Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study.

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Automatic quantification of REM sleep without atonia reliably identifies patients with REM sleep behavior disorder: a possible screening tool?

BACKGROUND: REM Sleep Behavior Disorder (RBD) is characterized by absence of physiological muscle atonia during REM sleep (REM sleep without atonia, RWA). Nigro-striatal dopaminergic impairment is a feature of Parkinson disease (PD) and can be identified in prodromal stages as well, such as idiopathic RBD (iRBD). Aims of this study are to explore the efficacy of an automatic RWA quantification in identifying RBD patients and the correlation between RWA and nigro-striatal dopaminergic function. METHODS: Forty-five iRBD, 46 PD with RBD, 24 PD without RBD patients and 11 healthy controls were enrolled in the Genoa Center (group A) and 25 patients with iRBD (group B) were enrolled in the Danish Center. Group A underwent brain [123I]FP-CIT-SPECT and group B underwent brain [18F]PE2I-PET as measures of nigro-striatal dopaminergic function. Chin muscle activity was recorded in all subjects and analyzed by applying a published automatic algorithm. Correlations between RWA and nigro-striatal dopaminergic function were explored. RESULTS: The automatic quantification of RWA significantly differentiated RBD from non-RBD subjects (AUC = 0.86), although with lower accuracy compared with conventional visual scoring (AUC = 0.99). No significant correlation was found between RWA and nigro-striatal dopaminergic function. CONCLUSION: The automatic quantification of RWA is a reliable tool to identify subjects with RBD and may be used as a first-line screening tool, but without correlations with nigro-striatal dopaminergic functioning.

Comparison of the blink reflex in classical and idiopathic trigeminal neuralgia.

BACKGROUND: Previous findings indicate that the blink reflex is useful to distinguish between primary (classical/idiopathic) and secondary trigeminal neuralgia. No prior studies have investigated whether the blink reflex could identify differences in electrophysiological responses between classical and idiopathic trigeminal neuralgia. With this in mind, we investigated the blink reflex in a cohort of classical and idiopathic trigeminal neuralgia patients. METHODS: Participants were consecutively enrolled in the study. According to magnetic resonance imaging findings, the patients were subgrouped into either classical or idiopathic trigeminal neuralgia. Assessors were blinded to the subgroup and pain side, and the blink reflex was examined to assess R1 and R2 latencies, as well as the area under the curve. RESULTS: The study group constituted of 55 patients with primary trigeminal neuralgia: 25 patients with classical trigeminal neuralgia and 30 patients with idiopathic trigeminal neuralgia. None of the blink reflex latencies (R1 and R2) or the area under the curve significantly differed between the two subgroups when adjusted for age and sex (p > 0.05). CONCLUSIONS: Our findings suggest that the blink reflex cannot be used to differentiate classical and idiopathic trigeminal neuralgia patients, and that both subgroups may share common pathophysiological mechanisms.Trial Registration: ClinicalTrials.gov Identifier: NCT05328661.

Atypical sleep in critically ill patients on mechanical ventilation is associated with increased mortality.

Sleep patterns in critically ill patients' polysomnographic sleep studies (PSG) are severely abnormal. PURPOSE: We aimed to investigate the association of atypical sleep patterns, micro-sleep phenomena (sleep spindles and K-complexes) and rapid eye movement (REM) sleep with intensive care unit (ICU), in-hospital and 90-day mortality in conscious critically ill patients on mechanical ventilation. METHOD: This was a prospective descriptive study. We analysed 52 PSGs recorded in conscious critically ill patients on mechanical ventilation. PSGs were scored according to standard classification when possible. Otherwise, modified classification proposed for scoring sleep in critically ill patients was used. The association of PSG findings with mortality was studied using logistic regression and Weibull model of survival analysis. RESULTS: The presence of atypical sleep patterns in accordance with modified sleep scoring classification was associated with higher odds for ICU mortality (odds ratio 11.63; p = 0.03). The absence of K-complexes was associated with higher odds for ICU mortality (odds ratio 11.63; p = 0.03), while the absence of sleep spindles was associated with higher odds for in-hospital (odds ratio 7.80; p = 0.02) and 90-day mortality (odds ratio 5.51; p = 0.02). Loss of sleep spindles was associated with higher mortality risk with cutoff point 90 days (hazard ratio 3.87; p = 0.03). CONCLUSIONS: The presence of atypical sleep and absence of normal PSG sleep characteristics in conscious critically ill patients on mechanical ventilation indicates involvement of sleep producing brain structures in the pathological process and is associated with poor outcome.

Analysis of automated quantification of motor activity in REM sleep behaviour disorder.

