Neonatal quantitative electroencephalography and long-term outcomes: a systematic review.

AIM: To evaluate quantitative electroencephalogram (EEG) measures as predictors of long-term neurodevelopmental outcome in infants with a postconceptional age below 46 weeks, including typically developing infants born at term, infants with heterogeneous underlying pathologies, and infants born preterm. METHOD: A comprehensive search was performed using PubMed, Embase, and Web of Science from study inception up to 8th January 2021. Studies that examined associations between neonatal quantitative EEG measures, based on conventional and amplitude-integrated EEG, and standardized neurodevelopmental outcomes at 2 years of age or older were reviewed. Significant associations between neonatal quantitative EEG and long-term outcome measures were grouped into one or more of the following categories: cognitive outcome; motor outcome; composite scores; and other standardized outcome assessments. RESULTS: Twenty-four out of 1740 studies were included. Multiple studies showed that conventional EEG-based absolute power in the delta, theta, alpha, and beta frequency bands and conventional and amplitude-integrated EEG-related amplitudes were positively associated with favourable long-term outcome across several domains, including cognition and motor performance. Furthermore, a lower presence of discontinuous background pattern was also associated with favourable outcomes. However, interpretation of the results is limited by heterogeneity in study design and populations. INTERPRETATION: Neonatal quantitative EEG measures may be used as prognostic biomarkers to identify those infants who will develop long-term difficulties and who might benefit from early interventions.

Preoperative Electroencephalography-Based Machine Learning Predicts Cognitive Deterioration After Subthalamic Deep Brain Stimulation.

BACKGROUND: Subthalamic deep brain stimulation (STN DBS) may relieve refractory motor complications in Parkinson's disease (PD) patients. Despite careful screening, it remains difficult to determine severity of alpha-synucleinopathy involvement which influences the risk of postoperative complications including cognitive deterioration. Quantitative electroencephalography (qEEG) reflects cognitive dysfunction in PD and may provide biomarkers of postoperative cognitive decline. OBJECTIVE: To develop an automated machine learning model based on preoperative EEG data to predict cognitive deterioration 1 year after STN DBS. METHODS: Sixty DBS candidates were included; 42 patients had available preoperative EEGs to compute a fully automated machine learning model. Movement Disorder Society criteria classified patients as cognitively stable or deteriorated at 1-year follow-up. A total of 16,674 EEG-features were extracted per patient; a Boruta algorithm selected EEG-features to reflect representative neurophysiological signatures for each class. A random forest classifier with 10-fold cross-validation with Bayesian optimization provided class-differentiation. RESULTS: Tweny-five patients were classified as cognitively stable and 17 patients demonstrated cognitive decline. The model differentiated classes with a mean (SD) accuracy of 0.88 (0.05), with a positive predictive value of 91.4% (95% CI 82.9, 95.9) and negative predictive value of 85.0% (95% CI 81.9, 91.4). Predicted probabilities between classes were highly differential (hazard ratio 11.14 [95% CI 7.25, 17.12]); the risk of cognitive decline in patients with high probabilities of being prognosticated as cognitively stable (>0.5) was very limited. CONCLUSIONS: Preoperative EEGs can predict cognitive deterioration after STN DBS with high accuracy. Cortical neurophysiological alterations may indicate future cognitive decline and can be used as biomarkers during the DBS screening. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Khodakarami, H., et al., Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor. Sensors 2019, 19, 5153.

The authors wish to make the following erratum to this paper [...].

A systematic review of MEG-based studies in Parkinson's disease: The motor system and beyond.

Parkinson's disease (PD) is accompanied by functional changes throughout the brain, including changes in the electromagnetic activity recorded with magnetoencephalography (MEG). An integrated overview of these changes, its relationship with clinical symptoms, and the influence of treatment is currently missing. Therefore, we systematically reviewed the MEG studies that have examined oscillatory activity and functional connectivity in the PD-affected brain. The available articles could be separated into motor network-focused and whole-brain focused studies. Motor network studies revealed PD-related changes in beta band (13-30 Hz) neurophysiological activity within and between several of its components, although it remains elusive to what extent these changes underlie clinical motor symptoms. In whole-brain studies PD-related oscillatory slowing and decrease in functional connectivity correlated with cognitive decline and less strongly with other markers of disease progression. Both approaches offer a different perspective on PD-specific disease mechanisms and could therefore complement each other. Combining the merits of both approaches will improve the setup and interpretation of future studies, which is essential for a better understanding of the disease process itself and the pathophysiological mechanisms underlying specific PD symptoms, as well as for the potential to use MEG in clinical care.

