MICHELIN STARS
MICHELIN Stars are awarded to restaurants offering outstanding cooking, taking into account five universal criteria: the quality of the ingredients; the harmony of flavours; the mastery of techniques; the personality of the chef as…
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Plants self-organize in a ‘hidden order,’ echoing pattern found across nature
Scientists have uncovered a “hidden order” in drylands across the planet, where plants follow disordered hyperuniformity — a layout that looks random and disorganized up close but adheres to a clear pattern when viewed from farther away.
The…
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The Orionid meteor shower lights up autumn skies – news.cgtn.com
- The Orionid meteor shower lights up autumn skies news.cgtn.com
- Don’t miss the Orionid meteor shower peak begin overnight tonight under a moonless sky Space
- Viewing the Orionid Meteor Shower in 2025 American Meteor Society
- How to Watch a Rare…
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When Asrani revealed he regretted doing Sunny Leone’s Mastizaade: ‘Mujhe sharam aayi’
Earlier this week, Bollywood lost one of its most well-known and versatile gems when actor and filmmaker Govardhan Asrani breathed his last. On October 16, he was admitted to the hospital due to breathing issues. On October 20, Asrani died at the…
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Combined with chemotherapy and radiotherapy is effective in improving prognosis: a case of primary mediastinal embryonal carcinoma | Journal of Cardiothoracic Surgery
A 28-year-old man presented with spasmodic cough lasting for more than 1 month. Physical examination was normal. The chest radiography demonstrated a large, mass-like opacity located at the left side of the upper mediastinum(‘. 1). A…
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Cold versus warm autoimmune hemolytic anemia in diffuse large B cell lymphoma: pathogenic and therapeutic implications: a case series | Journal of Medical Case Reports
Case 1: CAS combined with DLBCL
We report a case of a 29-year-old Han male patient. On 17 October 2024, he presented to the First Affiliated Hospital of Anhui Medical University with complaints of fatigue and palpitations for 2 weeks. A complete…
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Re-examining the association between region-specific pain recurrence and muscle force strategies in patients with patellofemoral pain via OpenSim and artificial intelligence: a prospective cohort study toward targeted rehabilitation | Journal of Neur…
In this study, we developed and evaluated an artificial intelligence (machine learning) model to elucidate the muscle force-production characteristics associated with the recurrence of pain in the APP, MBP, and LBP regions. The findings support our hypothesis, indicating that the muscle force–related factors underlying PFP recurrence differ across anatomical regions. Among the three machine learning classifiers employed, the XGBoost model exhibited the highest accuracy, with predictive performance ranging from 95.9% to 98.3% in forecasting pain across these regions. Consequently, SHAP values were computed based on the XGBoost model to visually represent the importance of each feature, thereby delineating the key muscle force patterns that precipitate PFP in distinct regions.
In addressing overfitting, it is noteworthy that XGBoost incorporates embedded regularization during training, specifically the L1 (lambda) and L2 (alpha) penalties, which effectively constrain model complexity and reduce the likelihood of overfitting to the training set [36]. In addition, we employed five-fold cross-validation within the training data to optimize hyperparameters, thereby avoiding the inappropriate use of the test set for model tuning and further mitigating the risk of overfitting. Nevertheless, given that the AUC achieved by the XGBoost models in this study was relatively high (>0.99), it remains possible that some degree of uncontrollable overfitting occurred. This observation highlights the need for future research to expand both sample size and feature dimensionality, as well as to improve group balance during testing, in order to further safeguard against overfitting.
