In Seminars in thrombosis and hemostasis ; h5-index 38.0

Bikdeli Behnood, Muriel Alfonso, Wang Yun, Piazza Gregory, Khairani Candrika D, Rosovsky Rachel P, Mehdipoor Ghazaleh, O’Donoghue Michelle L, Madridano Olga, Lopez-Saez Juan Bosco, Mellado Meritxell, Brasero Ana Maria Diaz, Grandone Elvira, Spagnolo Primavera A, Lu Yuan, Bertoletti Laurent, López-Jiménez Luciano, Núñez Manuel Jesús, Blanco-Molina Ángeles, Gerhard-Herman Marie, Goldhaber Samuel Z, Bates Shannon M, Jimenez David, Krumholz Harlan M, Monreal Manuel

2023-Mar-03

In Epilepsy & behavior : E&B

OBJECTIVE : Refractory temporal lobe epilepsy (TLE) with recurring seizures causing continuing pathological changes in neural reorganization. There is an incomplete understanding of how spatiotemporal electrophysiological characteristics changes during the development of TLE. Long-term multi-site epilepsy patients' data is hard to obtain. Thus, our study relied on animal models to reveal the changes in electrophysiological and epileptic network characteristics systematically.

METHODS : Long-term local field potentials (LFPs) were recorded over a period of 1 to 4 months from 6 pilocarpine-treated TLE rats. We compared variations of seizure onset zone (SOZ), seizure onset pattern (SOP), the latency of seizure onsets, and functional connectivity network from 10-channel LFPs between the early and late stages. Moreover, three machine learning classifiers trained by early-stage data were used to test seizure detection performance in the late stage.

RESULTS : Compared to the early stage, the earliest seizure onset was more frequently detected in hippocampus areas in the late stage. The latency of seizure onsets between electrodes became shorter. Low-voltage fast activity (LVFA) was the most common SOP and the proportion of it increased in the late stage. Different brain states were observed during seizures using Granger causality (GC). Moreover, seizure detection classifiers trained by early-stage data were less accurate when tested in late-stage data.

SIGNIFICANCE : Neuromodulation especially closed-loop deep brain stimulation (DBS) is effective in the treatment of refractory TLE. Although the frequency or amplitude of the stimulation is generally adjusted in existing closed-loop DBS devices in clinical usage, the adjustment rarely considers the pathological progression of chronic TLE. This suggests that an important factor affecting the therapeutic effect of neuromodulation may have been overlooked. The present study reveals time-varying electrophysiological and epileptic network properties in chronic TLE rats and indicates that classifiers of seizure detection and neuromodulation parameters might be designed to adapt to the current state dynamically with the progression of epilepsy.

Yang Yufang, Zhang Fang, Gao Xiang, Feng Linqing, Xu Kedi

2023-Mar-01

Electrophysiological properties, Granger causality, Functional connectivity, Neuromodulation, Temporal lobe epilepsy

In Computer methods and programs in biomedicine

Bennai Mohamed T, Guessoum Zahia, Mazouzi Smaine, Cormier Stéphane, Mezghiche Mohamed

2023-Feb-24

Image segmentation, Medical images, Multi-agent systems, Review, Survey

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVES : Chronic low back pain (CLBP) is a leading cause of disability. The management guidelines for the management of CLBP often recommend optimizing physical activity (PA). Among a subsample of patients with CLBP, central sensitization (CS) is present. However, knowledge about the association between PA intensity patterns, CLBP, and CS is limited. The objective PA computed by conventional approaches (e.g. cut-points) may not be sensitive enough to explore this association. This study aimed to investigate PA intensity patterns in patients with CLBP and low or high CS (CLBP-, CLBP+, respectively) by using advanced unsupervised machine learning approach, Hidden semi-Markov model (HSMM).

METHODS : Forty-two patients were included (23 CLBP-, 19 CLBP+). CS-related symptoms (e.g. fatigue, sensitivity to light, psychological features) were assessed by a CS Inventory. Patients wore a standard 3D-accelerometer for one week and PA was recorded. The conventional cut-points approach was used to compute the time accumulation and distribution of PA intensity levels in a day. For the two groups, two HSMMs were developed to measure the temporal organization of and transition between hidden states (PA intensity levels), based on the accelerometer vector magnitude.

RESULTS : Based on the conventional cut-points approach, no significant differences were found between CLBP- and CLBP+ groups (p = 0.87). In contrast, HSMMs revealed significant differences between the two groups. For the 5 identified hidden states (rest, sedentary, light PA, light locomotion, and moderate-vigorous PA), the CLBP- group had a higher transition probability from rest, light PA, and moderate-vigorous PA states to the sedentary state (p < 0.001). In addition, the CBLP- group had a significantly shorter bout duration of the sedentary state (p < 0.001). The CLBP+ group exhibited longer durations of active (p < 0.001) and inactive states (p = 0.037) and had higher transition probabilities between active states (p < 0.001).

