Age-Related Temporal Processing Deficits in Word Segments in Adult Cochlear-Implant Users

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Aging may limit speech understanding outcomes in cochlear-implant (CI) users. Here, we examined age-related declines in auditory temporal processing as a potential mechanism that underlies speech understanding deficits associated with aging in CI users. Auditory temporal processing was assessed with a categorization task for the words dish and ditch (i.e., identify each token as the word dish or ditch) on a continuum of speech tokens with varying silence duration (0 to 60 ms) prior to the final fricative. In Experiments 1 and 2, younger CI (YCI), middle-aged CI (MCI), and older CI (OCI) users participated in the categorization task across a range of presentation levels (25 to 85 dB). Relative to YCI, OCI required longer silence durations to identify ditch and exhibited reduced ability to distinguish the words dish and ditch (shallower slopes in the categorization function). Critically, we observed age-related performance differences only at higher presentation levels. This contrasted with findings from normal-hearing listeners in Experiment 3 that demonstrated age-related performance differences independent of presentation level. In summary, aging in CI users appears to degrade the ability to utilize brief temporal cues in word identification, particularly at high levels. Age-specific CI programming may potentially improve clinical outcomes for speech understanding performance by older CI listeners

Brain Functional Connectivity Networks do not Return to Resting-state During Control Trials in Block Design Experiments

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.</p

Resting-state Functional Connectivity Predicts Cochlear-Implant Speech Outcomes

Background: 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 this study, we explore the utility of resting-state functional near-infrared spectroscopy (fNIRS) recordings to predict speech understanding outcomes before and after CI implantation. Our hypothesis revolves around resting-state functional connectivity (FC) as a reflection of brain plasticity post-hearing loss and implantation. Specifically, we hypothesized that the average clustering coefficient in resting FC networks can capture this variation among CI users.Methods: Twenty-two cochlear implant candidates participated in this study. Resting-state fNIRS data were collected pre-implantation and at one month, three months, and one year post-implantation. Speech understanding performance was assessed using CNC words in quiet and BKB sentences in noise one year post-implantation. Resting-state functional connectivity 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. Importantly, our analysis reveals that this measure provides unique information not accounted for by subject-specific factors such as age and duration of deafness.Conclusion: This approach utilizes 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 post implantation, correlates with speech understanding outcomes

Resting-state Functional Connectivity Predicts Cochlear-Implant Speech Outcomes

Background: 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 this study, we explore the utility of resting-state functional near-infrared spectroscopy (fNIRS) recordings to predict speech understanding outcomes before and after CI implantation. Our hypothesis revolves around resting-state functional connectivity (FC) as a reflection of brain plasticity post-hearing loss and implantation. Specifically, we hypothesized that the average clustering coefficient in resting FC networks can capture this variation among CI users.Methods: Twenty-two cochlear implant candidates participated in this study. Resting-state fNIRS data were collected pre-implantation and at one month, three months, and one year post-implantation. Speech understanding performance was assessed using CNC words in quiet and BKB sentences in noise one year post-implantation. Resting-state functional connectivity 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. Importantly, our analysis reveals that this measure provides unique information not accounted for by subject-specific factors such as age and duration of deafness.Conclusion: This approach utilizes 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 post implantation, correlates with speech understanding outcomes