Muscle force production patterns associated with APP region pain
Insufficient strength in the gracilis, tibialis anterior, and internal oblique muscles, combined with excessive strength in the adductor longus and tensor fascia latae, constitutes the principal precipitating factors for pain in the APP region. Although both the gracilis and adductor longus are located on the medial thigh, their distinct insertion sites result in different contributions to joint kinematics [37, 38]. Specifically, the gracilis inserts on the medial aspect of the proximal tibia, facilitating hip internal rotation, whereas the adductor longus terminates at the linea aspera of the femur and functions to externally rotate the hip [37, 38]. Consequently, insufficient strength of the gracilis, combined with excessive strength of the adductor longus, may result in exaggerated hip external rotation during gait. Cadaveric studies have demonstrated that a hip external rotation of 30° can significantly increase patellofemoral joint contact forces [39]. During ambulation, the contact force generated by hip external rotation is continuously loaded onto the anterior aspect of the patella, peaking at knee flexion angles between 30° and 60°. Over time, this repetitive loading may precipitate the recurrence of pain in the APP region [39]. It is noteworthy that recent studies suggest potential differences in neural innervation and functional roles between the proximal and distal regions of the same muscle [40]. For the gracilis, the proximal fibers, originating from the medial aspect of the ischiopubic ramus, may primarily contribute to hip joint motion. In contrast, the distal fibers extend across the knee joint, where they merge with the tendons of the sartorius and semitendinosus to form the pes anserinus, inserting onto the superior medial tibia [41]. This anatomical arrangement suggests that the distal fibers may be more directly involved in knee movement and tibial internal rotation. Considering that current evidence generally supports the association between gracilis insufficiency and the recurrence of PFP, primarily through alterations in hip joint mechanics as previously discussed [37,38,39], clinical practice should place particular emphasis on targeted activation and rehabilitation of the proximal gracilis. Such interventions may help prevent recurrent pain in the APP region.
Our findings indicate that insufficient tibialis anterior strength is a primary precipitant of pain in the APP region. Notably, previous cohort studies have not clearly delineated the relationship between tibialis anterior strength and PFP. The present study’s ability to capture this association may be attributable to the employment of a machine learning model, which can discern more subtle inter-variable relationships that may be pivotal for PFP prevention. Inadequate tibialis anterior strength disrupts the balance between plantarflexor and dorsiflexor forces at the ankle, thereby impairing sagittal plane stability [42]. As a consequence, the ankle’s capacity for sagittal attenuation is markedly diminished, allowing unbuffered ground reaction forces to be transmitted proximally to the knee and patellofemoral joints, which leads to excessive localized loading on the patellar surface and ultimately precipitates APP pain [43]. Furthermore, the machine learning model revealed that diminished internal oblique strength is associated with APP pain, corroborating findings from previous cross-sectional studies [44]. Insufficient internal oblique strength increases trunk inclination and rotation during ambulation. In particular, when the internal oblique of the contralateral side is weak, the trunk tends to rotate and tilt toward the affected side. Given that the trunk accounts for over 50% of body weight, such a shift in mass distribution results in an increased overall load on the affected limb, thereby elevating patellofemoral joint stress [44]. This heightened joint stress exacerbates anterior patellar wear during knee flexion-extension cycles, ultimately culminating in APP pain [45].
Excessive strength in the tensor fascia latae is also a major predictor of APP region pain. Previous studies have further reported that such overexertion of the tensor fasciae latae can induce hip flexion [46]. However, during gait, excessive hip flexion results in forward tilting of the trunk, thereby shifting the center of mass anteriorly and increasing the load on the anterior aspect of the knee, ultimately precipitating recurrent APP pain [47]. Moreover, excessive force of the tensor fasciae latae may disrupt the strength balance between the hip flexors and extensors [48], thereby undermining sagittal-plane core stability and consequently amplifying anterior–posterior displacement of the center of mass [49]. In response, the lower limb joints often adopt a “stiff” movement pattern to stabilize the center of mass and prevent falls. Nevertheless, in such a “stiff” gait pattern, energy absorption by tissues distal to the knee may be reduced, thereby increasing the mechanical load on the knee joint. This load may be repeatedly transmitted to the anterior patellar region, ultimately resulting in patellar cartilage damage and the recurrence of APP [47].