Rapid eye movement (REM) sleep behaviour disorder (RBD) is characterized by dream enactment and REM sleep without atonia. Atonia is evaluated on the basis of visual criteria, but there is a need for more objective, quantitative measurements. We aimed to define and optimize a method for establishing baseline and all other parameters in automatic quantifying submental motor activity during REM sleep. We analysed the electromyographic activity of the submental muscle in polysomnographs of 29 patients with idiopathic RBD (iRBD), 29 controls and 43 Parkinson's (PD) patients. Six adjustable parameters for motor activity were defined. Motor activity was detected and quantified automatically. The optimal parameters for separating RBD patients from controls were investigated by identifying the greatest area under the receiver operating curve from a total of 648 possible combinations. The optimal parameters were validated on PD patients. Automatic baseline estimation improved characterization of atonia during REM sleep, as it eliminates inter/intra-observer variability and can be standardized across diagnostic centres. We found an optimized method for quantifying motor activity during REM sleep. The method was stable and can be used to differentiate RBD from controls and to quantify motor activity during REM sleep in patients with neurodegeneration. No control had more than 30% of REM sleep with increased motor activity; patients with known RBD had as low activity as 4.5%. We developed and applied a sensitive, quantitative, automatic algorithm to evaluate loss of atonia in RBD patients.

U-Sleep: resilient high-frequency sleep staging.

Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.

Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis.

OBJECTIVE: In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients. METHODS: We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed. RESULTS: Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). CONCLUSIONS: Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers. SIGNIFICANCE: Automatic cEEG analysis provides useful information in septic patients.

Steady-state visual evoked potential temporal dynamics reveal correlates of cognitive decline.

OBJECTIVE: A central concern in aging is the preservation of cognitive skills. Tools to detect cognitive decline are sparse. The aim of this study was to ascertain whether cognitive decline is accompanied by alterations in the temporal dynamics of steady-state visual evoked potentials (SSVEPs). METHODS: We included 162 men from the Danish Metropolit birth cohort. Their cognitive trajectory was based on their intelligence test score at youth (age ~18), middle age (age ~56), and late middle age (age ~62). Subjects underwent cognitive tests and steady-state visual stimulation. Temporal dynamics of SSVEPs were assessed in terms of amplitude and phase coherence. RESULTS: The latency and magnitude of the amplitude modulation of the 36-Hz response correlated negatively with subjects' cognition indices. Furthermore, negative cognition index was associated with loss of SSVEPs at 36 Hz, and both 8 Hz and 36 Hz in severe cases. CONCLUSION: Latency and magnitude of gamma frequency SSVEPs increase with cognitive decline. This suggests that the facilitation of SSVEPs first becomes problematic at gamma frequencies, then at alpha frequencies. SIGNIFICANCE: Our data suggests that the temporal dynamics of SSVEPs can be used as an indicator of cognitive impairment. Furthermore, evoked gamma oscillations are especially vulnerable in cognitive decline.

Consciousness in Neurocritical Care Cohort Study Using fMRI and EEG (CONNECT-ME): Protocol for a Longitudinal Prospective Study and a Tertiary Clinical Care Service.

Aims and Objectives: To facilitate individualized assessment of unresponsive patients in the intensive care unit for signs of preserved consciousness after acute brain injury. Background: Physicians and neuroscientists are increasingly recognizing a disturbing dilemma: Brain-injured patients who appear entirely unresponsive at the bedside may show signs of covert consciousness when examined by functional MRI (fMRI) or electroencephalography (EEG). According to a recent meta-analysis, roughly 15% of behaviorally unresponsive brain-injured patients can participate in mental tasks by modifying their brain activity during EEG- or fMRI-based paradigms, suggesting that they are conscious and misdiagnosed. This has major ethical and practical implications, including prognosis, treatment, resource allocation, and end-of-life decisions. However, EEG- or fMRI-based paradigms have so far typically been tested in chronic brain injury. Hence, as a novel approach, CONNECT-ME will import the full range of consciousness paradigms into neurocritical care. Methods: We will assess intensive care patients with acute brain injury for preserved consciousness by serial and multimodal evaluation using active, passive and resting state fMRI and EEG paradigms, as well as state-of-the-art clinical techniques including pupillometry and sophisticated clinical rating scales such as the Coma Recovery Scale-Revised. In addition, we are establishing a biobank (blood, cerebrospinal fluid and brain tissue, where available) to facilitate future genomic and microbiomic research to search for signatures of consciousness recovery. Discussion: We anticipate that this multimodal approach will add vital clinical information, including detection of preserved consciousness in patients previously thought of as unconscious, and improved (i.e., personalized) prognostication of individual patients. Our aim is two-fold: We wish to establish a cutting-edge tertiary care clinical service for unresponsive patients in the intensive care unit and lay the foundation for a fruitful multidisciplinary research environment for the study of consciousness in acute brain injury. Of note, CONNECT-ME will not only enhance our understanding of consciousness disorders in acute brain injury but it will also raise awareness for these patients who, for obvious reasons, have lacked a voice so far. Trial registration: The study is registered with clinicaltrials.org (ClinicalTrials.gov Identifier: NCT02644265).

