Tinnitus, the perception of sounds not present externally, can significantly impair quality of life. Currently there are no clinically available objective measures to enable monitoring of tinnitus-related changes in brain activity. Our previous work demonstrated sensitivity of a non-invasive imaging technique called functional near-infrared spectroscopy (fNIRS) to changes in brain activity associated with tinnitus presence and severity. In this study we aimed to assess whether fNIRS features could quantify tinnitus severity on a continuous scale, a requirement for effective monitoring of treatments. We collected fNIRS data at rest and in response to auditory and visual stimuli from a group of 103 individuals with tinnitus and 50 controls. We identified fNIRS-derived measures of connectivity which showed significant group differences and used these features in a linear regression model to predict tinnitus-related distress as rated using the Tinnitus Handicap Inventory (THI), and tinnitus loudness and annoyance scores. We assessed correlations between predicted and actual severity in the large tinnitus dataset as well as in two cohorts with shared clinical features- those with severe tinnitus (THI≥58), and cochlear implant users who experienced tinnitus. Findings showed predicted THI ratings correlated significantly with actual THI scores in our large group of individuals with tinnitus although predictive precision was limited, likely due to heterogeneity within this group. Predictions improved when considering both the severe cohort and cochlear implant users with tinnitus. A biomarker of tinnitus severity could provide a tool to monitor treatment effectiveness, particularly within tinnitus subtypes with shared clinical and neural characteristics.

Tinnitus is a condition which involves hearing sounds not present externally. This common condition can lead to a range of symptoms, including depression, resulting in a severe impact on quality of life. There are currently no reliable treatments for tinnitus. One factor hindering development of treatments is the lack of identified subtypes of tinnitus with different underlying patterns of neural activity to enable more personalised treatments and more accurate monitoring of treatment effects. It has been suggested that the perceived laterality of tinnitus, i.e., whether the sound is perceived unilaterally or bilaterally, characterizes tinnitus subtypes with different underlying neural changes. Our previous work showed sensitivity of a non-invasive brain imaging technique called functional near-infrared spectroscopy (fNIRS), to tinnitus-related changes in brain activity. In this study we aimed to investigate differentiating unilateral and bilateral tinnitus using fNIRS recordings and functional network analysis. We performed fNIRS recordings on 18 individuals with unilateral tinnitus (11 left-sided and 7 right-sided), 26 individuals bilateral tinnitus and 18 controls. fNIRS signals were recorded at rest and in response to auditory and visual stimuli. Using network analysis applied to fNIRS recordings, we derived modules characterized by strong connectivity among channels within a module and weak connectivity among channels in different modules. We then calculated two measures, Module Laterality and Modified Module Laterality, to quantify asymmetry in modules. Our findings showed significant difference in Module Laterality in individuals with unilateral tinnitus compared to both bilateral tinnitus and controls. Within the unilateral tinnitus group, Modified Module Laterality showed significant difference between individuals who experienced left-sided tinnitus compared to right-sided tinnitus. Differentiating tinnitus with distinct laterality precepts has the potential to assist in developing and monitoring relevant treatments by revealing neural mechanisms related to each subtype.

ObjectiveTo use a multimodal approach to classify individuals with tinnitus from controls, and individuals with mild versus severe tinnitus.DesignWe have previously shown feasibility of a non-invasive imaging technique called functional near-infrared spectroscopy (fNIRS) to detect tinnitus-related changes in cortical activity and classify individuals with tinnitus from controls, as well as individuals with mild versus severe tinnitus. In this study we have used a multimodal approach by recording heart rate, heart rate variability and skin conductance, in addition to fNIRS signals, from individuals with tinnitus and controls.Study SampleTwenty-seven participants with tinnitus and 21 controls were recruited.ResultsOur findings show, addition of heart rate measures can improve accuracy of classifying tinnitus severity, in particular loudness as rated subjectively. The f1-score, a measure of classification accuracy, increased from 0.73 to 0.86 when using a support vector machine classifier for differentiating low versus high tinnitus loudness.ConclusionsSubjective tinnitus is a condition that can only be described by the individual experiencing it, as there are currently no objective measures to determine tinnitus presence and severity, or assess the effectiveness of treatments. Objective measurement of tinnitus is a critical step in developing reliable treatments for this debilitating condition.

