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 (HCP) 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 ICC 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 to 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.

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

Dynamic causal modeling for EEG during complex laparoscopic skill acquisition

Fundamentals of Laparoscopic Surgery (FLS) is a prerequisite for board certification in general surgery in the USA. It includes a motor skills portion with five psychomotor tasks of increasing task complexity: (i) pegboard transfers, (ii) pattern cutting, (iii) placement of a ligating loop, (iv) suturing with extracorporeal knot tying, and (v) suturing with intracorporal knot tying. Learning these tasks typically relies on extensive practice [1]. Nemani et al. [2] showed that the wavelet coherence based functional connectivity from functional near-infrared spectroscopy (fNIRS) data between the medial prefrontal cortex and the supplementary motor area (SMA) was lower for experts than novices during FLS pattern cutting task. Here, SMA is known for the plasticity of interhemispheric connectivity involving sensorimotor network [3] relevant in learning bimanual laparoscopic tasks; however, transcranial direct current (tDCS) of SMA resulted in more variability during FLS pegboard transfers than bilateral primary motor cortex tDCS. Here, it is essential to differentiate tDCS effects on the pre-SMA from SMA proper in the SMA complex during laparoscopic skill acquisition due to differences in their fiber tracts [4] and their relevance to motor task complexity. Prior work using fNIRS-based activation during most complex FLS suturing task with intracorporeal knot tying [5] showed the involvement of premotor/frontal module [4] related Brodmann areas (BA), shown in Figure 1c, including ventrolateral PFC (VLPFC; BA: 44, 45, 47), frontopolar (FP; BA: 10), dorsolateral PFC (DLPFC; BA: 9, 46) as well as a part of the orbitofrontal cortex (OFC; BA: 11) on the lateral brain surface in addition to SMA complex. However, the effective connectivity of this cognitive-motor control network was not investigated based on dynamic causal modeling (DCM) [6], where the temporal resolution of electroencephalogram (EEG) can capture fast interactions expected via short frontal lobe connections [4]. Therefore, our research aimed to identify hidden brain networks during FLS suturing with intracorporeal knot tying skill acquisition using DCM of EEG.