Frank H. Guenther

Developmental stuttering (DS) is a complex neurodevelopmental disorder affecting approximately 5% of children, characterized by involuntary disruptions in speech fluency. Despite its prevalence, the precise pathophysiology remains elusive, and current behavioral and pharmacological interventions often yield variable long-term efficacy. This scoping review evaluates the therapeutic potential of transcranial photobiomodulation (t-PBM), a non-invasive neuromodulation technique, by summarizing its mechanisms of action with the known neurophysiological deficits of DS. Evidence indicates that DS is associated with reduced regional cerebral blood flow (rCBF) in Broca’s area, mitochondrial dysfunction, and impaired neural connectivity. t-PBM may address these deficits by stimulating cytochrome c oxidase, thereby increasing ATP production and triggering nitric oxide-mediated vasodilation to enhance rCBF. Furthermore, t-PBM promotes neuroplasticity, modulates astrocyte function—potentially counteracting GNPTAB-related deficits—and exhibits anxiolytic effects that may alleviate the secondary psychological burden of DS. By targeting these multifactorial underpinnings, t-PBM may represent a promising, low-risk adjunct or primary intervention for DS, though this remains to be tested empirically. While the theoretical framework is robust, clinical trials are needed to determine whether t-PBM has therapeutic utility, to optimize treatment parameters, establish longitudinal efficacy, and explore synergistic effects with established speech-language therapies.

Collaborative robots, or co-robots, are semi-autonomous robotic agents designed to work alongside humans in shared workspaces. To be effective, co-robots require the ability to respond and adapt to dynamic scenarios encountered in natural environments. One way to achieve this is through exploratory learning, or “learning by doing,” an unsupervised method in which co-robots are able to build an internal model for motor planning and coordination based on real-time sensory inputs. In this paper, we present an adaptive neural network-based system for co-robot control that employs exploratory learning to achieve the coordinated motor planning needed to navigate toward, reach for, and grasp distant objects. To validate this system we used the 11-degrees-of-freedom RoPro Calliope mobile robot. Through motor babbling of its wheels and arm, the Calliope learned how to relate visual and proprioceptive information to achieve hand-eye-body coordination. By continually evaluating sensory inputs and externally provided goal directives, the Calliope was then able to autonomously select the appropriate wheel and joint velocities needed to perform its assigned task, such as following a moving target or retrieving an indicated object.

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty – both in the functional network edges and the corresponding aggregate measures of network topology – are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique to establish functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here – appropriate for static and dynamic network inference and different statistical measures of coupling – permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.