Freezing of gait in Parkinson’s disease is related to imbalanced stopping-related cortical activity

Brain Communications, Journal Year: 2024, Volume and Issue: 6(5)

Published: Jan. 1, 2024

Abstract Freezing of gait, characterized by involuntary interruptions walking, is a debilitating motor symptom Parkinson's disease that restricts people's autonomy. Previous brain imaging studies investigating the mechanisms underlying freezing were restricted to scan people in supine positions and yielded conflicting theories regarding role supplementary area other cortical regions. We used functional near-infrared spectroscopy investigate haemodynamics related freely moving people. measured activity over multiple motor-related areas 23 persons with who experienced daily (‘freezers’) 22 age-matched controls during freezing-provoking tasks including turning doorway passing, voluntary stops actual freezing. Crucially, we corrected signals for confounds walking. first compared between freezers without (i.e. passing) stops. Secondly, within freezers, freezing, stopping First, show passing (without freezing) resemble both groups involving activation prefrontal cortex, known their inhibiting actions. During these tasks, displayed higher premotor than controls. that, events, cortex was lower stopping. The relation (turning may explain susceptibility trigger activating mechanism. Besides, stopping-related seems be out balance freezers. In this paper, postulate results from paroxysmal imbalance thereby extending upon current pathophysiology.

Language: Английский

---

Neuroprotection of the Perinatal Brain by Early Information of Cerebral Oxygenation and Perfusion Patterns

International Journal of Molecular Sciences, Journal Year: 2021, Volume and Issue: 22(10), P. 5389 - 5389

Published: May 20, 2021

Abnormal patterns of cerebral perfusion/oxygenation are associated with neuronal damage. In preterm neonates, hypoxemia, hypo-/hypercapnia and lack autoregulation related to peri-intraventricular hemorrhages white matter injury. Reperfusion damage after perinatal hypoxic ischemia in term neonates seems hyperoxygenation. Since biological tissue is transparent for near infrared (NIR) light, NIR-spectroscopy (NIRS) a noninvasive bedside tool monitor brain oxygenation perfusion. This review focuses on early assessment guiding abnormal oxygenation/perfusion possibly reduce infants, helps decide whether or not therapy (hypothermia) add-on therapies should be considered. Further NIRS-related technical advances such as the use (functional) NIRS allowing simultaneous estimation integrating heart rate, respiration rate monitoring will discussed.

Language: Английский

---

Capturing postural blood pressure dynamics with near-infrared spectroscopy-measured cerebral oxygenation

GeroScience, Journal Year: 2023, Volume and Issue: 45(4), P. 2643 - 2657

Published: April 12, 2023

Abstract Orthostatic hypotension (OH) is highly prevalent in older adults and associated with dizziness, falls, lower physical cognitive function, cardiovascular disease, mortality. OH currently diagnosed a clinical setting single-time point cuff measurements. Continuous blood pressure (BP) devices can measure dynamics but cannot be used for daily life monitoring. Near-infrared spectroscopy (NIRS) has potential diagnostic value measuring cerebral oxygenation continuously over longer time period, this needs further validation. This study aimed to compare NIRS-measured (cerebral) continuous BP transcranial Doppler-measured velocity (CBv) during postural changes. cross-sectional included 41 participants between 20 88 years old. BP, CBv, (long channels) superficial (short oxygenated hemoglobin (O 2 Hb) were measured various Pearson correlations O Hb calculated curves specific characteristics (maximum drop amplitude recovery). only showed good curve-based (0.58–0.75) the initial 30 s after standing up. Early (30–40 s) 1-min recovery significantly Hb, no consistent associations found maximum late (60–175 values. Associations CBv poor, stronger long-channel than short-channel well first change. Stronger suggest that NIRS specifically reflects flow transitions, necessary better understand consequences of such as intolerance symptoms.

