Development of a Method to Compensate for Signal Quality Variations in Repeated Auditory Event-Related Potential Recordings

Reliable measurements are mandatory in clinically relevant auditory event-related potential (AERP)-based tools and applications. The comparability of the results gets worse as a result of variations in the remaining measurement error. A potential method is studied that allows optimization of the length of the recording session according to the concurrent quality of the recorded data. In this way, the sufficiency of the trials can be better guaranteed, which enables control of the remaining measurement error. The suggested method is based on monitoring the signal-to-noise ratio (SNR) and remaining measurement error which are compared to predefined threshold values. The SNR test is well defined, but the criterion for the measurement error test still requires further empirical testing in practice. According to the results, the reproducibility of average AERPs in repeated experiments is improved in comparison to a case where the number of recorded trials is constant. The test-retest reliability is not significantly changed on average but the between-subject variation in the value is reduced by 33–35%. The optimization of the number of trials also prevents excessive recordings which might be of practical interest especially in the clinical context. The efficiency of the method may be further increased by implementing online tools that improve data consistency

Detecting Distal Radius Fractures Using a Segmentation-Based Deep Learning Model

Deep learning algorithms can be used to classify medical images. In distal radius fracture treatment, fracture detection and radiographic assessment of fracture displacement are critical steps. The aim of this study was to use pixel-level annotations of fractures to develop a deep learning model for precise distal radius fracture detection. We randomly divided 3785 consecutive emergency wrist radiograph examinations from six hospitals to a training set (3399 examinations) and test set (386 examinations). The training set was used to develop the deep learning model and the test set to assess its validity. The consensus of three hand surgeons was used as the gold standard for the test set. The area under the ROC curve was 0.97 (CI 0.95-0.98) and 0.95 (CI 0.92-0.98) for examinations without a cast. Fractures were identified with higher accuracy in the postero-anterior radiographs than in the lateral radiographs. Our deep learning model performed well in our multi-hospital and multi-radiograph system manufacturer settings. Thus, segmentation-based deep learning models may provide additional benefit. Further research is needed with algorithm comparison and external validation.Peer reviewe

Enhanced Memory Consolidation Via Automatic Sound Stimulation During Non-REM Sleep

Introduction: Slow-wave sleep (SWS) slow waves and sleep spindle activity have been shown to be crucial for memory consolidation. Recently, memory consolidation has been causally facilitated in human participants via auditory stimuli phase-locked to SWS slow waves. Aims: Here, we aimed to develop a new acoustic stimulus protocol to facilitate learning and to validate it using different memory tasks. Most importantly, the stimulation setup was automated to be applicable for ambulatory home use. Methods: Fifteen healthy participants slept 3 nights in the laboratory. Learning was tested with 4 memory tasks (word pairs, serial finger tapping, picture recognition, and face-name association). Additional questionnaires addressed subjective sleep quality and overnight changes in mood. During the stimulus night, auditory stimuli were adjusted and targeted by an unsupervised algorithm to be phase-locked to the negative peak of slow waves in SWS. During the control night no sounds were presented. Results: Results showed that the sound stimulation increased both slow wave (p =.002) and sleep spindle activity (p Conclusions: We showed that the memory effect of the SWS-targeted individually triggered single-sound stimulation is specific to verbal associative memory. Moreover, the ambulatory and automated sound stimulus setup was promising and allows for a broad range of potential follow-up studies in the future.Peer reviewe

Understanding developmental language disorder -The Helsinki longitudinal SLI study (HelSLI): A study protocol

