Mapping the connectivity with structural equation modeling in an fMRI study of shape-from-motion task

Human subcortical pathways automatically detect collision trajectory without attention and awareness

Detecting imminent collisions is essential for survival. Here, we used high-resolution fMRI at 7 Tesla to investigate the role of attention and consciousness for detecting collision trajectory in human subcortical pathways. Healthy participants can precisely discriminate collision from near-miss trajectory of an approaching object, with pupil size change reflecting collision sensitivity. Subcortical pathways from the superior colliculus (SC) to the ventromedial pulvinar (vmPul) and ventral tegmental area (VTA) exhibited collision-sensitive responses even when participants were not paying attention to the looming stimuli. For hemianopic patients with unilateral lesions of the geniculostriate pathway, the ipsilesional SC and VTA showed significant activation to collision stimuli in their scotoma. Furthermore, stronger SC responses predicted better behavioral performance in collision detection even in the absence of awareness. Therefore, human tectofugal pathways could automatically detect collision trajectories without the observers' attention to and awareness of looming stimuli, supporting "blindsight" detection of impending visual threats.

The expanding horizons of network neuroscience: From description to prediction and control

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.

Time-resolved effective connectivity in task fMRI: Psychophysiological interactions of Co-Activation patterns

Investigating context-dependent modulations of Functional Connectivity (FC) with functional magnetic resonance imaging is crucial to reveal the neurological underpinnings of cognitive processing. Most current analysis methods hypothesise sustained FC within the duration of a task, but this assumption has been shown too limiting by recent imaging studies. While several methods have been proposed to study functional dynamics during rest, task-based studies are yet to fully disentangle network modulations. Here, we propose a seed-based method to probe task-dependent modulations of brain activity by revealing Psychophysiological Interactions of Co-activation Patterns (PPI-CAPs). This point process-based approach temporally decomposes task-modulated connectivity into dynamic building blocks which cannot be captured by current methods, such as PPI or Dynamic Causal Modelling. Additionally, it identifies the occurrence of co-activation patterns at single frame resolution as opposed to window-based methods. In a naturalistic setting where participants watched a TV program, we retrieved several patterns of co-activation with a posterior cingulate cortex seed whose occurrence rates and polarity varied depending on the context; on the seed activity; or on an interaction between the two. Moreover, our method exposed the consistency in effective connectivity patterns across subjects and time, allowing us to uncover links between PPI-CAPs and specific stimuli contained in the video. Our study reveals that explicitly tracking connectivity pattern transients is paramount to advance our understanding of how different brain areas dynamically communicate when presented with a set of cues.

Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Dorsal and ventral stream contributions to form-from-motion perception in a patient with form-from motion deficit: a case report

The main model of visual processing in primates proposes an anatomo-functional distinction between the dorsal stream, specialized in spatio-temporal information, and the ventral stream, processing essentially form information. However, these two pathways also communicate to share much visual information. These dorso-ventral interactions have been studied using form-from-motion (FfM) stimuli, revealing that FfM perception first activates dorsal regions (e.g., MT+/V5), followed by successive activations of ventral regions (e.g., LOC). However, relatively little is known about the implications of focal brain damage of visual areas on these dorso-ventral interactions. In the present case report, we investigated the dynamics of dorsal and ventral activations related to FfM perception (using topographical ERP analysis and electrical source imaging) in a patient suffering from a deficit in FfM perception due to right extrastriate brain damage in the ventral stream. Despite the patient's FfM impairment, both successful (observed for the highest level of FfM signal) and absent/failed FfM perception evoked the same temporal sequence of three processing states observed previously in healthy subjects. During the first period, brain source localization revealed cortical activations along the dorsal stream, currently associated with preserved elementary motion processing. During the latter two periods, the patterns of activity differed from normal subjects: activations were observed in the ventral stream (as reported for normal subjects), but also in the dorsal pathway, with the strongest and most sustained activity localized in the parieto-occipital regions. On the other hand, absent/failed FfM perception was characterized by weaker brain activity, restricted to the more lateral regions. This study shows that in the present case report, successful FfM perception, while following the same temporal sequence of processing steps as in normal subjects, evoked different patterns of brain activity. By revealing a brain circuit involving the most rostral part of the dorsal pathway, this study provides further support for neuro-imaging studies and brain lesion investigations that have suggested the existence of different brain circuits associated with different profiles of interaction between the dorsal and the ventral streams.

Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis

Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer's Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19 % and the sensitivity of 0.9167.

What you see depends on what you saw, and what else you saw: the interactions between motion priming and object priming

Both visual object priming and motion priming have been reported independently, but the interactions between the two are still largely unexplored. Here we investigated this question using a novel type of SFM stimuli, 3-D helixes, and found that the motion direction perception of an ambiguous helix can be biased by the motion direction of a preceding SFM stimulus - a classic motion priming effect. However, the effectiveness of motion priming depends on object priming: a neutral object priming produced a weak motion priming, a congruent object priming led to a strong motion priming, and critically, an incongruent object priming abolished and overpowered the motion priming. In contrast, object priming alone (in the absence of motion overlap) had little effects biasing motion perception. Taken together, these results suggest that there exists an integrated neural representation of motion and structure of 3-D SFM stimuli, and motion priming of 3-D SFM stimuli might happen at an intermediate stage between MT/V5 (which is not shape selective) and LO (lateral occipital, which is not motion selective). This novel type of stimuli, 3-D helixes, along with the prime-target paradigm, thus might offer a unique tool to examine neural bases underlying the perception of 3-D SFM stimuli and perceptual priming.

Atypical lexicosemantic function of extrastriate cortex in autism spectrum disorder: evidence from functional and effective connectivity

Previous studies have suggested atypically enhanced activity of visual cortex during language processing in autism spectrum disorder (ASD). However, it remains unclear whether visual cortical participation reflects isolated processing within posterior regions or functional cooperation with distal brain regions, such as left inferior frontal gyrus (LIFG). We addressed this question using functional connectivity MRI (fcMRI) and structural equation modeling in 14 adolescents and adults with ASD and 14 matched typically developing (TD) participants. Data were analyzed to isolate low-frequency intrinsic fluctuations, by regressing out effects of a semantic decision task. For a right extrastriate seed derived from the strongest cluster of atypical activation in the ASD group, widespread effects of increased connectivity in prefrontal and medial frontal lobes bilaterally were observed for the ASD group, compared to the TD group. A second analysis for a seed in LIFG, derived from pooled activation effects in both groups, also yielded widespread effects of overconnectivity in the ASD group, especially in temporal lobes. Structural equation modeling showed that whereas right extrastriate cortex did not impact function of language regions (left and right IFG, left middle temporal gyrus) in the TD model, it was an integral part of a language circuit in the ASD group. These results suggest that atypical extrastriate activation during language processing in ASD reflects integrative (not isolated) processing. Furthermore, our findings are inconsistent with previous reports of functional underconnectivity in ASD, probably related to removal of task effects required to isolate intrinsic low-frequency fluctuations.

Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model

Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.

Electrophysiological imaging of brain activity and connectivity-challenges and opportunities

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.