CONCLUSIONS : HSMM discloses the temporal organization and transitions of PA intensity levels based on accelerometer data, yielding valuable and detailed clinical information. The results imply that patients with CLBP- and CLBP+ have different PA intensity patterns. CLBP+ patients may adopt the distress-endurance response pattern with a prolonged bout duration of activity engagement.

Zheng Xiaoping, Reneman Michiel F, Preuper Rita Hr Schiphorst, Otten Egbert, Lamoth Claudine Jc

2023-Feb-20

Accelerometer, Avoidance-endurance model, Central sensitization, Chronic pain, Daily life, Hidden semi-Markov model, Low back pain, Physical activity

In Psychoneuroendocrinology ; h5-index 69.0

BACKGROUND : Many studies investigating the cortisol awakening response (CAR) suffer from low adherence to the study protocol as well as from the lack of precise and objective methods for assessing the awakening and saliva sampling times which leads to measurement bias on CAR quantification.

METHODS : To address this issue, we have developed "CARWatch", a smartphone application that aims to enable low-cost and objective assessment of saliva sampling times as well as to concurrently increase protocol adherence. As proof-of-concept study, we assessed the CAR of N = 117 healthy participants (24.2 ± 8.7 years, 79.5% female) on two consecutive days. During the study, we recorded awakening times (AW) using self-reports, the CARWatch application, and a wrist-worn sensor, and saliva sampling times (ST) using self-reports and the CARWatch application. Using combinations of different AW and ST modalities, we derived different reporting strategies and compared the reported time information to a Naive sampling strategy assuming an ideal sampling schedule. Additionally, we compared the AUC_I, computed using information from different reporting strategies, against each other to demonstrate the effect of inaccurate sampling on the CAR.

RESULTS : The use of CARWatch led to a more consistent sampling behavior and reduced sampling delay compared to self-reported saliva sampling times. Additionally, we observed that inaccurate saliva sampling times, as resulting from self-reports, were associated with an underestimation of CAR measures. Our findings also revealed potential error sources for inaccuracies in self-reported sampling times and showed that CARWatch can help in better identifying, and possibly excluding, sampling outliers that would remain undiscovered by self-reported sampling.

CONCLUSION : The results from our proof-of-concept study demonstrated that CARWatch can be used to objectively record saliva sampling times. Further, it suggests its potential of increasing protocol adherence and sampling accuracy in CAR studies and might help to reduce inconsistencies in CAR literature resulting from inaccurate saliva sampling. For that reason, we published CARWatch and all necessary tools under an open-source license, making it freely accessible to every researcher.

Richer Robert, Abel Luca, Küderle Arne, Eskofier Bjoern M, Rohleder Nicolas

2023-Feb-24

Adherence, App, CAR, Saliva, Sampling accuracy, Smartphone

In EBioMedicine

BACKGROUND : It has been widely recognized that a critical time window for neurodevelopment occurs in early life and the host's gut microbiome plays an important role in neurodevelopment. Following recent demonstrations that the maternal prenatal gut microbiome influences offspring brain development in murine models, we aim to explore whether the critical time window for the association between the gut microbiome and neurodevelopment is prenatal or postnatal for human.

METHODS : Here we leverage a large-scale human study and compare the associations between the gut microbiota and metabolites from mothers during pregnancy and their children with the children's neurodevelopment. Specifically, using multinomial regression integrated in Songbird, we assessed the discriminating power of the maternal prenatal and child gut microbiome for children's neurodevelopment at early life as measured by the Ages & Stages Questionnaires (ASQ).

FINDINGS : We show that the maternal prenatal gut microbiome is more relevant than the children's gut microbiome to the children's neurodevelopment in the first year of life (maximum Q² = 0.212 and 0.096 separately using the taxa at the class level). Moreover, we found that Fusobacteriia is more associated with high fine motor skills in ASQ in the maternal prenatal gut microbiota but become more associated with low fine motor skills in the infant gut microbiota (rank = 0.084 and -0.047 separately), suggesting the roles of the same taxa with respect to neurodevelopment can be opposite at the two stages of fetal neurodevelopment.

INTERPRETATION : These findings shed light, especially in terms of timing, on potential therapeutic interventions to prevent neurodevelopmental disorders.

FUNDING : This work was supported by the National Institutes of Health (grant numbers: R01AI141529, R01HD093761, RF1AG067744, UH3OD023268, U19AI095219, U01HL089856, R01HL141826, K08HL148178, K01HL146980), and the Charles A. King Trust Postdoctoral Fellowship.

Sun Zheng, Lee-Sarwar Kathleen, Kelly Rachel S, Lasky-Su Jessica A, Litonjua Augusto A, Weiss Scott T, Liu Yang-Yu

2023-Mar-01

Ages and stages questionnaire, Childhood neurodevelopment, Early-life gut microbiome, Maternal gut microbiome