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.

Non-invasive coordinated reset stimulation (CRS) to the hands has been shown to improve motor ability in Parkinson’s patients, but not specific for gait disturbances. The overall aim of the project is the application of vibrotactile CRS to the feet to improve gait impairments in Parkinson’s disease. As a first step towards this objective, we showed that vibrotactile stimulation to the feet can elicit a cortical response and have identified differences in younger and older individuals. Our findings suggest the potential for non-invasive peripheral stimulation as a therapeutic technique.Clinical Relevance— This is an important step towards developing a non-invasive stimulation technique for the management of gait disturbances in Parkinson’s disease.

Tinnitus affects around six to 20 percent of adults, with approximately 20 percent of these having severe and debilitating symptoms, such as depression and cognitive dysfunction.1, 2 Despite its wide prevalence, no clinically objective test is available to determine the presence or severity of tinnitus or assess the effectiveness of treatments.3 Developing an objective measure of tinnitus will enable a more accurate assessment of this condition, marking an important step in the development and assessment of potential treatments.Shutterstock/sdecoretFigure 1: fNIRS Montage. Sixteen sources (red circles) and 16 detectors (gray circles) forming channels were placed on the frontal, temporal, and occipital regions of the scalp. Channel numbers are shown. 36 long and 4 short channels (marked by * and yellow source-detector links) were formed. Audiology, machine learning, artificial intelligenceTo address this concern, we used functional near-infrared spectroscopy (fNIRS), which monitors changes in blood oxygen levels, allowing the imaging of the oxygen demands of active brain regions. fNIRS is non-invasive and non-radioactive. It operates quietly, features a good temporal resolution, and has several advantages over other imaging modalities used to localize human brain areas that show changes in activity related to tinnitus.2, 4 It is also portable and cost-effective, which are important for routine clinical use. Changes in the level of spontaneous neural activity, neural synchrony, and reorganization of cortical tonotopic maps have been correlated with tinnitus.2 Several cortical regions identified in tinnitus neuroimaging studies are accessible using fNIRS, including the auditory cortex, frontal cortex, and cuneus.4-6 Most reported findings in brain imaging studies on tinnitus are based on a statistical analysis of either resting state or evoked brain activity.2 Machine learning methods are well suited to integrating signal features from multiple channels and different conditions with providing a diagnosis for a single subject rather than showing group-level differences as statistical methods usually do. Subjective ratings of tinnitus severity can be used as input to machine learning algorithms to map fNIRS signal features to severity levels (training phase). fNIRS features from other individuals can then be classified to tinnitus severity levels based on past observations. In this study, we applied statistical and machine learning algorithms to fNIRS signals to (1) assess the sensitivity of fNIRS to differentiate individuals with tinnitus from controls and (2) identify fNIRS features associated with subjective ratings of tinnitus severity and whether these could differentiate between perceived loudness of tinnitus and annoyance. We first performed statistical analysis to gain a better understanding of signal features, cortical regions, and conditions that show group differences and changes with tinnitus severity levels, and to avoid our machine learning algorithms becoming a ‘black box’. STUDY METHODS fNIRS recordings were performed on 25 participants with chronic subjective tinnitus (23 experience it bilaterally) and 21 healthy adults with no history of tinnitus, neurological, or hearing disorders. There were no significant group differences in age or hearing thresholds. Data from three healthy participants were excluded due to signal quality or technical issues with the fNIRS. Tinnitus severity was assessed using the Tinnitus Handicap Inventory (THI),7 which quantifies the perceived severity of tinnitus and associates score ranges with different severity levels (e.g., 0 to 16 for slight tinnitus, 58 to76 for severe). Participants with tinnitus also rated the loudness and annoyance of their tinnitus on a scale of 1 to 10 before each recording. fNIRS signals were collected using a continuous-wave fNIRS system (NIRScout, NIRx Medical Technologies LLC), with 16 sources and 16 detectors placed over the frontal, temporal, and occipital cortical regions (Fig. 1). Each source-detector pair (called a ‘channel’), was placed ~30mm apart. Using source-detector pairs placed close together (~11mm), systemic signals from superficial layers were recorded and used to remove systemic artifacts from other channels. The fNIRS test session consisted of three recording periods. The first recording period was a six-minute resting-state recording with participants sitting still with their eyes closed. The two remaining periods recorded fNIRS signals in response to 15-second auditory or visual stimuli presented at random, and 20- or 25-second non-stimulus intervals in between. Several preprocessing steps were applied to the fNIRS signals to remove channels with poor signal quality from further analysis. Signals from