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ABSTRACT The ability to interface electronic materials with the peripheral nervous system is required for stimulation and monitoring of neural signals. Thus, the design and engineering of robust neural interfaces that maintain material-tissue contact in the presence of material or tissue micromotion offer the potential to conduct novel measurements and develop future therapies that require chronic interface with the peripheral nervous system. However, such remains an open challenge given the constraints of existing materials sets and manufacturing approaches for design and fabrication of neural interfaces. Here, we investigated the potential to leverage a rapid prototyping approach for the design and fabrication of nerve cuffs that contain supporting features to mechanically stabilize the interaction between cuff electrodes and peripheral nerve. A hybrid 3D printing and robotic-embedding (i.e., pick-and-place) system was used to design and fabricate silicone nerve cuffs (800 µm diameter) containing conforming platinum (Pt) electrodes. We demonstrate that the electrical impedance of the cuff electrodes can be reduced by deposition of the conducting polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on cuff electrodes via a post-processing electropolymerization technique. The computer-aided design and manufacturing approach was also used to design and integrate supporting features to the cuff that mechanically stabilize the interface between the cuff electrodes and the peripheral nerve. Both ‘self-locking’ and suture-assisted locking mechanisms are demonstrated based on the principle of making geometric alterations to the cuff opening via 3D printing. Ultimately, this work shows 3D printing offers considerable opportunity to integrate supporting features, and potentially even novel electronic materials, into nerve cuffs that can support the design and engineering of next generation neural interfaces. 

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala–cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

 

Spontaneous infra-slow (<0.1 Hz) fluctuations in functional magnetic resonance imaging (fMRI) signals are temporally correlated within large-scale functional brain networks, motivating their use for mapping systems-level brain organization. However, recent electrophysiological and hemodynamic evidence suggest state-dependent propagation of infra-slow fluctuations, implying a functional role for ongoing infra-slow activity. Crucially, the study of infra-slow temporal lag structure has thus far been limited to large groups, as analyzing propagation delays requires extensive data averaging to overcome sampling variability. Here, we use resting-state fMRI data from 11 extensively-sampled individuals to characterize lag structure at the individual level. In addition to stable individual-specific features, we find spatiotemporal topographies in each subject similar to the group average. Notably, we find a set of early regions that are common to all individuals, are preferentially positioned proximal to multiple functional networks, and overlap with brain regions known to respond to diverse behavioral tasks—altogether consistent with a hypothesized ability to broadly influence cortical excitability. Our findings suggest that, like correlation structure, temporal lag structure is a fundamental organizational property of resting-state infra-slow activity.