Muscle force production patterns associated with MBP region pain
The machine learning model identified that excessive strength in the rectus femoris, gracilis, gluteus maximus, and adductor longus muscles, coupled with insufficient strength in the semitendinosus, constitutes the primary mechanism underlying MBP region pain. Notably, the rectus femoris serves as a knee extensor, whereas the semitendinosus—being part of the hamstring group—functions as a knee flexor. Excessive rectus femoris force, combined with insufficient semitendinosus strength, may lead to an increased knee extension moment during gait. Prior investigations have demonstrated that when the knee extension moment reaches 240 Nm, the patellofemoral joint stress escalates by 3.9 MPa [50]. Furthermore, inadequate semitendinosus strength may compromise the dynamic control of the knee’s frontal plane, resulting in augmented lateral displacement of the patella, exacerbation of medial patellar wear, and ultimately the manifestation of MBP region pain in patients with PFP [51]. Recent studies have reported that the proximal and distal compartments of the semitendinosus exhibit distinct discharge rates and differences in the variability of neural drive [40]. It is speculated that the region of the semitendinosus located closer to the knee joint may be more directly involved in knee and patellofemoral joint loading and movement, and thus more strongly associated with the recurrence of MBP. Consequently, targeted stimulation and rehabilitation of this knee-adjacent region of the semitendinosus during clinical training may yield greater therapeutic benefits and improve the effectiveness of preventing MBP recurrence. However, this assumption remains speculative and requires confirmation through future cohort studies.
During ambulation, excessive force generated by the gracilis and adductor longus muscles is a key precipitant of MBP region pain. In addition to mediating hip internal and external rotation, both muscles are part of the hip adductor group; their combined force production may lead to pronounced hip adduction [52]. Studies by McKenzie et al. [53] and Willson et al. [54] have shown that patients with PFP exhibit significantly greater hip adduction during functional activities—such as running, jump landing, and squatting—compared with healthy controls, thereby further validating these findings. Ultrasonographic data indicate that when hip adduction reaches 20°, lateral displacement of the patella increases by approximately 7.3 mm [55]. Repeated lateral displacement may exacerbate friction at the medial femoral condyle and medial patellar region, ultimately precipitating pain in the MBP region [55].
Results from the machine learning model indicate that excessive gluteus maximus strength is one of the contributing factors to pain in the MBP region. Previous studies have demonstrated that excessive force production by the gluteus maximus during gait leads to heightened frontal-plane stiffness at the hip joint [56]. Given that the hip and knee joints serve as the primary sites for frontal-plane energy absorption during ground contact—accounting for 59.1% and 38.5% of energy absorption, respectively [57]—excessive hip stiffness markedly reduces its capacity to absorb energy. Consequently, unbuffered ground reaction forces are transferred to the knee joint [57], increasing the frontal-plane load and elevating medial patellar pressure [57]. Moreover, excessive force production by the gluteus maximus during gait may promote an overly upright trunk posture, thereby elevating the center of mass and increasing lateral trunk sway. The lateral forces generated by this trunk movement are repeatedly imposed on the knee joint, which is already burdened by high energy absorption demands [58], ultimately leading to repeated compression of the medial patellar margin and significant pain over time.
Muscle force production patterns associated with LBP region pain
Our explainable machine learning model revealed that insufficient strength in the rectus femoris, tensor fascia latae, and gluteus maximus, coupled with excessive force generated by the adductor longus and gracilis, constitutes the primary mechanism underlying pain in the LBP region. Among these, the rectus femoris—an integral component of the quadriceps—primarily generates the knee extension moment during gait [59]. Powers et al. [60] reported that PFP patients exhibit a knee extension moment of 23.6 N·cm/kg, compared to 30.4 N·cm/kg in healthy individuals; the inability of the rectus femoris to produce an adequate extension moment may therefore serve as a precipitating factor for PFP. Moreover, the quadriceps play a critical role in controlling patellar tracking during knee flexion-extension. Weakness in this muscle group can result in aberrant patellar kinematics, including increased frontal plane displacement of the patella and consequent excessive compression of its lateral edge, ultimately leading to pain [59]. It should be noted that previous studies have reported regional heterogeneity in the activation of the rectus femoris under different contraction tasks. Specifically, hip flexion predominantly activates the proximal region of the muscle, whereas knee extension primarily recruits its central and distal regions [61]. This task-dependent distribution is thought to arise from the relatively segregated organization of motor neuron pools within the spinal cord, where neurons innervating the proximal region are mainly located in the rostral part of the motor nucleus, while those serving the distal region are positioned more caudally [61]. Such topographical differentiation provides a mechanistic explanation for the distinct activation patterns of the proximal versus distal regions of the rectus femoris [61]. In the context of the present study, the distal region may be a critical contributor to LBP-related pain, as it is more directly associated with knee and patellofemoral joint function, suggesting that this region should receive particular attention during clinical interventions [61]. However, we did not directly examine the relationship between regional rectus femoris activation and LBP pain in this study. Accordingly, this interpretation remains speculative and requires systematic investigation in future research.