Melatonin Secretion Pattern in Critically Ill Patients: A Pilot Descriptive Study.

Critically ill patients have abnormal circadian and sleep homeostasis. This may be associated with higher morbidity and mortality. The aims of this pilot study were (1) to describe melatonin secretion in conscious critically ill mechanically ventilated patients and (2) to describe whether melatonin secretion and sleep patterns differed in these patients with and without remifentanil infusion. Eight patients were included. Blood-melatonin was taken every 4th hour, and polysomnography was carried out continually during a 48-hour period. American Academy of Sleep Medicine criteria were used for sleep scoring if sleep patterns were identified; otherwise, Watson's classification was applied. As remifentanil was periodically administered during the study, its effect on melatonin and sleep was assessed. Melatonin secretion in these patients followed a phase-delayed diurnal curve. We did not observe any effect of remifentanil on melatonin secretion. We found that the risk of atypical sleep compared to normal sleep was significantly lower (p < 0.001) under remifentanil infusion. Rapid Eye Movement (REM) sleep was only observed during the nonsedation period. We found preserved diurnal pattern of melatonin secretion in these patients. Remifentanil did not affect melatonin secretion but was associated with lower risk of atypical sleep pattern. REM sleep was only registered during the period of nonsedation.

Sleep in intensive care unit: The role of environment.

PURPOSE: To determine if improving intensive care unit (ICU) environment would enhance sleep quality, assessed by polysomnography (PSG), in critically ill mechanically ventilated patients. MATERIALS AND METHODS: Randomized controlled trial, crossover design. The night intervention "quiet routine" protocol was directed toward improving ICU environment between 10pm and 6am. Noise levels during control and intervention nights were recorded. Patients on mechanical ventilation and able to give consent were eligible for the study. We monitored sleep by PSG.The standard (American Association of Sleep Medicine) sleep scoring criteria were insufficient for the assessment of polysomnograms. Modified classification for sleep scoring in critically ill patients, suggested by Watson et al. (Crit Care Med 2013;41:1958-1967), was used. RESULTS: Sound level analysis showed insignificant effect of the intervention on noise reduction (P=.3). The analysis of PSGs revealed that only 53% of the patients had identifiable characteristics of normal sleep, whereas 47% showed only pathologic patterns. CONCLUSIONS: Characteristics of normal sleep were absent in many of the PSG recordings in these critically ill patients. We were not able to further reduce the already existing low noise levels in the ICU and did not find any association between the environmental intervention and the presence of normal sleep characteristics in the PSG.

Sleep spindle density in narcolepsy.

BACKGROUND: Patients with narcolepsy type 1 (NT1) show alterations in sleep stage transitions, rapid-eye-movement (REM) and non-REM sleep due to the loss of hypocretinergic signaling. However, the sleep microstructure has not yet been evaluated in these patients. We aimed to evaluate whether the sleep spindle (SS) density is altered in patients with NT1 compared to controls and patients with narcolepsy type 2 (NT2). METHODS: All-night polysomnographic recordings from 28 NT1 patients, 19 NT2 patients, 20 controls (C) with narcolepsy-like symptoms, but with normal cerebrospinal fluid hypocretin levels and multiple sleep latency tests, and 18 healthy controls (HC) were included. Unspecified, slow, and fast SS were automatically detected, and SS densities were defined as number per minute and were computed across sleep stages and sleep cycles. The between-cycle trends of SS densities in N2 and NREM sleep were evaluated within and between groups. RESULTS: Between-group comparisons in sleep stages revealed no significant differences in any type of SS. Within-group analyses of the SS trends revealed significant decreasing trends for NT1, HC, and C between first and last sleep cycle. Between-group analyses of SS trends between first and last sleep cycle revealed that NT2 differ from NT1 patients in the unspecified SS density in NREM sleep, and from HC in the slow SS density in N2 sleep. CONCLUSIONS: SS activity is preserved in NT1, suggesting that the ascending neurons to thalamic activation of SS are not significantly affected by the hypocretinergic system. NT2 patients show an abnormal pattern of SS distribution.

