Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations.

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system's adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo-force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

Sensor-Based Gait Retraining Lowers Knee Adduction Moment and Improves Symptoms in Patients with Knee Osteoarthritis: A Randomized Controlled Trial.

The present study compared the effect between walking exercise and a newly developed sensor-based gait retraining on the peaks of knee adduction moment (KAM), knee adduction angular impulse (KAAI), knee flexion moment (KFM) and symptoms and functions in patients with early medial knee osteoarthritis (OA). Eligible participants (n = 71) with early medial knee OA (Kellgren-Lawrence grade I or II) were randomized to either walking exercise or gait retraining group. Knee loading-related parameters including KAM, KAAI and KFM were measured before and after 6-week gait retraining. We also examined clinical outcomes including visual analog pain scale (VASP) and Knee Injury and Osteoarthritis Outcome Score (KOOS) at each time point. After gait retraining, KAM1 and VASP were significantly reduced (both Ps < 0.001) and KOOS significantly improved (p = 0.004) in the gait retraining group, while these parameters remained similar in the walking exercise group (Ps ≥ 0.448). However, KAM2, KAAI and KFM did not change in both groups across time (Ps ≥ 0.120). A six-week sensor-based gait retraining, compared with walking exercise, was an effective intervention to lower medial knee loading, relieve knee pain and improve symptoms for patients with early medial knee OA.

Comparing Mechanistic and Preclinical Predictions of Volume of Distribution on a Large Set of Drugs.

PURPOSE: Volume of distribution at steady state (Vdss) is a fundamental pharmacokinetic (PK) parameter driven predominantly by passive processes and physicochemical properties of the compound. Human Vdss can be estimated using in silico mechanistic methods or empirically scaled from Vdss values obtained from preclinical species. In this study the accuracy and the complementarity of these two approaches are analyzed leveraging a large data set (over 150 marketed drugs). METHODS: For all the drugs analyzed in this study experimental in vitro measurements of LogP, plasma protein binding and pKa are used as input for the mechanistic in silico model to predict human Vdss. The software used for predicting human tissue partition coefficients and Vdss based on the method described by Rodgers and Rowland is made available as supporting information. RESULTS: This assessment indicates that overall the in silico mechanistic model presented by Rodgers and Rowland is comparably accurate or superior to empirical approaches based on the extrapolation of in vivo data from preclinical species. CONCLUSIONS: These results illustrate the great potential of mechanistic in silico models to accurately predict Vdss in humans. This in silico method does not rely on in vivo data and is, consequently, significantly time and resource sparing. The success of this in silico model further suggests that reasonable predictability of Vdss in preclinical species could be obtained by a similar process.

Evaluation of DILI Predictive Hypotheses in Early Drug Development.

Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals. DILI has been shown to be dependent on both daily dose and extent of hepatic metabolism. Yet, early in drug development daily dose is unknown. Here, we perform a comprehensive analysis of the published hypotheses that attempt to predict DILI, including a new analysis of the Biopharmaceutics Drug Disposition Classification System (BDDCS) in evaluating the severity of DILI warnings in drug labels approved by the FDA and the withdrawal status due to adverse drug reactions (ADRs). Our analysis confirms that higher doses ≥50 mg/day lead to increased DILI potential, but this property alone is not sufficient to predict the DILI potential. We evaluate prior attempts to categorize DILI such as Rule of 2, BSEP inhibition, and measures of key mechanisms of toxicity compared to BDDCS classification. Our results show that BDDCS Class 2 drugs exhibit the highest DILI severity and that all of the published methodologies evaluated here, except when daily dose is known, do not yield markedly better predictions than BDDCS. The assertion that extensive metabolized compounds are at higher risk of developing DILI is confirmed but can be enhanced by differentiating BDDCS Class 2 from Class 1 drugs. We do not propose that the BDDCS classification, which does not require knowledge of the clinical dose, is sufficiently predictive/accurate of DILI potential for new molecular entities but suggest that comparison of proposed DILI prediction methodologies with BDDCS classification is a useful tool to evaluate the potential reliability of newly proposed algorithms. CONCLUSION: The most successful approaches to predict DILI potential all include a measure of dose, yet there is a quantifiable uncertainty associated with the predicted dose early in drug development. Here, we compare the possibility of predicting DILI potential using the BDDCS classification versus previously published methods and note that many hypothesized predictive DILI metrics do no better than just avoiding BDDCS Class 2 drugs.

Impairment of decision making and disruption of synchrony between basolateral amygdala and anterior cingulate cortex in the maternally separated rat.