the remaining channels were converted to optical density and concentration changes of oxygenated and deoxygenated hemoglobin (HbO and HbR) estimated using the modified Beer-Lambert law.8 fNIRS RESTING-STATE STATISTICAL ANALYSIS & FINDINGS Seed analysis was used to investigate resting-state functional connectivity networks by finding correlations between seed regions and other brain regions (e.g., see 9) using whitened correlations.10 In this study, a left seed was used by averaging channels 9 and 10 (Fig. 1), and a right seed was obtained by averaging channels 30 and 31 (channels estimated to cover the superior temporal and Heschl's gyrus). HbO and HbR correlation values for channels comprising frontal and occipital regions of interest (ROI) were then averaged for statistical analysis. Frontal channels were located over the superior frontal gyrus, medial, superior frontal gyrus, medial orbital, and middle frontal gyrus. The occipital channels covered the cuneus and superior occipital gyrus. Connectivity measures between both left and right seeds with frontal HbO signals were higher in the tinnitus group, with right seed differences reaching significance. This was not found for left seed connectivity or HbR signals. In the tinnitus group, HbO-derived connectivity between left and right seeds and frontal channels increased with the duration of tinnitus with the correlation on the right side approaching significance. Right seed- occipital connectivity values derived from HbR signals were significantly higher in the tinnitus group. This was not found for left seed connectivity. In the tinnitus group, HbR-derived connectivity between the right seed and occipital channels increased significantly with subjective ratings of loudness. fNIRS EVOKED RESPONSE STATISTICAL ANALYSIS & FINDINGS To analyze evoked responses, HbO and HbR signals were epoched from t = –5 to t = 30s relative to stimulus onset and epochs with excessive amplitudes due to noise rejected. Mean HbO and HbR amplitudes across time windows 0 to 5 seconds (for auditory responses) and 10–15 seconds (visual) were calculated. These time windows were chosen based on the initial rising phase of the group averaged responses and waveform morphology.11, 12 fNIRS evoked response amplitudes were averaged over ROIs for statistical analysis. Left and right temporal ROIs included channels on the left and right sides covering the superior, middle and inferior temporal gyrus, Heschl's gyrus, and angular gyrus. Visual evoked responses were averaged over the occipital ROI, covering the cuneus and superior occipital gyrus. There was no significant difference between left and right auditory responses. Averaged across both sides, the HbO auditory response was smaller in the tinnitus group. This result could be due to the increased background neural activity present in tinnitus leading to saturation of the hemodynamic response.13 Visual response amplitudes were significantly larger in the control group. COMBINING fNIRS FEATURES USING MACHINE LEARNING To combine features from HbO and HbR resting state and evoked response signals from fNIRS channels over different cortical regions, machine learning methods including feature selection and classifiers were used. Features input to these algorithms included auditory and visual response amplitudes and frontal and occipital connectivity measures described above. Features from all channels were used, and Information Gain, a feature selection algorithm, was used to rank features based on their importance in classification. These features were then used with four different classification methods to classify participants as controls or experiencing tinnitus. Classifiers were also used to differentiate patients with tinnitus as having slight/ mild versus moderate/ severe tinnitus (based on THI ratings). The four classifiers used were Naïve Bayes, K-nearest neighbor (KNN), Rule Induction, and Artificial neural networks (ANN). Classifier performance was assessed using only connectivity measures, only evoked response features, or using both connectivity and evoked features to assess the relative importance of the different features. To calculate the performance of these algorithms, 10-fold cross-validation was used and the average sensitivity (true positive rate), specificity (true negative rate), and accuracy (number of correctly predicted samples over the total number of samples) were calculated. The best accuracy in differentiating tinnitus participants from controls was achieved using auditory features alone and a Naïve Bayes classifier, resulting in an accuracy of 78.3 percent. The highest accuracy of 87 percent for differentiating slight/ mild (n = 18) from moderate/ severe (n = 7) tinnitus was achieved using connectivity measures with the Neural Network classifier although the sensitivity obtained was low (51.23%). FUTURE DIRECTIONS Our statistical findings support previous research that has identified measures of brain connectivity or evoked responses associated with tinnitus. We have built on these findings further using fNIRS and machine learning, and have shown the feasibility of this approach to classify an individual's fNIRS data to a tinnitus or control group as well as a tinnitus severity level with promising accuracy. Confirmation of findings with larger sample sizes is needed to validate our models. Tinnitus, by nature, will always have a subjective component. However, an objective measure will help measure certain aspects of tinnitus that will assist with the development and testing of new treatments.