BackgroundDevelopmental language disorder (DLD, also called specific language impairment, SLI) is a common developmental disorder comprising the largest disability group in pre-school-aged children. Approximately 7% of the population is expected to have developmental language difficulties. However, the specific etiological factors leading to DLD are not yet known and even the typical linguistic features appear to vary by language. We present here a project that investigates DLD at multiple levels of analysis and aims to make the reliable prediction and early identification of the difficulties possible. Following the multiple deficit model of developmental disorders, we investigate the DLD phenomenon at the etiological, neural, cognitive, behavioral, and psychosocial levels, in a longitudinal study of preschool children.MethodsIn January 2013, we launched the Helsinki Longitudinal SLI study (HelSLI) at the Helsinki University Hospital (http://tiny.cc/HelSLI). We will study 227 children aged 3–6 years with suspected DLD and their 160 typically developing peers. Five subprojects will determine how the child’s psychological characteristics and environment correlate with DLD and how the child’s well-being relates to DLD, the characteristics of DLD in monolingual versus bilingual children, nonlinguistic cognitive correlates of DLD, electrophysiological underpinnings of DLD, and the role of genetic risk factors. Methods include saliva samples, EEG, computerized cognitive tasks, neuropsychological and speech and language assessments, video-observations, and questionnaires.DiscussionThe project aims to increase our understanding of the multiple interactive risk and protective factors that affect the developing heterogeneous cognitive and behavioral profile of DLD, including factors affecting literacy development. This accumulated knowledge will form a heuristic basis for the development of new interventions targeting linguistic and non-linguistic aspects of DLD.<br /

Is epileptiform activity related to developmental language disorder?:findings from the HelSLI study

Abstract Objectives: To study if interictal epileptiform discharges (IEDs) are associated with language performance or pre-/perinatal factors in children with developmental language disorder (DLD). Methods: We recorded routine EEG in wake and sleep in 205 children aged 2.9–7.1 years with DLD, without neurologic diseases or intellectual disability. We examined the language performance of the children and collected data on pre-/perinatal factors. Results: Interictal epileptiform discharges were not associated with lower language performance. Children with so-called “rolandic”, i.e. centrotemporoparietal, IEDs had better language skills, but age explained this association. Most pre-/perinatal factors evaluated did not increase the risk of rolandic IEDs, except for maternal smoking (OR 4.4, 95% CI 1.4–14). We did not find electrical status epilepticus during slow-wave sleep (ESES)/spike-and-wave activation in sleep (SWAS) in any children. Conclusions: Interictal epileptiform discharges are not associated with lower language performance, and ESES/SWAS is not common in children with DLD. Significance: Routine EEGs do not bring additional information about language performance in children with DLD who do not have any neurologic diseases, seizures, intellectual disability, or regression of language development

Understanding developmental language disorder - the Helsinki longitudinal SLI study (HelSLI):a study protocol

Abstract Background: Developmental language disorder (DLD, also called specific language impairment, SLI) is a common developmental disorder comprising the largest disability group in pre-school-aged children. Approximately 7% of the population is expected to have developmental language difficulties. However, the specific etiological factors leading to DLD are not yet known and even the typical linguistic features appear to vary by language. We present here a project that investigates DLD at multiple levels of analysis and aims to make the reliable prediction and early identification of the difficulties possible. Following the multiple deficit model of developmental disorders, we investigate the DLD phenomenon at the etiological, neural, cognitive, behavioral, and psychosocial levels, in a longitudinal study of preschool children. Methods: In January 2013, we launched the Helsinki Longitudinal SLI study (HelSLI) at the Helsinki University Hospital (http://tiny.cc/HelSLI). We will study 227 children aged 3–6 years with suspected DLD and their 160 typically developing peers. Five subprojects will determine how the child’s psychological characteristics and environment correlate with DLD and how the child’s well-being relates to DLD, the characteristics of DLD in monolingual versus bilingual children, nonlinguistic cognitive correlates of DLD, electrophysiological underpinnings of DLD, and the role of genetic risk factors. Methods include saliva samples, EEG, computerized cognitive tasks, neuropsychological and speech and language assessments, video-observations, and questionnaires. Discussion: The project aims to increase our understanding of the multiple interactive risk and protective factors that affect the developing heterogeneous cognitive and behavioral profile of DLD, including factors affecting literacy development. This accumulated knowledge will form a heuristic basis for the development of new interventions targeting linguistic and non-linguistic aspects of DLD