic perception was found to involve a variety of interactions between areas generally regarded as somatosensory, multisensory, visual and motor, emphasizing flexible cooperation between different brain regions rather than rigid functional separation. The left postcentral sulcus (PCS), left precentral gyrus and right posterior insula were important sources of connections in the network. Bottom-up somatosensory inputs from the left PCS and right posterior insula fed into visual cortical areas, both the shape-selective right lateral occipital complex (LOC) and the texture-selective right medial occipital cortex (probable V2). In addition, top-down inputs from left postero-supero-medial parietal cortex influenced the right LOC. Thus, there is strong evidence for the bottom-up somatosensory inputs predicted by models of visual cortical areas as multisensory processors and suggestive evidence for top-down parietal (but not prefrontal) inputs that could mediate visual imagery. This is consistent with modality-independent representations accessible through both bottom-up sensory inputs and top-down processes such as visual imagery.

Art for Reward’s Sake: Visual Art Recruits the Ventral Striatum

A recent study showed that people evaluate products more positively when they are physically associated with art images than similar non-art images. Neuroimaging studies of visual art have investigated artistic style and esthetic preference but not brain responses attributable specifically to the artistic status of images. Here we tested the hypothesis that the artistic status of images engages reward circuitry, using event-related functional magnetic resonance imaging (fMRI) during viewing of art and non-art images matched for content. Subjects made animacy judgments in response to each image. Relative to non-art images, art images activated, on both subject- and item-wise analyses, reward-related regions: the ventral striatum, hypothalamus and orbitofrontal cortex. Neither response times nor ratings of familiarity or esthetic preference for art images correlated significantly with activity that was selective for art images, suggesting that these variables were not responsible for the art-selective activations. Investigation of effective connectivity, using time-varying, wavelet-based, correlation-purged Granger causality analyses, further showed that the ventral striatum was driven by visual cortical regions when viewing art images but not non-art images, and was not driven by regions that correlated with esthetic preference for either art or non-art images. These findings are consistent with our hypothesis, leading us to propose that the appeal of visual art involves activation of reward circuitry based on artistic status alone and independently of its hedonic value.

Instantaneous and Causal Connectivity in Resting State Brain Networks Derived from Functional MRI Data

BACKGROUND Most neuroimaging studies of resting state networks have concentrated on functional connectivity (FC) based on instantaneous correlation in a single network. In this study we investigated both FC and effective connectivity (EC) based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data - default mode network (DMN), hippocampal cortical memory network (HCMN), dorsal attention network (DAN) and fronto-parietal control network (FPCN). METHODOLOGY/PRINCIPLE FINDINGS A method called correlation-purged Granger causality analysis was used, not only enabling the simultaneous evaluation of FC and EC of all networks using a single multivariate model, but also accounting for the interaction between them resulting from the smoothing of neuronal activity by hemodynamics. FC was visualized using a force-directed layout upon which causal interactions were overlaid. FC results revealed that DAN is very tightly coupled compared to the other networks while the DMN forms the backbone around which the other networks amalgamate. The pattern of bidirectional causal interactions indicates that posterior cingulate and posterior inferior parietal lobule of DMN act as major hubs. The pattern of unidirectional causal paths revealed that hippocampus and anterior prefrontal cortex (aPFC) receive major inputs, likely reflecting memory encoding/retrieval and cognitive integration, respectively. Major outputs emanating from anterior insula and middle temporal area, which are directed at aPFC, may carry information about interoceptive awareness and external environment, respectively, into aPFC for integration, supporting the hypothesis that aPFC-seeded FPCN acts as a control network. CONCLUSIONS/SIGNIFICANCE Our findings indicate the following. First, regions whose activities are not synchronized interact via time-delayed causal influences. Second, the causal interactions are organized such that cingulo-parietal regions act as hubs. Finally, segregation of different resting state networks is not clear cut but only by soft boundaries.

Activation and Effective Connectivity Changes Following Explicit-Memory Training for Face–Name Pairs in Patients With Mild Cognitive Impairment A Pilot Study

Background. Mild cognitive impairment (MCI) is often a precursor to Alzheimer disease. Little research has examined the efficacy of cognitive rehabilitation in patients with MCI, and the relevant neural mechanisms have not been explored. The authors previously showed the behavioral efficacy of cognitive rehabilitation using mnemonic strategies for face–name associations in patients with MCI. Here, the authors used functional magnetic resonance imaging (fMRI) to test whether there were training-specific changes in activation and connectivity within memory-related areas. Methods. A total of 6 patients with amnestic, multidomain MCI underwent pretraining and posttraining fMRI scans, during which they encoded 90 novel face–name pairs and completed a 4-choice recognition memory test immediately after scanning. Patients were taught mnemonic strategies for half the face–name pairs during 3 intervening training sessions. Results. Training-specific effects comprised significantly increased activation within a widespread cerebral cortical network involving medial frontal, parietal, and occipital regions; the left frontal operculum and angular gyrus; and regions in the left lateral temporal cortex. Increased activation common to trained and untrained stimuli was found in a separate network involving inferior frontal, lateral parietal, and occipital cortical regions. Effective connectivity analysis using multivariate, correlation-purged Granger causality analysis revealed generally increased connectivity after training, particularly involving the middle temporal gyrus and foci in the occipital cortex and the precuneus. Conclusion. The authors’ findings suggest that the effectiveness of explicit-memory training in patients with MCI is associated with training-specific increases in activation and connectivity in a distributed neural system that includes areas involved in explicit memory.