Prediction of the Levodopa Challenge Test in Parkinson's Disease Using Data from a Wrist-Worn Sensor.

The response to levodopa (LR) is important for managing Parkinson's Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson's Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies.

Predicting Motor Outcome and Quality of Life After Subthalamic Deep Brain Stimulation for Parkinson's Disease: The Role of Standard Screening Measures and Wearable-Data.

BACKGROUND: Standardized screening for subthalamic deep brain stimulation (STN DBS) in Parkinson's disease (PD) patients is crucial to determine eligibility, but its utility to predict postoperative outcomes in eligible patients is inconclusive. It is unknown whether wearable data can contribute to this aim. OBJECTIVE: To evaluate the utility of universal components incorporated in the DBS screening, complemented by a wearable sensor, to predict motor outcomes and Quality of life (QoL) one year after STN DBS surgery. METHODS: Consecutive patients were included in the OPTIMIST cohort study from two DBS centers. Standardized assessments included a preoperative Levodopa Challenge Test (LCT), and questionnaires on QoL and non-motor symptoms including cognition, psychiatric symptoms, impulsiveness, autonomic symptoms, and sleeping problems. Moreover, an ambulatory wearable sensor (Parkinson Kinetigraph (PKG)) was used. Postoperative assessments were similar and also included a Stimulation Challenge Test to determine DBS effects on motor function. RESULTS: Eighty-three patients were included (median (interquartile range) age 63 (56-68) years, 36% female). Med-OFF (Stim-OFF) motor severity deteriorated indicating disease progression, but patients significantly improved in terms of Med-ON (Stim-ON) motor function, motor fluctuations, QoL, and most non-motor domains. Motor outcomes were not predicted by preoperative tests, including covariates of either LCT or PKG. Postoperative QoL was predicted by better preoperative QoL, lower age, and more preoperative impulsiveness scores in multivariate models. CONCLUSION: Data from the DBS screening including wearable data do not predict postoperative motor outcome at one year. Post-DBS QoL appears primarily driven by non-motor symptoms, rather than by motor improvement.

Assessment of Parkinson's Disease Severity From Videos Using Deep Architectures.

Parkinson's disease (PD) diagnosis is based on clinical criteria, i.e., bradykinesia, rest tremor, rigidity, etc. Assessment of the severity of PD symptoms with clinical rating scales, however, is subject to inter-rater variability. In this paper, we propose a deep learning based automatic PD diagnosis method using videos to assist the diagnosis in clinical practices. We deploy a 3D Convolutional Neural Network (CNN) as the baseline approach for the PD severity classification and show the effectiveness. Due to the lack of data in clinical field, we explore the possibility of transfer learning from non-medical dataset and show that PD severity classification can benefit from it. To bridge the domain discrepancy between medical and non-medical datasets, we let the network focus more on the subtle temporal visual cues, i.e., the frequency of tremors, by designing a Temporal Self-Attention (TSA) mechanism. Seven tasks from the Movement Disorders Society - Unified PD rating scale (MDS-UPDRS) part III are investigated, which reveal the symptoms of bradykinesia and postural tremors. Furthermore, we propose a multi-domain learning method to predict the patient-level PD severity through task-assembling. We show the effectiveness of TSA and task-assembling method on our PD video dataset empirically. We achieve the best MCC of 0.55 on binary task-level and 0.39 on three-class patient-level classification.

Patient-Related Factors Influencing Caregiver Burden in Parkinson's Disease Patients: Comparison of Effects Before and After Deep Brain Stimulation.

BACKGROUND: Caregivers of Parkinson's disease (PD) patients provide important support during the pre- and postoperative phase of deep brain stimulation (DBS). High levels of caregiver burden have been reported after DBS. However, a comparison between preoperative and postoperative burden and associated factors has been insufficiently studied. OBJECTIVE: To investigate the influence of DBS on caregiver burden, and to identify the differential impact of patient-related factors on caregiver burden before and after DBS. METHODS: Consecutive patients referred for DBS eligibility screening or during one-year follow-up assessments were included. Caregiver burden was measured with the short Zarit Burden Interview (ZBI-12). Inverse Probability Weighting (IPW) was used to compare caregiver burden between preoperative and postoperative assessments. RESULTS: We included 47 patients (24 screening, 23 follow-up) (median age 65 years, 29.4% female sex). DBS did not impact caregiver burden (screening: median ZBI-12 9.5 (IQR 3.25, 16.75); follow-up median ZBI-12 6 (IQR 4, 14); IPW-coefficient 0.57 (95% CI -2.75, 3.89)). Worse caregiver burden during DBS screening was associated with worse patient-related scores on depressive symptoms, anxiety, QoL, and impulsiveness. Worse scores on depressive symptoms, anxiety, apathy, postural-instability-gait-disorder, and QoL were associated with worse caregiver burden at one-year follow-up. CONCLUSION: DBS appears not associated with changes in caregiver burden. Various symptoms are valued differently between screening and follow-up assessments in terms of caregiver burden. Early recognition of caregivers "at risk" may improve guidance of patient-caregiver dyads throughout the DBS process.