Novel method for evaluation of eye movements in patients with narcolepsy.

BACKGROUND: Narcolepsy causes abnormalities in the control of wake-sleep, non-rapid-eye-movement (non-REM) sleep and REM sleep, which includes specific eye movements (EMs). In this study, we aim to evaluate EM characteristics in narcolepsy as compared to controls using an automated detector. METHODS: We developed a data-driven method to detect EMs during sleep based on two EOG signals recorded as part of a polysomnography (PSG). The method was optimized using the manually scored hypnograms from 36 control subjects. The detector was applied on a clinical sample with subjects suspected for central hypersomnias. Based on PSG, multiple sleep latency test and cerebrospinal fluid hypocretin-1 measures, they were divided into clinical controls (N = 20), narcolepsy type 2 (NT2, N = 19), and narcolepsy type 1 (NT1, N = 28). We investigated the distribution of EMs across sleep stages and cycles. RESULTS: NT1 patients had significantly less EMs during wake, N1, and N2 sleep and more EMs during REM sleep compared to clinical controls, and significantly less EMs during wake and N1 sleep compared to NT2 patients. Furthermore, NT1 patients showed less EMs during NREM sleep in the first sleep cycle and more EMs during NREM sleep in the second sleep cycle compared to clinical controls and NT2 patients. CONCLUSIONS: NT1 patients show an altered distribution of EMs across sleep stages and cycles compared to NT2 patients and clinical controls, suggesting that EMs are directly or indirectly controlled by the hypocretinergic system. A data-driven EM detector may contribute to the evaluation of narcolepsy and other disorders involving the control of EMs.

Visual steady state in relation to age and cognitive function.

Neocortical gamma activity is crucial for sensory perception and cognition. This study examines the value of using non-task stimulation-induced EEG oscillations to predict cognitive status in a birth cohort of healthy Danish males (Metropolit) with varying cognitive ability. In particular, we examine the steady-state VEP power response (SSVEP-PR) in the alpha (8Hz) and gamma (36Hz) bands in 54 males (avg. age: 62.0 years) and compare these with 10 young healthy participants (avg. age 27.6 years). Furthermore, we correlate the individual alpha-to-gamma difference in relative visual-area power (ΔRV) with cognitive scores for the older adults. We find that ΔRV decrease with age by just over one standard deviation when comparing young with old participants (p<0.01). Furthermore, intelligence is significantly negatively correlated with ΔRV in the older adult cohort, even when processing speed, global cognition, executive function, memory, and education (p<0.05). In our preferred specification, an increase in ΔRV of one standard deviation is associated with a reduction in intelligence of 48% of a standard deviation (p<0.01). Finally, we conclude that the difference in cerebral rhythmic activity between the alpha and gamma bands is associated with age and cognitive status, and that ΔRV therefore provide a non-subjective clinical tool with which to examine cognitive status in old age.

Increased Motor Activity During REM Sleep Is Linked with Dopamine Function in Idiopathic REM Sleep Behavior Disorder and Parkinson Disease.

STUDY OBJECTIVES: Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by impaired motor inhibition during REM sleep, and dream-enacting behavior. RBD is especially associated with α-synucleinopathies, such as Parkinson disease (PD). Follow-up studies have shown that patients with idiopathic RBD (iRBD) have an increased risk of developing an α-synucleinopathy in later life. Although abundant studies have shown that degeneration of the nigrostriatal dopaminergic system is associated with daytime motor function in Parkinson disease, only few studies have investigated the relation between this system and electromyographic (EMG) activity during sleep. The objective of this study was to investigate the relationship between the nigrostriatal dopamine system and muscle activity during sleep in iRBD and PD. METHODS: 10 iRBD patients, 10 PD patients with PD, 10 PD patients without RBD, and 10 healthy controls were included and assessed with (123)I-N-omega-fluoropropyl-2-beta-carboxymethoxy-3beta-(4-iodophenyl) nortropane ((123)I-FP-CIT) Single-photon emission computed tomography (SPECT) scanning ((123)I-FP-CIT SPECT), neurological examination, and polysomnography. RESULTS: iRBD patients and PD patients with RBD had increased EMG-activity compared to healthy controls. (123)I-FP-CIT uptake in the putamen-region was highest in controls, followed by iRBD patients, and lowest in PD patients. In iRBD patients, EMG-activity in the mentalis muscle was correlated to (123)I-FP-CIT uptake in the putamen. In PD patients, EMG-activity was correlated to anti-Parkinson medication. CONCLUSIONS: Our results support the hypothesis that increased EMG-activity during REM sleep is at least partly linked to the nigrostriatal dopamine system in iRBD, and with dopamine function in PD.