Object Familiarity Modulates Effective Connectivity During Haptic Shape Perception

In the preceding paper (Lacey, S., Flueckiger, P., Stilla, R., Lava, M., Sathian, K., 2009a. Object familiarity modulates involvement of visual imagery in haptic shape perception), we showed that the activations evoked by visual imagery overlapped more extensively, and their magnitudes were more correlated, with those evoked during haptic shape perception of familiar, compared to unfamiliar, objects. Here we used task-specific analyses of functional and effective connectivity to provide convergent evidence. These analyses showed that the visual imagery and familiar haptic shape tasks activated similar networks, whereas the unfamiliar haptic shape task activated a different network. Multivariate Granger causality analyses of effective connectivity, in both a conventional form and one purged of zero-lag correlations, showed that the visual imagery and familiar haptic shape networks involved top-down paths from prefrontal cortex into the lateral occipital complex (LOC), whereas the unfamiliar haptic shape network was characterized by bottom-up, somatosensory inputs into the LOC. We conclude that shape representations in the LOC are flexibly accessible, either top-down or bottom-up, according to task demands, and that visual imagery is more involved in LOC activation during haptic shape perception when objects are familiar, compared to unfamiliar.

DUAL PATHWAYS FOR HAPTIC AND VISUAL PERCEPTION OF SPATIAL AND TEXTURE INFORMATION

Segregation of information flow along a dorsally directed pathway for processing object location and a ventrally directed pathway for processing object identity is well established in the visual and auditory systems, but is less clear in the somatosensory system. We hypothesized that segregation of location vs. identity information in touch would be evident if texture is the relevant property for stimulus identity, given the salience of texture for touch. Here, we used functional magnetic resonance imaging (fMRI) to investigate whether the pathways for haptic and visual processing of location and texture are segregated, and the extent of bisensory convergence. Haptic texture-selectivity was found in the parietal operculum and posterior visual cortex bilaterally, and in parts of left inferior frontal cortex. There was bisensory texture-selectivity at some of these sites in posterior visual and left inferior frontal cortex. Connectivity analyses demonstrated, in each modality, flow of information from unisensory non-selective areas to modality-specific texture-selective areas and further to bisensory texture-selective areas. Location-selectivity was mostly bisensory, occurring in dorsal areas, including the frontal eye fields and multiple regions around the intraparietal sulcus bilaterally. Many of these regions received input from unisensory areas in both modalities. Together with earlier studies, the activation and connectivity analyses of the present study establish that somatosensory processing flows into segregated pathways for location and object identity information. The location-selective somatosensory pathway converges with its visual counterpart in dorsal frontoparietal cortex, while the texture-selective somatosensory pathway runs through the parietal operculum before converging with its visual counterpart in visual and frontal cortex. Both segregation of sensory processing according to object property and multisensory convergence appear to be universal organizing principles.

Investigating Effective Brain Connectivity from fMRI Data: Past Findings and Current Issues with Reference to Granger Causality Analysis

Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of Granger causality analysis. Finally, we discuss our previous work on the less-appreciated interactions between instantaneous and causal relationships and the utility and interpretation of Granger causality results obtained from task versus resting state (e.g., ability of causal relationships to provide a mode of connectivity between regions that are instantaneously dissociated in resting state). We conclude by discussing future directions in this area.

Posteromedial Parietal Cortical Activity and Inputs Predict Tactile Spatial Acuity

We used functional magnetic resonance imaging (fMRI) to investigate the neural circuitry underlying tactile spatial acuity at the human finger pad. Stimuli were linear, three-dot arrays, applied to the immobilized right index finger pad using a computer-controlled, MRI-compatible, pneumatic stimulator. Activity specific for spatial processing was isolated by contrasting discrimination of left–right offsets of the central dot in the array with discrimination of the duration of stimulation by an array without a spatial offset. This contrast revealed activity in a distributed frontoparietal cortical network, within which the levels of activity in right posteromedial parietal cortical foci [right posterior intraparietal sulcus (pIPS) and right precuneus] significantly predicted individual acuity thresholds. Connectivity patterns were assessed using both bivariate analysis of Granger causality with the right pIPS as a reference region and multivariate analysis of Granger causality for a selected set of regions. The strength of inputs into the right pIPS was significantly greater in subjects with better acuity than those with poorer acuity. In the better group, the paths predicting acuity converged from the left postcentral sulcus and right frontal eye field onto the right pIPS and were selective for the spatial task, and their weights predicted the level of right pIPS activity. We propose that the optimal strategy for fine tactile spatial discrimination involves interaction in the pIPS of a top-down control signal, possibly attentional, with somatosensory cortical inputs, reflecting either visualization of the spatial configurations of tactile stimuli or engagement of modality-independent circuits specialized for fine spatial processing.