Based on our findings, insufficient gluteus maximus strength predisposes individuals to LBP region pain. The gluteus maximus not only facilitates hip extension but also functions as a hip external rotator; when its strength is diminished, the hip and femur tend to exhibit excessive internal rotation. Powers et al. [62] demonstrated via magnetic resonance imaging that an 8° increase in femoral internal rotation can induce a 13° patellar external rotation, during which the lateral edge of the patella is repeatedly subjected to friction and compressive forces against the lateral femoral condyle. Moreover, owing to the inherent anatomy of the patellofemoral joint, the lateral force on the patella during gait (0.54 BW) far exceeds that on the medial side (0.15 BW) [63]. Collectively, this loading environment markedly increases the risk of LBP region pain. Furthermore, our machine learning results indicate that insufficient tensor fascia latae strength, combined with excessive force generated by the adductor longus and gracilis, further predisposes to LBP region pain. The tensor fascia latae, a principal component of the hip abductor group [64], contrasts with the adductor longus and gracilis, which are key elements of the hip adductor group. Reduced strength of the tensor fascia latae, combined with excessive force production by the adductor longus and gracilis, leads to pronounced hip adduction during ambulation [52, 65]. Ultrasonographic evidence indicates that, compared with a neutral position, 20° of hip adduction results in a 0.24-cm increase in lateral patellar displacement. Repeated lateral displacement of the patella may exacerbate wear and local compressive loading on its lateral margin, ultimately precipitating LBP region pain [66].
Implications for preventive training
Targeted neuromuscular training may alleviate regional PFP by correcting specific deficits in force-generation patterns. For the prevention of APP region pain, it is imperative to enhance the strength of the hip internal rotator group to counteract the excessive force generated by the adductor longus, which predisposes the hip to external rotation. In addition, incorporating resistance exercises for the tibialis anterior, alongside relaxation protocols for the tensor fascia latae to prevent its overcontraction during the stance phase, is recommended. Furthermore, targeted training of the contralateral internal oblique may help correct trunk lean during gait, thereby reducing unilateral knee loading.
In the context of MBP region pain, emphasis should be placed on augmenting hamstring strength to restore a balanced knee flexion–extension moment and improve knee joint stability. Concurrently, targeted strengthening of the hip abductor group is recommended to counteract the aberrant force production by the gracilis and adductor longus during gait. Furthermore, relaxation training for the gluteus maximus may increase the hip joint’s frontal-plane mobility and its capacity for energy absorption, thereby reducing the overall load on the knee joint.
For LBP region pain, priority should be given to reinforcing the rectus femoris and gluteus maximus. Concurrently, targeted strengthening of the hip abductor group (e.g., tensor fascia latae) should be implemented, while relaxation training for the hip adductor group (e.g., adductor longus and gracilis) is recommended to correct imbalances between hip adduction and abduction. This integrated approach is expected to enhance proximal joint stability and ultimately reduce lateral displacement and loading of the patellofemoral joint.
It should be emphasized that although the SHAP dependence plots identified apparent “thresholds” for these risk factors, these values do not represent clinically meaningful cut-off points. Rather, they indicate the points at which the relationship between a given feature and the risk of PFP recurrence begins to change within the model. Accordingly, these thresholds cannot be directly applied in clinical practice and should instead be viewed as preliminary reference values that require further verification and refinement through systematic clinical studies.