Objectives: Functional near-infrared spectroscopy (fNIRS) is a brain imaging technique particularly suitable for hearing studies. However, the nature of fNIRS responses to auditory stimuli presented at different stimulus intensities is not well understood. In this study, we investigated whether fNIRS response amplitude was better predicted by stimulus properties (intensity) or individually perceived attributes (loudness). Design: Twenty-two young adults were included in this experimental study. Four different stimulus intensities of a broadband noise were used as stimuli. First, loudness estimates for each stimulus intensity were measured for each participant. Then, the 4 stimulation intensities were presented in counterbalanced order while recording hemoglobin saturation changes from cortical auditory brain areas. The fNIRS response was analyzed in a general linear model design, using 3 different regressors: a non-modulated, an intensity-modulated, and a loudness-modulated regressor. Results: Higher intensity stimuli resulted in higher amplitude fNIRS responses. The relationship between stimulus intensity and fNIRS response amplitude was better explained using a regressor based on individually estimated loudness estimates compared with a regressor modulated by stimulus intensity alone. Conclusions: Brain activation in response to different stimulus intensities is more reliant upon individual loudness sensation than physical stimulus properties. Therefore, in measurements using different auditory stimulus intensities or subjective hearing parameters, loudness estimates should be examined when interpreting results.

Chronic tinnitus is a debilitating condition which affects 10–20% of adults and can severely impact their quality of life. Currently there is no objective measure of tinnitus that can be used clinically. Clinical assessment of the condition uses subjective feedback from individuals which is not always reliable. We investigated the sensitivity of functional near-infrared spectroscopy (fNIRS) to differentiate individuals with and without tinnitus and to identify fNIRS features associated with subjective ratings of tinnitus severity. We recorded fNIRS signals in the resting state and in response to auditory or visual stimuli from 25 individuals with chronic tinnitus and 21 controls matched for age and hearing loss. Severity of tinnitus was rated using the Tinnitus Handicap Inventory and subjective ratings of tinnitus loudness and annoyance were measured on a visual analogue scale. Following statistical group comparisons, machine learning methods including feature extraction and classification were applied to the fNIRS features to classify patients with tinnitus and controls and differentiate tinnitus at different severity levels. Resting state measures of connectivity between temporal regions and frontal and occipital regions were significantly higher in patients with tinnitus compared to controls. In the tinnitus group, temporal-occipital connectivity showed a significant increase with subject ratings of loudness. Also in this group, both visual and auditory evoked responses were significantly reduced in the visual and auditory regions of interest respectively. Naïve Bayes classifiers were able to classify patients with tinnitus from controls with an accuracy of 78.3%. An accuracy of 87.32% was achieved using Neural Networks to differentiate patients with slight/ mild versus moderate/ severe tinnitus. Our findings show the feasibility of using fNIRS and machine learning to develop an objective measure of tinnitus. Such a measure would greatly benefit clinicians and patients by providing a tool to objectively assess new treatments and patients’ treatment progress.