Sleep spindle alterations in patients with Parkinson's disease.

UNLABELLED: The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinson's disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. CONTROLS: We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).

Early automatic detection of Parkinson's disease based on sleep recordings.

SUMMARY: Idiopathic rapid-eye-movement (REM) sleep behavior disorder (iRBD) is most likely the earliest sign of Parkinson's Disease (PD) and is characterized by REM sleep without atonia (RSWA) and consequently increased muscle activity. However, some muscle twitching in normal subjects occurs during REM sleep. PURPOSE: There are no generally accepted methods for evaluation of this activity and a normal range has not been established. Consequently, there is a need for objective criteria. METHOD: In this study we propose a full-automatic method for detection of RSWA. REM sleep identification was based on the electroencephalography and electrooculography channels, while the abnormal high muscle activity was detected from the electromyography channels, in this case the submentalis combined with left and right anterior tibialis. RSWA was identified by considering it an outlier problem, in which the number of outliers during REM sleep was used as a quantitative measure of muscle activity. RESULTS: The proposed method was able to automatically separate all iRBD test subjects from healthy elderly controls and subjects with periodic limb movement disorder. CONCLUSION: The proposed work is considered a potential automatic method for early detection of PD.

Prepulse inhibition is associated with attention, processing speed, and 123I-FP-CIT SPECT in Parkinson's disease.

BACKGROUND: Prepulse inhibition is a measure of sensorimotor gating, which reflects the ability to filter or 'gate' irrelevant information. Prepulse inhibition is dramatically altered in basal ganglia disorders associated with dysfunction in the midbrain dopaminergic system, and corresponding cognitive information processing deficits such as slowed processing speed. Parkinson's disease is characterised by the degeneration of the midbrain dopaminergic system and is associated with cognitive dysfunction, including slowed information processing. Although sensorimotor processes in Parkinson's disease have been extensively studied in relation to motor function, less is known about the potential role of sensorimotor processes in cognitive function. OBJECTIVE: We investigated the relationship between prepulse inhibition, cognition and nigrostriatal dysfunction, as measured with 123I-FP-CIT-SPECT scanning, in patients with Parkinson's disease. METHODS: 38 Parkinson patients were assessed with prepulse inhibition, neuropsychological tests, and neurological investigation. A subset of these patients underwent 123I-FP-CIT-SPECT scanning. RESULTS: Patients with a higher level of prepulse inhibition performed better on cognitive measures tapping attention and processing speed than patients with a lower level of prepulse inhibition. Furthermore, there were significant correlations between prepulse inhibition and 123I-FP-CIT uptake in the striatum. CONCLUSIONS: Our results suggest that the level of prepulse inhibition is related to the efficiency of information processing in Parkinson's disease, and to the density of dopamine transporters in the striatum.

Sensorimotor gating deficits in multiple system atrophy: comparison with Parkinson's disease and idiopathic REM sleep behavior disorder.

BACKGROUND: Prepulse inhibition (PPI) of the auditory blink reflex is a measure of sensorimotor gating, which reflects an organism's ability to filter out irrelevant sensory information. PPI has never been studied in patients with multiple system atrophy (MSA), although sensorimotor deficits are frequently associated with synucleinopathies. We investigated whether alterations in PPI were more pronounced in MSA compared with Parkinson's disease (PD), idiopathic rapid eye movement sleep behavior disorder (iRBD) and healthy controls. METHODS: 10 patients with MSA, 12 patients with iRBD, 40 patients with PD, and 20 healthy controls completed the study. A passive acoustic prepulse inhibition paradigm was applied with prepulses 5 dB and 15 dB above background noise at 30-, 60-, 120- and 300-ms intervals. RESULTS: Non-parametric analyses showed that MSA patients had significantly lower prepulse inhibition, as measured with max-amplitude, than PD patients and iRBD patients on the 60 ms-85 dB and 120 ms-85 dB inter-stimulus intervals. The same relation was found when using area under the curve. No differences were found between groups for the 30 ms-85 dB and 300 ms-85 dB. Furthermore, blink reflex characteristics such as habituation did not differ between patients and controls. CONCLUSIONS: The present study showed that sensorimotor gating, as measured with PPI, is markedly reduced in MSA. This may be due to the pronounced severity of striatal and brainstem dysfunction, as well as the degeneration of other structures related to the PPI modulating pathways in MSA. PPI may be a non-invasive neurophysiological measure that can aid in the differential diagnosis between PD and MSA.