Looking ahead, our model could be integrated into clinical decision-support tools, such as OpenSim-based musculoskeletal modeling platforms or gait analysis systems, to enable direct identification of region-specific risks of PFP recurrence and the muscle strength deficits most in need of improvement. Such integration would not only facilitate the development of individualized intervention strategies but also provide more robust evidence to inform clinical decision making. Furthermore, review studies have indicated that different regions within the same muscle exhibit distinct levels of focal adhesion kinase (FAK) phosphorylation during eccentric and concentric training [67]. For instance, following eccentric training of the vastus lateralis, pY397-FAK levels in the distal region were approximately four times higher than those observed after concentric training, contributing to distal hypertrophy of the quadriceps [67]. Given that different training modalities induce region-specific muscle hypertrophy, future strength interventions for patients with PFP should first identify the specific muscle regions implicated in the condition and then select training strategies that effectively target these regions. This study provides only a preliminary intervention framework by treating the muscle as a single functional unit. Future research should build on these findings to investigate more refined, region-specific approaches.
Limitations
Although this study provides preliminary evidence of distinct muscle force strategies associated with PFP across different anatomical regions, several limitations should be acknowledged. First, although SHAP was used to preliminarily identify the points at which the relationship between muscle strength features and the risk of PFP recurrence begins to shift within the machine-learning model, their generalizability may be limited by individual variability and environmental context. To translate these thresholds into clinical practice, further clinical studies are needed for validation and refinement. Furthermore, although a six-month follow-up in a cohort of 299 participants yielded important initial findings, a longer observation window and a larger, more heterogeneous sample would likely offer a more comprehensive understanding of the temporal dynamics and recurrence patterns of PFP. Finally, recent studies have suggested that neural innervation and activation levels may vary across different regions (proximal versus distal) of the same muscle [40]. In the present study, however, the OpenSim modeling approach treated each muscle as a single unit, which made it difficult to examine how region-specific muscle activation might be related to the recurrence of PFP. This limitation underscores the need for future cohort studies to further investigate these region-dependent mechanisms underlying PFP recurrence. Finally, recent studies have demonstrated that neural innervation and activation levels can differ across distinct regions of the same muscle, such as proximal versus distal segments [40, 61]. This phenomenon is likely attributable to the differential spatial organization of motor neurons within the spinal cord that control the proximal and distal portions of the muscle [61]. However, in the present study, the OpenSim modeling approach treated each muscle as a single unit, limiting our ability to investigate the relationship between region-specific muscle activation and PFP recurrence. This limitation underscores the need for future cohort studies to examine the region-dependent mechanisms underlying PFP recurrence. In addition, evidence from training studies indicates that different muscle regions exhibit distinct molecular responses during concentric and eccentric exercises, which can lead to region-specific hypertrophy [67]. Because the present study considered muscles as whole units, it provides limited guidance for interventions targeting specific muscle regions, representing a key limitation of this work.
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Disruption of low-frequency narrowband EEG microstate networks in Parkinson’s disease with mild cognitive impairment | Journal of NeuroEngineering and Rehabilitation
Participants and study design
This investigation enrolled a total of 67 individuals, comprising 47 individuals diagnosed with primary PD who were undergoing treatment at Beijing Rehabilitation Hospital, affiliated with Capital Medical University (25 males and 22 females), along with 20 individuals without any neurological conditions (healthy controls, HC) sourced from the local population, ensuring parity in gender and age. These controls had no past records of neurological impairments. Motor function was primarily appraised via the Movement Disorder Society Unified Parkinson’s Disease Rating Scale part III (MDS-UPDRS III). Concurrently, cognitive abilities were assessed through the Mini-Mental State Examination (MMSE) as well as the Montreal Cognitive Assessment (MoCA) scales [22]. The MoCA is the most frequently used standard screening tool for PD-MCI, with a specificity of 82%, a sensitivity of 41%, and a diagnostic accuracy of 68% while maintaining clinical relevance [23]. From the MoCA results, two groups emerged among the 47 participants with PD: those with normal cognition (PD-NC, score > 25) and those with mild cognitive impairment (PD-MCI, score ≤ 25). With the approval of the Ethics Committee of Beijing Rehabilitation Hospital, Capital Medical University, we collected the data for this study (Approval Number: 2023bkky044). The data acquisition adhered rigorously to the ethical principles outlined in the Declaration of Helsinki. Before participating, all participants gave written informed consent.