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technique that measures changes in oxygenated and de-oxygenated hemoglobin concentration and can provide a measure of brain activity. In addition to neural activity, fNIRS signals contain components that can be used to extract physiological information such as cardiac measures. Previous studies have shown changes in cardiac activity in response to different sounds. This study investigated whether cardiac responses collected using fNIRS differ for different loudness of sounds. fNIRS data were collected from 28 normal hearing participants. Cardiac response measures evoked by broadband, amplitude-modulated sounds were extracted for four sound intensities ranging from near-threshold to comfortably loud levels (15, 40, 65 and 90 dB Sound Pressure Level (SPL)). Following onset of the noise stimulus, heart rate initially decreased for sounds of 15 and 40 dB SPL, reaching a significantly lower rate at 15 dB SPL. For sounds at 65 and 90 dB SPL, increases in heart rate were seen. To quantify the timing of significant changes, inter-beat intervals were assessed. For sounds at 40 dB SPL, an immediate significant change in the first two inter-beat intervals following sound onset was found. At other levels, the most significant change appeared later (beats 3 to 5 following sound onset). In conclusion, changes in heart rate were associated with the level of sound with a clear difference in response to near-threshold sounds compared to comfortably loud sounds. These findings may be used alone or in conjunction with other measures such as fNIRS brain activity for evaluation of hearing ability.

Sound intensity is a key feature of auditory signals. A profound understanding of cortical processing of this feature is therefore highly desirable. This study investigates whether cortical functional near-infrared spectroscopy (fNIRS) signals reflect sound intensity changes and where on the brain cortex maximal intensity-dependent activations are located. The fNIRS technique is particularly suitable for this kind of hearing study, as it runs silently. Twenty-three normal hearing subjects were included and actively participated in a counterbalanced block design task. Four intensity levels of a modulated noise stimulus with long-term spectrum and modulation characteristics similar to speech were applied, evenly spaced from 15 to 90 dB SPL. Signals from auditory processing cortical fields were derived from a montage of 16 optodes on each side of the head. Results showed that fNIRS responses originating from auditory processing areas are highly dependent on sound intensity level: higher stimulation levels led to higher concentration changes. Caudal and rostral channels showed different waveform morphologies, reflecting specific cortical signal processing of the stimulus. Channels overlying the supramarginal and caudal superior temporal gyrus evoked a phasic response, whereas channels over Broca's area showed a broad tonic pattern. This data set can serve as a foundation for future auditory fNIRS research to develop the technique as a hearing assessment tool in the normal hearing and hearing-impaired populations.

