Test-retest reliability of regression dynamic causal modeling

Regression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability-a test-theoretical property of particular importance for clinical applications-together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean intraclass correlation coefficient 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates with measures of functional connectivity, rDCM performed favorably-particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications. Keywords: Connectomics; Effective connectivity; Generative model; Regression dynamic causal modeling; Test-retest reliability; rDC

Pupil size signals novelty and predicts later retrieval success for declarative memories of natural scenes

Declarative memories of personal experiences are a key factor in defining oneself as an individual, which becomes particularly evident when this capability is impaired. Assessing the physiological mechanisms of human declarative memory is typically restricted to patients with specific lesions and requires invasive brain access or functional imaging. We investigated whether the pupil, an accessible physiological measure, can be utilized to probe memories for complex natural visual scenes. During memory encoding, scenes that were later remembered elicited a stronger pupil constriction compared to scenes that were later forgotten. Thus, pupil size predicts success or failure of memory formation. In contrast, novel scenes elicited stronger pupil constriction than familiar scenes during retrieval. When viewing previously memorized scenes, those that were forgotten (misjudged as novel) still elicited stronger pupil constrictions than those correctly judged as familiar. Furthermore, pupil constriction was influenced more strongly if images were judged with high confidence. Thus, we propose that pupil constriction can serve as a marker of novelty. Since stimulus novelty modulates the efficacy of memory formation, our pupil measurements during learning indicate that the later forgotten images were perceived as less novel than the later remembered pictures. Taken together, our data provide evidence that pupil constriction is a physiological correlate of a neural novelty signal during formation and retrieval of declarative memories for complex, natural scenes

Pupil size signals novelty and predicts later retrieval success for declarative memories of natural scenes

Declarative memories of personal experiences are a key factor in defining oneself as an individual, which becomes particularly evident when this capability is impaired. Assessing the physiological mechanisms of human declarative memory is typically restricted to patients with specific lesions and requires invasive brain access or functional imaging. We investigated whether the pupil, an accessible physiological measure, can be utilized to probe memories for complex natural visual scenes. During memory encoding, scenes that were later remembered elicited a stronger pupil constriction compared to scenes that were later forgotten. Thus, pupil size predicts success or failure of memory formation. In contrast, novel scenes elicited stronger pupil constriction than familiar scenes during retrieval. When viewing previously memorized scenes, those that were forgotten (misjudged as novel) still elicited stronger pupil constrictions than those correctly judged as familiar. Furthermore, pupil constriction was influenced more strongly if images were judged with high confidence. Thus, we propose that pupil constriction can serve as a marker of novelty. Since stimulus novelty modulates the efficacy of memory formation, our pupil measurements during learning indicate that the later forgotten images were perceived as less novel than the later remembered pictures. Taken together, our data provide evidence that pupil constriction is a physiological correlate of a neural novelty signal during formation and retrieval of declarative memories for complex, natural scenes

Regression DCM for fMRI

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data

Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics

No-Report Paradigms: Extracting the True Neural Correlates of Consciousness

The goal of consciousness research is to reveal the neural basis of phenomenal experience. To study phenomenology, experimenters seem obliged to ask reports from the subjects to ascertain what they experience. However, we argue that the requirement of reports has biased the search for the neural correlates of consciousness over the past decades. More recent studies attempt to dissociate neural activity that gives rise to consciousness from the activity that enables the report; in particular, no-report paradigms have been utilized to study conscious experience in the full absence of any report. We discuss the advantages and disadvantages of report-based and no-report paradigms, and ask how these jointly bring us closer to understanding the true neural basis of consciousness