EEG data acquisition and preprocessing
EEG data were recorded using a 30-channel electrode system (actiCAP snap, Brain Products GmbH, Gilching, Germany). The electrode positions included frontal electrodes (Fp1, Fz, F3, F4, F7, F8), fronto-central electrodes (FT9, FT10, FC5, FC6, FC1, FC2), central electrodes (C3, Cz, C4), parietal electrodes (CP5, CP6, CP1, CP2, Pz, P3, P4, P7, P8), and occipital electrodes (O1, Oz, O2). Participants sat relaxed with eyes closed in dimly lit rooms, maintaining wakefulness. The impedance of all electrodes was kept under 5 kΩ. The EEG signals were sampled at 1000 Hz for 15 min per individual. A rigorous, multi-stage preprocessing pipeline was applied to the raw EEG data to ensure high data quality and mitigate the constraints of the acquisition setup. Data were originally recorded using an actiCAP system with TP9 as the online reference. The data were first filtered using a zero-phase bandpass filter (0.5–45 Hz; Hamming-windowed Finite Impulse Response filter in EEGLAB) to attenuate low-frequency drifts and high-frequency noise. As the online reference (TP9) is suboptimal for Independent Component Analysis (ICA), the data were offline-referenced to the average of the bilateral mastoids (TP9 and TP10). This approach established a pragmatic and stable non-cephalic reference point for ICA, consistent with established practices in the field [24]. Subsequently, the data were decomposed into 30 independent components using the Infomax ICA algorithm. To ensure the validity of the decomposition and the accuracy of artifact rejection, a robust, semi-automated component classification strategy was employed. An initial, data-driven labeling was performed using ICLabel [25], followed by a rigorous manual review conducted by two experienced researchers. Components unambiguously identified as ocular (blinks, saccades), cardiac, or muscle artifacts (along with other categories) were rejected. A 5-minute continuous portion of EEG data per participant was selected for the purpose of microstate analysis. Due to the intermittent nature of artifacts, this 5-minute segment was often composed of multiple concatenated artifact-free intervals. The artifact-free EEG data were down-sampled to 125 Hz and then filtered for the broadband range (1–30 Hz). This broadband range was divided into four narrow bands based on their frequency ranges: the 1–4 Hz delta band, the 4–7 Hz theta band, the 7–13 Hz alpha band, and the 13–30 Hz beta band. Each narrow-band signal was further filtered using a zero-phase Finite Impulse Response filter (Hamming window) to ensure precise frequency isolation and temporal fidelity.
EEG spectral microstate analysis
The core principle of EEG microstate analysis is to segment the continuous EEG stream into a sequence of quasi-stable topographical maps, achieved by clustering the EEG data at specific time points [26]. The global field potential (GFP) quantifies the aggregate field strength of EEG signals at varying time points, as depicted in formula (1):
$$ \,GFP\left( t \right) = \sqrt {\frac{1}{N}\sum {\,_{{i = 1}}^{N} } (V_{i}^{{\prime \,}} (t))^{2} } $$
(1)
where \(\:{V}_{i}\left(t\right)\) denotes the potential of the \(\:i\)-th electrode at time\(\:\:t\), with \(\:N\) indicating the overall count of electrodes. Since the peak point of GFP typically coincides with the transition between microstates, it allows for the segmentation of microstate time points based on the GFP peak locations. GFP peaks were extracted with a minimum inter-peak distance of 10 ms and a maximum of 1000 peaks per subject. To mitigate noise, peaks with an amplitude exceeding one standard deviation above the mean GFP amplitude were excluded. Microstate templates were derived using a modified k-means clustering algorithm (modkmeans). The number of clusters (k) was fixed at 5, and the clustering procedure was repeated 50 times with random initializations to ensure solution stability. The optimal cluster set was selected based on the lowest cross-validation error, and the resulting maps were sorted by their global explained variance. Clustering iterations were limited to 1000 with a convergence threshold of 1e-6. Following template extraction, a back-fitting procedure was applied to assign a microstate label to each time point of the continuous EEG data for each subject. During this process, the polarity of the EEG signals was ignored to prevent label inversion. Subsequently, temporal smoothing was applied to remove microstate segments shorter than 30 ms, thereby improving the temporal continuity and physiological plausibility of the sequences. The refined, ongoing EEG data from each subject were sorted into their corresponding microstate types A, B, C, D, and E (abbreviated as MS-A, MS-B, MS-C, MS-D, and MS-E) by using the derived microstate type labels as templates. This analysis was performed on both the broadband data and data filtered into four classical frequency bands: delta, theta, alpha, and beta. For each of these five conditions, the preprocessed EEG data were independently matched against the set of microstate templates. From the resulting microstate sequences, several parameters were computed for each microstate class (Fig. 1A): (a) Coverage: The ratio of the duration a microstate is present to the total recording time. (b) Duration: Measures the length of each instance of a microstate. (c) Occurrence: The frequency of microstate occurrences throughout the recording period. (d) Transition Probability (TP): The chance that one microstate in the brain transforms into another.