Intraoperative vs. Postoperative Side-Effects-Thresholds During Pallidal and Thalamic DBS.

Background: It is currently unknown whether results from intraoperative test stimulation of two types of Deep Brain Stimulation (DBS), either during awake pallidal (GPi) or thalamic (Vim), are comparable to the results generated by chronic stimulation through the definitive lead. Objective: To determine whether side-effects-thresholds from intraoperative test stimulation are indicative of postoperative stimulation findings. Methods: Records of consecutive patients who received GPi or Vim were analyzed. Thresholds for the induction of either capsular or non-capsular side-effects were compared at matched depths and at group-level. Results: Records of fifty-two patients were analyzed (20 GPis, 75 Vims). The induction of side-effects was not significantly different between intraoperative and postoperative assessments at matched depths, although a large variability was observed (capsular: GPi DBS: p = 0.79; Vim DBS: p = 0.68); non-capsular: GPi DBS: p = 0.20; and Vim DBS: p = 0.35). Linear mixed-effect models revealed no differences between intraoperative and postoperative assessments, although the Vim had significantly lower thresholds (capsular side-effects p = 0.01, non-capsular side-effects p < 0.01). Unpaired survival analyses demonstrated lower intraoperative than postoperative thresholds for capsular side-effects in patients under GPi DBS (p = 0.01), while higher intraoperative thresholds for non-capsular side-effects in patients under Vim DBS (p = 0.01). Conclusion: There were no significant differences between intraoperative and postoperative assessments of GPi and Vim DBS, although thresholds cannot be directly extrapolated at an individual level due to high variability.

Effect of deep brain stimulation on caregivers of patients with Parkinson's disease: A systematic review.

BACKGROUND: Caregivers of patients with Parkinson's Disease (PD) often provide important support in the pre- and postoperative phase of Deep Brain Stimulation (DBS). DBS-associated changes of patient-functioning may affect caregiver wellbeing and impact the support system. Factors influencing caregiver-wellbeing under these circumstances are incompletely known. OBJECTIVE: to systematically review studies of sufficient methodological quality on the impact of DBS on caregivers of PD patients. METHODS: using PRISMA guidelines, major databases were searched up to May 2020. Five subcategories were identified: Caregiver burden, Caregiver cognitive and psychiatric functioning, Caregiver Quality of Life (QoL), Marital Satisfaction/Conflicts, and Caregiver Satisfaction. Quality was assessed using an in-house checklist. RESULTS: 293 studies were identified; 12 were ultimately included. Caregiver burden, psychiatric and cognitive functioning and QoL remained relatively unchanged. Results on marital satisfaction/conflicts were contrasting: an increase in marital conflicts despite improved relationship quality scores DBS. Caregiver satisfaction with surgery was low with 50-58% of caregivers being disappointed with DBS outcomes. Concerning caregiver related factors: a higher preoperative caregiver QoL, younger age, lower scores on psychiatric rating scales, and more favourable preoperative relationship quality scores, were associated with better caregiver wellbeing. A favourable patient-profile includes younger age and age-at-onset, shorter disease duration, lower medication requirements, and lower scores on psychiatric rating scales. CONCLUSION: Although most patient- and caregiver-related subdomains remained unchanged after DBS, dissatisfaction among caregivers and marital problems may constitute a large risk for a well-functioning patient-caregiver dyad. Early recognition of potential problem situations may improve post-DBS care for both patients and caregivers.

The degree of prematurity affects functional brain activity in preterm born children at school-age: An EEG study.

