Objectives: Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Design: Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Results: Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. Conclusions: This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Objective.We investigated tinnitus-related cortical networks in cochlear implant users who experience tinnitus and whose perception of tinnitus changes with use of their implant. Tinnitus, the perception of unwanted sounds which are not present externally, can be a debilitating condition. In individuals with cochlear implants, use of the implant is known to modulate tinnitus, often improving symptoms but worsening them in some cases. Little is known about underlying cortical changes with use of the implant, which lead to changes in tinnitus perception. In this study we investigated whether changes in brain networks with the cochlear implant turned on and off, were associated with changes in tinnitus perception, as rated subjectively.Approach.Using functional near-infrared spectroscopy, we recorded cortical activity at rest, from 14 cochlear implant users who experienced tinnitus. Recordings were performed with the cochlear implant turned off and on. For each condition, participants rated the loudness and annoyance of their tinnitus using a visual rating scale. Changes in neural synchrony have been reported in humans and animal models of tinnitus. To assess neural synchrony, functional connectivity networks with the implant turned on and off, were compared using two network features: node strength and diversity coefficient.Main results.Changes in subjective ratings of loudness were significantly correlated with changes in node strength, averaged across occipital channels (r=-0.65, p=0.01). Changes in both loudness and annoyance were significantly correlated with changes in diversity coefficient averaged across all channels (r=-0.79,p<0.001 and r=-0.86,p<0.001). More distributed connectivity with the implant on, compared to implant off, was associated with a reduction in tinnitus loudness and annoyance.Significance.A better understanding of neural mechanisms underlying tinnitus suppression with cochlear implant use, could lead to their application as a tinnitus treatment and pave the way for effective use of other less invasive stimulation-based treatments.

Many studies on morphology analysis show that if short inter-stimulus intervals separate tasks, the hemodynamic response amplitude will return to the resting-state baseline before the subsequent stimulation onset; hence, responses to successive tasks do not overlap. Accordingly, popular brain imaging analysis techniques assume changes in hemodynamic response amplitude subside after a short time (around 15 seconds). However, whether this assumption holds when studying brain functional connectivity has yet to be investigated. This paper assesses whether or not the functional connectivity network in control trials returns to the resting-state functional connectivity network. Traditionally, control trials in block-design experiments are used to evaluate response morphology to no stimulus. We analyzed data from an event-related experiment with audio and visual stimuli and resting state. Our results showed that functional connectivity networks during control trials were more similar to that of tasks than resting-state networks. In other words, contrary to task-related changes in the hemodynamic amplitude, where responses settle after a short time, the brain’s functional connectivity networks do not return to their intrinsic resting-state network in such short intervals.

Block-design is a popular experimental paradigm for functional near-infrared spectroscopy (fNIRS). Traditional block-design analysis techniques such as generalized linear modeling (GLM) and waveform averaging (WA) assume that the brain is a time-invariant system. This is a flawed assumption. In this paper, we propose a parametric Gaussian model to quantify the time-variant behavior found across consecutive trials of block-design fNIRS experiments. Using simulated data at different signal-to-noise ratios (SNRs), we demonstrate that our proposed technique is capable of characterizing Gaussian-like fNIRS signal features with ≥3dB SNR. When used to fit recorded data from an auditory block-design experiment, model parameter values quantitatively revealed statistically significant changes in fNIRS responses across trials, consistent with visual inspection of data from individual trials. Our results suggest that our model effectively captures trial-to-trial differences in response, which enables researchers to study time-variant brain responses using block-design fNIRS experiments.