Fig. 1 
The scheme of the EEG microstate dynamics and spatiotemporal network analysis. A Steps for frequency–spectral microstate extraction, including bandpass filtering, global field power detection, microstate class identification, and microstate parameter analysis. B Steps for microstate-derived network analysis, including dynamic functional connectivity computation per microstate class by phase lag index, and quantification of temporal variability and spatial variability
EEG spectral microstate network analysis
The EEG microstate sequence for each individual was segmented into a series of discrete, non-overlapping intervals based on the assigned microstate labels (A-E). For each resulting microstate window, a functional connectivity network was constructed from 30 EEG electrodes using the Phase Lag Index (PLI). The PLI serves to measure the asymmetry level observed in the phase disparity between two signals [27]. Functional connectivity was estimated independently within each microstate segment, without concatenation or averaging across segments, resulting in a series of state-specific functional connectivity matrices. The segmentation algorithm adaptively defines the duration of each window, thereby avoiding arbitrary temporal partitioning and maintaining physiologically meaningful boundaries. Moreover, microstate segments, typically lasting between 60 and 300 ms, are considered quasi-stationary and have been empirically demonstrated to support reliable phase-based connectivity estimation [21]. Thus, the functional connectivity of paired EEG channels in the interval represented by the \(\:r\)-th microstate window was determined, as illustrated by formula (2),
$$ F_{r} \left( {i,j} \right) = \left| {~\left\langle {sign~\left[ {~sin\left( {\Delta \phi \left( {t_{l} } \right)} \right)~} \right]} \right\rangle ~} \right|l = 1,2,…,m_{r} ,i \ne j $$
(2)
where \(\:{F}_{r}(i,j)\) represents the functional connectivity between electrode channels \(\:i\) and \(\:j\) in the r-th microstate epoch. The notation \(\:\left\langle.\right\rangle\) is employed to denote the average value over that period. \(\:\Delta\varphi\:\left({t}_{l}\right)\) represents the phase differential determined at the \(\:l\)-th time instance, while \(\:{m}_{r}\) stands for the total number of time points within the \(\:r\)-th microstate epoch. By aggregating these PLI values for all pairs of EEG channels, the 30 × 30 adjacency matrix was constructed, which forms the brain network \(\:{F}_{r}\) for the \(\:r\)-th microstate window.
