Discrimination Task Reveals Differences in Neural Bases of Tinnitus and Hearing Impairment

We investigated auditory perception and cognitive processing in individuals with chronic tinnitus or hearing loss using functional magnetic resonance imaging (fMRI). Our participants belonged to one of three groups: bilateral hearing loss and tinnitus (TIN), bilateral hearing loss without tinnitus (HL), and normal hearing without tinnitus (NH). We employed pure tones and frequency-modulated sweeps as stimuli in two tasks: passive listening and active discrimination. All subjects had normal hearing through 2 kHz and all stimuli were low-pass filtered at 2 kHz so that all participants could hear them equally well. Performance was similar among all three groups for the discrimination task. In all participants, a distributed set of brain regions including the primary and non-primary auditory cortices showed greater response for both tasks compared to rest. Comparing the groups directly, we found decreased activation in the parietal and frontal lobes in the participants with tinnitus compared to the HL group and decreased response in the frontal lobes relative to the NH group. Additionally, the HL subjects exhibited increased response in the anterior cingulate relative to the NH group. Our results suggest that a differential engagement of a putative auditory attention and short-term memory network, comprising regions in the frontal, parietal and temporal cortices and the anterior cingulate, may represent a key difference in the neural bases of chronic tinnitus accompanied by hearing loss relative to hearing loss alone

Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative

Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC).We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities.Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis.Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae

Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study

BackgroundArtificial intelligence (AI) technologies, such as machine learning and natural language processing, have the potential to provide new insights into complex health data. Although powerful, these algorithms rarely move from experimental studies to direct clinical care implementation. ObjectiveWe aimed to describe the key components for successful development and integration of two AI technology–based research pipelines for clinical practice. MethodsWe summarized the approach, results, and key learnings from the implementation of the following two systems implemented at a large, tertiary care children’s hospital: (1) epilepsy surgical candidate identification (or epilepsy ID) in an ambulatory neurology clinic; and (2) an automated clinical trial eligibility screener (ACTES) for the real-time identification of patients for research studies in a pediatric emergency department. ResultsThe epilepsy ID system performed as well as board-certified neurologists in identifying surgical candidates (with a sensitivity of 71% and positive predictive value of 77%). The ACTES system decreased coordinator screening time by 12.9%. The success of each project was largely dependent upon the collaboration between machine learning experts, research and operational information technology professionals, longitudinal support from clinical providers, and institutional leadership. ConclusionsThese projects showcase novel interactions between machine learning recommendations and providers during clinical care. Our deployment provides seamless, real-time integration of AI technology to provide decision support and improve patient care

Local maxima for the individual groups and the inter-group contrasts for the passive listening task compared to rest.

<p>All reported clusters are p<0.05 FWE corrected for multiple comparisons at the voxel (indicated by ^* next to the Z-score) or cluster-level (indicated by ^#), cluster extent is 50 voxels.</p

Local maxima for the individual groups (cluster extent = 50 voxels) for the discrimination task compared to rest.

<p>All reported clusters are p≤0.05 FWE corrected for multiple comparisons at the voxel (indicated by ^* next to the Z-score) or cluster-level (indicated by ^#).</p

Timeline of a trial using sparse imaging technique.

<p>PL = passive listening, DT = discrimination task.</p

Demographic and clinical characteristics of the participants in the study.

<p>BDI = Beck Depression Inventory, THI = Tinnitus Handicap Inventory.</p

Statistical parametric maps of the discrimination task.

<p>Statistical parametric maps of the discrimination task (DT>Rest) rendered on a template brain for (a) normal hearing, (b) hearing loss and (c) tinnitus with hearing loss groups are shown on the left. The sagittal sections shown are located at <i>x</i> = 0 for all groups are shown on the right. All reported clusters are p<0.05 FWE corrected for multiple comparisons at the voxel or cluster-level. Some clusters are highlighted in the figure - AC: anterior cingulate, DMFG: dorsomedial frontal gyrus, STG: superior temporal gyrus.</p

Effect of tinnitus.

<p>Statistical parametric maps rendered on a template brain for the contrasts (a) TIN>NH and (b) HL>TIN, showing increased and decreased response, respectively, due to tinnitus. The contrasts NH>TIN and TIN>HL did not result in any suprathreshold voxels. All reported clusters are p<0.05 FWE corrected for multiple comparisons at the voxel or cluster-level. Some clusters are highlighted in the figure – STG: superior temporal gyrus, IPL: inferior parietal lobule, MTG: middle temporal gyrus.</p

Average audiograms of the participants including error bars that depict standard error of the mean.

<p>Average audiograms of the participants including error bars that depict standard error of the mean.</p