Language: Английский

---

fNIRS: Non-stationary preprocessing methods

Biomedical Signal Processing and Control, Journal Year: 2022, Volume and Issue: 79, P. 104110 - 104110

Published: Aug. 24, 2022

• MVE is able to accurately detect (97.56 %) noisy channels in fNIRS data. CCFA filtering produce a higher SNR than other conventional methods. Choosing correct window can improve of specific HRF amplitude range. In this paper we present algorithms for preprocessing functional Near Infrared Spectroscopy (fNIRS) We propose statistical method that provides an automatic identification and non-stationary procedure both detrending removal high frequency contamination sources. A recently published Cumulative Curve Fitting Approximation (CCFA) algorithm was used the filtration signals reduce distortion effects due non-stationarity The output compared Discrete Cosine Transform (DCT) based filtering, followed by Low Pass Filtering (LPF) Band (BPF) results demonstrate greater Signal Noise Ratio (SNR) improvement comparison commonly/conventionally

Language: Английский

---

Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System

Sensors, Journal Year: 2023, Volume and Issue: 23(7), P. 3632 - 3632

Published: March 31, 2023

The employment of wearable systems for continuous monitoring vital signs is increasing. However, due to substantial susceptibility conventional bio-signals recorded by motion artifacts, estimation the respiratory rate (RR) during physical activities a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable subject's movements. This paper proposes fusion-based method estimating RR bicycling from fNIRS signals system.Firstly, five modulations are extracted, based on amplitude, frequency, and intensity oxygenated hemoglobin concentration (O2Hb) signal. Secondly, dominant frequency each modulation computed using fast Fourier transform. Finally, frequencies all fused, averaging, estimate RR. performance proposed was validated 22 young healthy subjects, whose were simultaneously task, compared against zero delay domain band-pass filter.The comparison between results obtained filtering indicated superiority former, with lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). fusion strategy also outperformed estimations analysis individual modulation.This study orients towards practical limitations traditional activities.

Language: Английский

---

Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference

Biosensors, Journal Year: 2022, Volume and Issue: 12(12), P. 1170 - 1170

Published: Dec. 14, 2022

Objective: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered to whether possible estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation fNIRS can provide complementary information that be used alongside cerebral activity analysis, e.g., sport studies. Thus, objective of this paper propose method for fNIRS. Our primary presumption changes baseline wander oxygenated hemoglobin concentration (O2Hb) signal are related RR. Methods: and signals were concurrently collected subjects during controlled breathing tasks at constant 0.1 Hz 0.4 Hz. Firstly, quality index algorithm employed select best O2Hb signal, then band-pass filter with cut-off frequencies 0.05 2 remove very low- high-frequency artifacts. Secondly, troughs filtered localized synthesizing (S1) using cubic spline interpolation. Finally, fast Fourier transform S1 computed, its dominant frequency considered In paper, two different datasets employed, where first one was parameter adjustment proposed method, second solely testing. Results: The low mean absolute error between reference estimated RRs (2.6 1.3 breaths per minute, respectively) indicates feasibility Significance: This provides novel view on respiration source

Language: Английский

---

Respiratory Rate Extraction from Neonatal Near-Infrared Spectroscopy Signals

Sensors, Journal Year: 2023, Volume and Issue: 23(9), P. 4487 - 4487

Published: May 5, 2023

Background: Near-infrared spectroscopy (NIRS) relative concentration signals contain 'noise' from physiological processes such as respiration and heart rate. Simultaneous assessment of NIRS respiratory rate (RR) using a single sensor would facilitate perfectly time-synced (cerebral) physiology. Our aim was to extract cerebral intensity in neonates admitted neonatal intensive care unit (NICU). Methods: A novel algorithm, NRR (NIRS RR), is developed for extracting RR recorded critically ill neonates. In total, 19 measurements were ten the NICU with gestational age birth weight 38 ± 5 weeks 3092 990 g, respectively. We synchronously reference sampled at 100 Hz 0.5 Hz, The performance algorithm assessed terms agreement linear correlation between extracted RRs, it compared statistically that two existing methods. Results: showed mean error 1.1 breaths per minute (BPM), root square 3.8 BPM, Bland-Altman limits 6.7 BPM averaged over all measurements. addition, 84.5% (p < 0.01) achieved RRs. statistical analyses confirmed significant 0.05) outperformance respect Conclusions: possibility an environment, which high correspondence recorded. Adding system provides opportunity record different sources information about perfusion by monitoring system. This allows concurrent integrated analysis impact breathing (including apnea) on hemodynamics.