Subsequently, the functional connection networks corresponding to every distinct microstate category was divided into five different brain network sets (microstate networks A, B, C, D, and E). Utilizing these networks, the temporal and spatial variabilities were computed [21]. Temporal variability, by assessing the correlation between functional connectivity patterns of the same brain region across different time windows, captures the time-dependent changes of the functional architecture in specific brain areas, thereby providing insights into the temporal robustness of the region’s functional connectivity. The specific definition is shown in formula (3):
$$\:{T}_{i}=1-\frac{1}{v(v-1)}\sum\:_{p,q=1,p\ne\:q}^{v}\text{c}\text{o}\text{r}\text{r}\left({F}_{p}(i,:),{F}_{q}(i,:)\right)$$
(3)
where \(\:v\) is the number of microstate windows, \(\:{F}_{p}(i,:)\) indicates the functional arrangement of the \(\:i\)-th channel within the \(\:p\)-th microstate window, while \(\:corr({F}_{p}\left(i,:\right),{F}_{q}\left(i,:\right))\) calculates the correlation coefficient that measures the temporal similarity between two separate functional connectivity patterns. Spatial variability, by evaluating the correlation between the functional connectivity sequences of a particular brain region and those of other regions, reflects both the dynamic changes in local spatial functional connectivity and the spatial consistency of connectivity between regions. The definition is shown in formula (4),
$$\:{S}_{i}=1-\frac{1}{29\times\:28}\sum\:_{i,h=1,j\ne\:h\ne\:i}^{30}\text{c}\text{o}\text{r}\text{r}\left({F}_{s}(i,j),{F}_{s}(i,h)\right)$$
(4)
where \(\:corr({F}_{s}\left(i,j\right),{F}_{s}\left(i,h\right))\) denotes the correlation coefficient that quantifies the relationship between two different spatial functional connectivity patterns, \(\:{F}_{s}\left(i,j\right)\) and \(\:{F}_{s}\left(i,h\right)\). This metric is employed to assess the spatial consistency across all functional connectivity sequences associated with a particular scalp region (channel \(\:i\)) (Fig. 1B).
Clinical effect prediction
A feature set for classification was derived from microstate parameters, including coverage, duration, occurrence, and both temporal and spatial variability. These parameters were extracted across multiple frequency bands. To capture the spectral specificity of brain dynamics, these features were organized into distinct sets based on the following frequency bands: delta (1–4 Hz), theta (4–7 Hz), alpha (7–13 Hz), beta (13–30 Hz), and a broadband (1–30 Hz). A Random Forest classifier, implemented using a Bagging ensemble of decision trees, was trained for multi-class classification. To enhance model robustness and generalizability, hyperparameters (i.e., the number of trees, minimum leaf size, and maximum number of splits) were optimized via a Bayesian optimization algorithm. A nested cross-validation (CV) strategy was employed to prevent data leakage and overfitting. In this scheme, an inner 5-fold CV performed hyperparameter tuning, while an outer 5-fold CV provided an unbiased estimate of the model’s performance. To address potential class imbalance, class weights were set to be inversely proportional to class frequencies and applied during model training. The final model performance was determined by averaging the prediction metrics across the five outer folds of the nested CV. Classifier performance was comprehensively assessed using accuracy, sensitivity, and specificity. The results for each metric are reported as the mean ± standard deviation across these folds.
Statistical analysis
Data normality was evaluated by applying the Shapiro-Wilk test. The Mann-Whitney U test and independent t-tests were used to examine differences in demographic characteristics and clinical scores among the groups. Repeated measures Analysis of Variance was employed to evaluate group (PD-MCI, PD-NC, and HC) differences across narrowband and broadband microstate network metrics using four model specifications: (1) microstate parameters (3 groups × 3 parameters [coverage, duration, and occurrence] × 5 classes [MS-A, MS-B, MS-C, MS-D, and MS-E]), (2) transition probabilities (3 groups × 20 metrics [A to B/C/D/E, B to A/C/D/E, C to A/B/D/E, D to A/B/C/E, E to A/B/C/D]), (3) microstate network regional temporal or spatial variabilities (3 groups × 5 microstate levels × 30 channels), and (4) microstate network global temporal or spatial variabilities (3 groups × 5 classes [MS-A, MS-B, MS-C, MS-D, and MS-E]). Where significant main and interaction effects emerged (p < 0.05), post hoc analyses was conducted using Bonferroni-corrected paired-samples t-tests. Pearson correlations then quantified relationships between MoCA scores and group-discriminating features in PD subgroups (PD-MCI vs. PD-NC), with independent Bonferroni corrections applied to four metric families per frequency band: microstate parameters, transition probabilities, regional temporal or spatial variabilities, and global temporal or spatial variabilities.
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Functional motor mapping of domestic pig lumbar spinal cord using penetrating microelectrodes | Journal of NeuroEngineering and Rehabilitation
Alizadeh A, Dyck SM, Karimi-Abdolrezaee S. Traumatic spinal cord injury: an overview of pathophysiology, models and acute injury mechanisms. Front Neurol. 2019;10:282.
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