Prematurely born children are at higher risk for long-term adverse motor and cognitive outcomes. The aim of this paper was to compare quantitative measures derived from electroencephalography (EEG) between extremely (EP) and very prematurely (VP) born children at 9-10 years of age. Fifty-five children born <32 weeks' of gestation underwent EEG at 9-10 years of age and were assessed for motor development and cognitive outcome. Relative frequency power and functional connectivity, as measured by the Phase Lag Index (PLI), were calculated for all frequency bands. Per subject, power spectrum and functional connectivity results were averaged over all channels and pairwise PLI values to explore differences in global frequency power and functional connectivity between EP and VP children. Brain networks were constructed for the upper alpha frequency band using the Minimum Spanning Tree method and were compared between EP and VP children. In addition, the relationships between upper alpha quantitative EEG results and cognitive and motor outcomes were investigated. Relative power and functional connectivity were significantly higher in VP than EP children in the upper alpha frequency band, and VP children had more integrated networks. A strong positive correlation was found between relative upper alpha power and motor outcome whilst controlling for gestational age, age during EEG recording, and gender (ρ = 0.493, p = 0.004). These results suggest that 9-10 years after birth, the effects of the degree of prematurity can be observed in terms of alterations in functional brain activity and that motor deficits are associated with decreases in relative upper alpha power.

Clinical correlates of quantitative EEG in Parkinson disease: A systematic review.

OBJECTIVE: To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD). METHODS: Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics. RESULTS: Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking. CONCLUSION: qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.

Quantitative EEG reflects non-dopaminergic disease severity in Parkinson's disease.

OBJECTIVE: In Parkinson's Disease (PD), measures of non-dopaminergic systems involvement may reflect disease severity and therefore contribute to patient-selection for Deep Brain Stimulation (DBS). There is currently no determinant for non-dopaminergic disease severity. In this exploratory study, we investigated whether quantitative EEG reflects non-dopaminergic disease severity in PD. METHODS: Sixty-three consecutive PD patients screened for DBS were included (mean age 62.4 ±â€¯7.2 years, 32% females). Relative spectral powers and the Phase-Lag-Index (PLI) reflecting functional connectivity were analysed on routine EEGs. Non-dopaminergic disease severity was quantified using the SENS-PD score and its subdomains; motor-severity was quantified using the MDS-UPDRS III. RESULTS: The SENS-PD composite score correlated with a spectral ratio ((δ + θ)/(α1 + α2 + ß) powers) (global spectral ratio Pearson's r = 0.4, 95% Confidence Interval (95%CI) 0.1-0.6), and PLI in the α2 band (10-13 Hz) (r = -0.3, 95%CI -0.5 to -0.1). These correlations seem driven by the subdomains cognition and psychotic symptoms. MDS-UPDRS III was not significantly correlated with EEG parameters. CONCLUSIONS: EEG slowing and reduced functional connectivity in the α2 band were associated with non-dopaminergic disease severity in PD. SIGNIFICANCE: The described EEG parameters may have complementary utility as determinants of non-dopaminergic involvement in PD.

Importance of Reperfusion Status after Intra-Arterial Thrombectomy for Prediction of Outcome in Anterior Circulation Large Vessel Stroke.

BACKGROUND: Reperfusion status after intra-arterial thrombectomy (IAT) is a critical predictor of functional outcome after acute ischemic stroke. However, most prognostic models have not included a detailed assessment of reperfusion status after IAT. OBJECTIVE: The aim of this work was to assess the association between successful reperfusion and clinical outcome. METHODS: Clinical, radiological, and procedural variables of patients treated with IAT were extracted from our prospective stroke registry. The association with functional outcome using the modified Rankin Scale (mRS) after 3 months was assessed using multivariable logistic regression. An extension of the modified TICI score, eTICI, was used to classify reperfusion status. The prognostic value of reperfusion status after IAT in addition to age, stroke severity, imaging characteristics, treatment with intravenous thrombolysis, and time from symptom onset to the end of IAT was assessed with logistic regression and summarized with receiver operating characteristic curves. RESULTS: In total, 119 patients were included (mean age 66 years). In multivariable analysis, age >80 years (OR 6.8, 95% CI 1.2-39.8), NIHSS at presentation >15 (OR 7.3, 95% CI 2.3-23.5), and incomplete reperfusion status (eTICI score <2C; OR 10.3, 95% CI 3.5-30.6) were the strongest predictors of a poor outcome (mRS 3-6). Adding reperfusion status to the model improved the prognostic accuracy (AUC 0.88, 95% CI 0.91-0.94). Our results indicate a large difference between using an eTICI cutoff of ≥2C versus ≥2B: a cutoff ≥2C improved the predictive value for a good clinical outcome (2C: positive predictive value, PPV, 0.78; 2B: PPV 0.32). CONCLUSION: Our results promote using reperfusion status for assessing prognosis in ischemic stroke patients treated with IAT. A model using eTICI ≥2C had greater PPV than eTICI ≥2B and could improve prognostic accuracy.

