fMRI as a Preimplant Objective Tool to Predict Postimplant Oral Language Outcomes in Children with Cochlear Implants

Deshpande, Aniruddha K.; Tan, Lirong; Lu, Long; Altaye, Mekibib; Holland, Scott K.

doi:10.1097/aud.0000000000000259

Cited by 24 publications

(29 citation statements)

References 76 publications

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“…fMRI scanning included an active speech categorization task. Sixteen BKB sentences (Bench et al, 1979) recorded by a single male North American speaker under studio recording conditions were presented using sparse scanning procedures (‘HUSH’; (Deshpande et al, 2016; Schmithorst & Holland, 2004). Specifically, sentences were presented during a 6-second silent interval followed by 6 seconds of fMRI scanning (details below).…”

Section: Methodsmentioning

confidence: 99%

Listening difficulties in children: Behavior and brain activation produced by dichotic listening of CV syllables

Moore

Hugdahl

Stewart

et al. 2019

Preprint

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AbstractListening difficulties (LiD), also known as auditory processing disorders, are common in children with and without hearing loss. Impaired interactions between the two ears have been proposed as an important component of LiD. Previous studies have focused on testing using multiple sequential dichotic digits that carry a substantial memory load and executive control demands. We examined the ability of 6-13 year old children with normal audiometric thresholds to identify and selectively attend to dichotically presented CV syllables using the Bergen Dichotic Listening Test (BDLT;www.dichoticlistening.com). Children were recruited as typically developing (TD; n=39) or having LiD (n=35) based primarily on composite score of the ECLiPS caregiver report. Different single syllables (ba, da, ga, pa, ta, ka) were presented simultaneously to each ear (6×36 trials). Children reported the syllable heard most clearly (non-forced, NF) or the syllable presented to the right (forced, FR) or left (FL) ear. Interaural level differences (ILDs) manipulated bottom-up perceptual salience. Dichotic listening data (correct responses, Laterality Index) were analyzed initially by group (LiD, TD), age, report method (NF, FR, FL) and ILD (0, ± 15 dB) and compared with speech-in-noise thresholds (LiSN-S) and cognitive performance (NIH Toolbox). fMRI measured brain activation produced by a receptive speech task that segregated speech, phonetic and intelligibility components. Some activated areas (planum temporale, inferior frontal gyrus and orbitofrontal cortex) were correlated with dichotic results in TD children only.Neither group, age nor report method affected the Laterality Index of right/left recall. However, a significant interaction was found between ear, group and ILD. Children with LiD were more influenced by large ILDs, especially favoring the left ear, than were TD children. Neural activity associated with Speech, Phonetic and Intelligibility sentence cues did not significantly differ between groups. Significant correlations between brain activity level and BDLT were found in several frontal and temporal locations for the TD but not for the LiD group.Children with LiD were more influenced by large ILDs, especially favoring the left ear, than were TD children and were thus less able to modulate performance through attention, and more driven by the physical properties of the acoustic stimuli.

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Section: Methodsmentioning

confidence: 99%

Listening difficulties in children: Behavior and brain activation produced by dichotic listening of CV syllables

Moore

Hugdahl

Stewart

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…Modifications such as missing elements or syntactic deviations to induce activation in wider language networks may be especially promising in younger children (due to the functionally more integrated approach to syntax and semantics the developing brain seems to rely on). Such studies may even be conducted during natural sleep or under sedation (e.g., prior to inserting a cochlear implant). See also Ref.…”

Section: Preconditionsmentioning

confidence: 99%

“…In particular, the region within and the statistical threshold at which such an index is calculated must be chosen with great care . Functionally‐defined regions of interest may be indicated, as may be taking into account overt brain lesions …”

Section: Preconditionsmentioning

confidence: 99%

Clinical application of advanced MR methods in children: points to consider

Wilke

Groeschel

Lorenzen

et al. 2018

Ann Clin Transl Neurol

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The application of both functional MRI and diffusion MR tractography prior to a neurosurgical operation is well established in adults, but less so in children, for several reasons. For this review, we have identified several aspects (task design, subject preparation, actual scanning session, data processing, interpretation of results, and decision‐making) where pediatric peculiarities should be taken into account. Further, we not only systematically identify common issues, but also provide solutions, based on our experience as well as a review of the pertinent literature. The aim is to provide the clinician as well as the imaging scientist with information that helps to plan, conduct, and interpret such a clinically‐indicated exam in a way that maximizes benefit for, and minimizes the burden on the individual child.

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“…Using the thematic analysis ( Figure 3) we identified six themes: AI in brain mapping applications include prediction of child brain maturity based on fMRI [14], brain functional connectivity in preterm infants [15], classifying individuals at high-risk for psychosis based on functional brain activity [16], prediction of pediatric unipolar depression [17], analysis of resting-state brain function for attentiondeficit/hyperactivity disorder, predicting the language outcomes following cochlear implantation [18] and similar; AI use in pattern recognition are used for seizure prediction in children with epilepsy [19], visualization of complex data [20], predicting neurodevelopment [21] , identifying motor abnormalities [22], analysing EMG, ECG and other signal [23], image analysis, segmentation [24], etc;…”

Section: Ai In Pediatrics From a Bibliometric Perspectivementioning

confidence: 99%