Language: Английский

---

Effects of individual research practices on fNIRS signal quality and latent characteristics

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2024, Volume and Issue: 32, P. 3515 - 3521

Published: Jan. 1, 2024

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies still open issue in scientific community. The paucity experimental practices lack clear guidelines regarding use contribute to undermining results. For this reason, much effort now directed at assessing impact heterogeneous creating divergent current work aims assess differences signal quality data collected by two different labs cohorts: Singapore (N=74) Italy (N=84). Random segments 20s were extracted from each channel participant's NIRScap 1280 deep features obtained using a learning model trained classify data. Two datasets generated: ALL dataset (segments with bad good quality) GOOD quality). Each was divided into train test partitions, which used evaluate performance Support Vector Machine (SVM) classifying cohorts features. Results showed that SG cohort had significantly higher occurrences majority channels. Moreover, SVM correctly classified when dataset. dropped almost completely (except five channels) These results suggest raw might possess levels as well latent characteristics beyond per se. study highlights importance defining conduction experiments reporting manuscripts.

Language: Английский

---

Near-Infrared Spectroscopy for Neonatal Sleep Classification

Sensors, Journal Year: 2024, Volume and Issue: 24(21), P. 7004 - 7004

Published: Oct. 31, 2024

Sleep, notably active sleep (AS) and quiet (QS), plays a pivotal role in the brain development gradual maturation of (pre) term infants. Monitoring their patterns is imperative, as it can serve tool promoting neurological well-being, particularly important preterm infants who are at an increased risk immature development. An accurate classification neonatal states contribute to optimizing treatments for high-risk infants, with respiratory rate (RR) heart (HR) serving key components assessment systems neonates. Recent studies have demonstrated feasibility extracting both RR HR using near-infrared spectroscopy (NIRS) This study introduces comprehensive approach leveraging high-frequency NIRS signals recorded sampling 100 Hz from cohort nine admitted intensive care unit. Eight distinct features were extracted raw signals, including HR, RR, motion-related parameters, proxies neural activity. These served inputs deep convolutional network (CNN) model designed AS QS states. The performance proposed CNN was evaluated two cross-validation approaches: ten-fold data pooling five-fold cross-validation, where each fold contains independently data. accuracy, balanced F1-score, Kappa, AUC-ROC (Area Under Curve Receiver Operating Characteristic) employed assess classifier performance. In addition, comparative analyses against six benchmark classifiers, comprising K-Nearest Neighbors, Naive Bayes, Support Vector Machines, Random Forest (RF), AdaBoost, XGBoost (XGB), conducted. Our results reveal model's superior performance, achieving average accuracy 88%, 94%, F1-score 91%, Kappa 95%, 96% cross-validation. Furthermore, methods, RF XGB levels closely comparable classifier. findings underscore data, coupled NIRS-based extraction, assessing neonates, even setting. user-friendliness, portability, reduced sensor complexity suggest its potential applications various less-demanding settings. research thus presents promising avenue advancing implications infant health

Language: Английский

---

fNIRS-QC: Crowd-Sourced Creation of a Dataset and Machine Learning Model for fNIRS Quality Control

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(20), P. 9531 - 9531

Published: Oct. 14, 2021

Despite technological advancements in functional Near Infra-Red Spectroscopy (fNIRS) and a rise the application of fNIRS neuroscience experimental designs, processing data remains characterized by high number heterogeneous approaches, implicating scientific reproducibility interpretability results. For example, manual inspection is still necessary to assess quality subsequent retention collected signals for analysis. Machine Learning (ML) approaches are well-positioned provide unique contribution automating standardizing methodological control, where ML models can produce objective reproducible However, any successful grounded high-quality dataset labeled training data, unfortunately, no such currently available signals. In this work, we introduce fNIRS-QC, platform designed crowd-sourced creation control dataset. particular, (a) composed 4385 signals; (b) created web interface allow multiple users manually label signal 510 10 s segments. Finally, (c) subset used develop proof-of-concept model automatically The developed serve as more efficient check that minimizes error from need expertise with control.

Language: Английский

---