BACKGROUND:Current electroencephalogram (EEG)-derived measures provide information on cortical activity and hypnosis but are less accurate regarding subcortical activity, which is expected to vary with the degree of antinociception. Recently, the neurophysiologically based EEG measures of cortical input (CI) and cortical state (CS) have been shown to be prospective indicators of analgesia/antinociception and hypnosis, respectively. In this study, we compared CI and an alternate measure of CS, the composite cortical state (CCS), with the Bispectral Index (BIS) and another recently developed measure of antinociception, the composite variability index (CVI). CVI is an EEG-derived measure based on a weighted combination of BIS and estimated electromyographic activity. By assessing the relationship between these indices for equivalent levels of hypnosis (as quantified using the BIS) and the nociceptive-antinociceptive balance (as determined by the predicted effect-site concentration of remifentanil), we sought to evaluate whether combining hypnotic and analgesic measures could better predict movement in response to a noxious stimulus than when used alone. METHODS:Time series of BIS and CVI indices and the raw EEG from a previously published study were reanalyzed. In our current study, the data from 80 patients, each randomly allocated to a target hypnotic level (BIS 50 or BIS 70) and a target remifentanil level (Remi-0, -2, -4 or -6 ng/mL), were included in the analysis. CCS, CI, BIS, and CVI were calculated or quantified at baseline and at a number of intervals after the application of the Observer's Assessment of Alertness/Sedation scale and a subsequent tetanic stimulus. The dependency of the putative measures of antinociception CI and CVI on effect-site concentration of remifentanil was then quantified, together with their relationship to the hypnotic measures CCS and BIS. Finally, statistical clustering methods were used to evaluate the extent to which simple combinations of antinociceptive and hypnotic measures could better detect and predict response to stimulation. RESULTS:Before stimulation, both CI and CVI differentiated patients who received remifentanil from those who were randomly allocated to the Remi-0 group (CI: Cohen's d = 0.65, 95% confidence interval, 0.48-0.83; CVI: Cohen's d = 0.72, 95% confidence interval, 0.56-0.88). Strong correlations between BIS and CCS were found (at different periods: 0.55 < R2 < 0.68, P < 0.001). Application of the Observer's Assessment of Alertness/Sedation stimulus was associated with changes in CI and CCS, whereas, subsequent to the application of both stimuli, changes in all measures were seen. Pairwise combinations of CI and CCS showed higher sensitivity in detecting response to stimulation than CVI and BIS combined (sensitivity [99% confidence interval], 75.8% [52.7%-98.8%] vs 42% [15.4%-68.5%], P = 0.006), with specificity for CI and CCS approaching significance (52% [34.7%-69.3%] vs 24% [9.1%-38.9%], P = 0.0159). CONCLUSIONS:Combining electroencephalographically derived hypnotic and analgesic quantifiers may enable better prediction of patients who are likely to respond to tetanic stimulation.

The brain anaesthesia response (BAR) monitor uses a method of EEG analysis, based on a model of brain electrical activity, to monitor the cerebral response to anaesthetic and sedative agents via two indices, composite cortical state (CCS) and cortical input (CI). It was hypothesised that CCS would respond to the hypnotic component of anaesthesia and CI would differentiate between two groups of patients receiving different doses of fentanyl. Twenty-five patients scheduled to undergo elective first-time coronary artery bypass graft surgery were randomised to receive a total fentanyl dose of either 12 μg/kg (fentanyl low dose, FLD) or 24 μg/kg (fentanyl moderate dose, FMD), both administered in two divided doses. Propofol was used for anaesthesia induction and pancuronium for intraoperative paralysis. Hemodynamic management was protocolised using vasoactive drugs. BIS, CCS and CI were simultaneously recorded. Response of the indices (CI, CCS and BIS) to propofol and their differences between the two groups at specific points from anaesthesia induction through to aortic cannulation were investigated. Following propofol induction, CCS and BIS but not CI showed a significant reduction. Following the first dose of fentanyl, CI, CCS and BIS decreased in both groups. Following the second dose of fentanyl, there was a significant reduction in CI in the FLD group but not the FMD group, with no significant change found for BIS or CCS in either group. The BAR monitor demonstrates the potential to monitor the level of hypnosis following anaesthesia induction with propofol via the CCS index and to facilitate the titration of fentanyl as a component of balanced anaesthesia via the CI index.

Evidence indicates Levodopa effects central postural control. As electrophysiological postural control biomarkers, sensory oto-acoustic features were extracted from Electrovestibulography (EVestG) data to identify 20 healthy age and gender matched individuals as Controls from 20 PD subjects before (PDlowmed) and 18 after (PDmed) morning doses of Levodopa. EVestG data was collected using a single tilt stimulus applied in the pitch plane. The extracted features were based on the measured firing pattern, interval histogram and the shape of the average field potential response. An unbiased cross validated classification accuracy of 88%, 88% and 79% was achieved using combinations of 2 features for separating PDlowmed from control, control from PD (combined PDlowmed and PDmed), and PDlowmed from PDmed groups respectively. One feature showed significant correlations (p<0.05) with the Modified Hoehn and Yahr PD staging scale. The results indicate disturbed vestibular function is observed in both the PDmed and PDlowmed conditions, and these are separable. The implication is that Levodopa may also affect peripheral as well as central postural control.