Predictors of Quality of Life in Acromegaly: No Consensus on Biochemical Parameters.

BACKGROUND: Quality of life (QoL) in patients with acromegaly is reduced irrespective of disease state. The contributions of multifactorial determinants of QoL in several disease stages are presently not well known. OBJECTIVE: To systematically review predictors of QoL in acromegalic patients. METHODS: Main databases were systematically searched using predefined search terms for potentially relevant articles up to January 2017. Inclusion criteria included separate acromegaly cohort, non-hereditary acromegaly, QoL as study parameter with clearly described method of measurement and quantitative results, N ≥ 10 patients, article in English and adult patients only. Data extraction was performed by two independent reviewers; studies were included using the PRISMA flow diagram. RESULTS: We identified 1,162 studies; 51 studies met the inclusion criteria: 31 cross-sectional observational studies [mean AcroQoL score 62.7 (range 46.6-87.0, n = 1,597)], 9 had a longitudinal component [mean baseline AcroQoL score 61.4 (range 54.3-69.0, n = 386)], and 15 were intervention studies [mean baseline AcroQoL score 58.6 (range 52.2-75.3, n = 521)]. Disease-activity reflected by biochemical control measures yielded mixed, and therefore inconclusive results with respect to their effect on QoL. Addition of pegvisomant to somatostatin analogs and start of lanreotide autogel resulted in improvement in QoL. Data from intervention studies on other treatment modalities were too limited to draw conclusions on the effects of these modalities on QoL. Interestingly, higher BMI and greater degree of depression showed consistently negative associations with QoL. Hypopituitarism was not significantly correlated with QoL in acromegaly. CONCLUSION: At present, there is insufficient published data to support that biochemical control, or treatment of acromegaly in general, is associated with improved QoL. Studies with somatostatin receptor ligand treatment, i.e., particularly lanreotide autogel and pegvisomant have shown improved QoL, but consensus on the correlation with biochemical control is missing. Longitudinal studies investigating predictors in treatment-naive patients and their follow-up after therapeutic interventions are lacking but are urgently needed. Other factors, i.e., depression and obesity were identified from cross-sectional cohort studies as consistent factors associated with poor QoL. Perhaps treatment strategies of acromegaly patients should not only focus on normalizing biochemical markers but emphasize improvement of QoL by alternative interventions such as psychosocial or weight lowering interventions.

Postural instability and gait are associated with severity and prognosis of Parkinson disease.

OBJECTIVE: Differences in disease progression in Parkinson disease (PD) have variously been attributed to 2 motor subtypes: tremor-dominant (TD) and postural instability and gait difficulty (PIGD)-dominant (PG). We evaluated the role of these phenotypic variants in severity and progression of nondopaminergic manifestations of PD and motor complications. METHODS: Linear mixed models were applied to data from the Profiling Parkinson's disease (PROPARK) cohort (n = 396) to evaluate the effect of motor subtype on severity and progression of cognitive impairment (Scales for Outcomes in Parkinson's disease [SCOPA]-Cognition [SCOPA-COG]), depression (Hospital Anxiety and Depression Scale [HADS]), autonomic dysfunction (SCOPA-Autonomic [SCOPA-AUT]), excessive daytime sleepiness, psychotic symptoms (SCOPA-Psychiatric Complications [SCOPA-PC]), and motor complications. In first analyses, subtype as determined by the commonly used ratio of tremor over PIGD score was entered as a factor, whereas in second analyses separate tremor and PIGD scores were used. Results were verified in an independent cohort (Estudio Longitudinal de Pacientes con Enfermedad de Parkinson [ELEP]; n = 365). RESULTS: The first analyses showed that PG subtype patients had worse SCOPA-COG, HADS, SCOPA-AUT, SCOPA-PC, and motor complications scores, and exhibited faster progression on the SCOPA-COG. The second analyses showed that only higher PIGD scores were associated with worse scores for these variables; tremor score was not associated with severity or progression of any symptom. Analyses in the independent cohort yielded similar results. CONCLUSIONS: In contrast to PIGD, which consistently was associated with greater severity of nondopaminergic symptoms, there was no evidence of a benign effect of tremor. Our findings do not support the use of the TD subtype as a prognostic trait in PD. The results showed that severity of PIGD is a useful indicator of severity and prognosis in PD by itself.