ObjectiveTo assess the introduction of Practical Obstetric Multi-professional Training (PROMPT) into maternity units and evaluate effects on organisational culture and perinatal outcomes.DesignA retrospective cohort study.SettingMaternity units in eight public hospitals in metropolitan and regional Victoria, Australia.PopulationStaff in eight maternity units and a total of 43408 babies born between July 2008 and December 2011.MethodsRepresentatives from eight Victorian hospitals underwent a single day of training (Train the Trainer), to conduct PROMPT. Organisational culture was compared before and after PROMPT. Clinical outcomes were evaluated before, during and after PROMPT.Main outcome measuresThe number of courses run and the proportion of staff trained were determined. Organisational culture was measured using the Safety Attitude Questionnaire. Clinical measures included Apgar scores at 1 and 5minutes (Apgar 1 and Apgar 5), cord lactate, blood loss and length of baby's stay in hospital.ResultsSeven of the eight hospitals conducted PROMPT. Overall about 50% of staff were trained in each year of the study. Significant increases were found in Safety Attitude Questionnaire scores representing domains of teamwork (Hedges' g 0.27, 95% confidence interval [95% CI] 0.13-0.41), safety (Hedges' g 0.28, 95% CI 0.15-0.42) and perception of management (Hedges' g 0.17, 95% CI 0.04-0.31). There were significant improvements in Apgar 1 (OR 0.84, 95% CI 0.77-0.91), cord lactates (odds ratio 0.92, 95% CI 0.85-0.99) and average length of baby's stay in hospital (Hedges' g 0.03, 95% CI 0.01-0.05) during or after training, but no change in Apgar 5 scores or proportion of cases with high blood loss.ConclusionPROMPT can be introduced using the Train the Trainer model. Improvements in organisational culture and some clinical measures were observed following PROMPT.

The aim of this study was to determine the effects of knee extensor muscle damage on shock attenuation patterns during running. Nine well-trained males aged 18 - 22 years completed a treadmill running test before and 48 hours after, performing 60 maximal bilateral maximal eccentric knee flexions on an isokinetic dynamometer. Accelerometers mounted on the distal tibia and head measured impact accelerations (960 Hz), which were synchronized with three-dimensional motion capture (Vicon) of lower limb movement (120 Hz). Eccentric and isometric peak torque measures, perceived ratings of muscle soreness, and finger prick blood samples to assess creatine kinase concentration confirmed that the quadriceps muscle group was significantly damaged in eight of the nine participants 48 hours after exercise. No changes were observed overall in running mechanics (kinematics, impact attenuation) for the eight athletes exhibiting significant muscle damage, with the exception of increased ankle plantarflexion at initial contact (p = 0.03). Further individual analysis (case study approach) indicated that changes in angular kinematics and impact accelerations did occur (p < 0.05), suggesting that individuals may have unique response patterns to exercise-induced muscle damage. Thus, lower limb eccentric training elicits individual biomechanical responses and/or compensation strategies to protect internal structures from excessive impact shock.

Parkinson's disease (PD) is the second largest neurodegenerative disorder worldwide. This disease results from the loss of dopamine producing neurons in parts of the basal ganglia of the brain. Previous studies have shown the involvement of the dopamine system in the basal ganglia in balance control. Sensations of balance in the body are detected by the vestibular apparatus. In this project, electrovestibulography (EVestG) has been used to measure neuronal activity of the vestibular apparatus and nuclei from Parkinson's patients. A wavelet based signal processing technique, a Neural Event Extraction Routine, has been used to extract biomarkers from these EVestG recordings. These measurements appear to be correlated with scores from mobility tests which indicate disease progression and mobility impairment in Parkinson's patients.