There is considerable evidence to suggest early life experiences, such as maternal separation (MS), play a role in the prevalence of emotional dysregulation and cognitive impairment. At the same time, optimal decision making requires functional integrity between the amygdala and anterior cingulate cortex (ACC), and any dysfunction of this system is believed to induce decision-making deficits. However, the impact of MS on decision-making behavior and the underlying neurophysiological mechanisms have not been thoroughly studied. As such, we consider the impact of MS on the emotional and cognitive functions of rats by employing the open-field test, elevated plus-maze test, and rat gambling task (RGT). Using multi-channel recordings from freely behaving rats, we assessed the effects of MS on the large scale synchrony between the basolateral amygdala (BLA) and the ACC; while also characterizing the relationship between neural spiking activity and the ongoing oscillations in theta frequency band across the BLA and ACC. The results indicated that the MS rats demonstrated anxiety-like behavior. While the RGT showed a decrease in the percentage of good decision-makers, and an increase in the percentage of poor decision-makers. Electrophysiological data revealed an increase in the total power in the theta band of the LFP in the BLA and a decrease in theta power in the ACC in MS rats. MS was also found to disrupt the spike-field coherence of the ACC single unit spiking activity to the ongoing theta oscillations in the BLA and interrupt the synchrony in the BLA-ACC pathway. We provide specific evidence that MS leads to decision-making deficits that are accompanied by alteration of the theta band LFP in the BLA-ACC circuitries and disruption of the neural network integrity. These observations may help revise fundamental notions regarding neurophysiological biomarkers to treat cognitive impairment induced by early life stress.

Compact Graph based Semi-Supervised Learning for Medical Diagnosis in Alzheimer's Disease.

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important for early diagnosis in order to slow down the disease progression and help preserve some cognitive functions of the brain. To achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern classification problem. In this letter, we introduce a graph based semi-supervised learning algorithm for Medical Diagnosis by using partly labeled samples and large amount of unlabeled samples. The new method is derived by a compact graph that can well grasp the manifold structure of medical data. Simulation results show that the proposed method can achieve better sensitivities and specificities compared with other state-of-art graph based semi-supervised learning methods.

Ultrasound imaging of the dorsalis pedis artery as an early indicator of the precursory changes for rheumatoid vasculitis: A case series.

Introduction: Clinical verification of rheumatoid vasculitis (RV) persists as a mid-to-late diagnosis with medical imaging or biopsy. Early and subclinical presentations of RV, in particular, can remain underdiagnosed in the absence of adequate diagnostic testing. In this study, the research demonstrated the precursory changes for RV in patients with rheumatoid arthritis (RA) using non-invasive ultrasound imaging of a peripheral vessel. Method: Six participants were recruited: three participants with (RA) and three age- and gender-matched healthy controls. All participants completed a Foot Health Survey Questionnaire (FHSQ), and participants with RA completed a Rheumatoid Arthritis Disease Activity Index-5 (RADAI-5). Bilateral B-mode and Doppler ultrasound of the dorsalis pedis artery (DPA) was performed. The degree of inflammation, lumen and artery diameters, lumen diameter-to-artery diameter ratio and peak systolic velocity in the proximal DPA were compared between the two groups. Results: The mean RADAI-5 score (5.4 ± 0.8 out of 10) indicated moderate disease activity amongst participants with RA. Inflammation was observed in the DPA wall in all participants with RA, compared to no inflammation observed in the control group (Friedmans two-way analysis: χ2 = 15.733, P = 0.003). Differences between groups for inflammation, lumen diameter and lumen diameter-to-artery diameter ratio were found (P < 0.034), without differences for artery diameter and peak systolic velocity (P > 0.605). DPA wall inflammation did not correlate with FHSQ scores (r = -0.770, P = 0.073). Conclusion: Despite moderate RA disease activity, this is the first study to demonstrate the use of ultrasound to observe inflammation in small vessel disease. Our findings suggest ultrasound imaging may be a viable screening tool to demonstrate arterial wall inflammation, indicating the precursory changes of RV.

Concerto of movement: how expertise shapes the synergistic control of upper limb muscles in complex motor tasks with varying tempo and dynamics.

Objective. This research aims to reveal how the synergistic control of upper limb muscles adapts to varying requirements in complex motor tasks and how expertise shapes the motor modules.Approach. We study the muscle synergies of a complex, highly skilled and flexible task-piano playing-and characterize expertise-related muscle-synergy control that permits the experts to effortlessly execute the same task at different tempo and force levels. Surface EMGs (28 muscles) were recorded from adult novice (N= 10) and expert (N= 10) pianists as they played scales and arpeggios at different tempo-force combinations. Muscle synergies were factorized from EMGs.Main results. We found that experts were able to cover both tempo and dynamic ranges using similar synergy selections and achieved better performance, while novices altered synergy selections more to adapt to the changing tempi and keystroke intensities compared with experts. Both groups relied on fine-tuning the muscle weights within specific synergies to accomplish the different task styles, while the experts could tune the muscles in a greater number of synergies, especially when changing the tempo, and switch tempo over a wider range.Significance. Our study sheds light on the control mechanism underpinning expertise-related motor flexibility in highly skilled motor tasks that require decade-long training. Our results have implications on musical and sports training, as well as motor prosthetic design.

Prothrombin Complex Concentrate vs Conservative Management in ICH Associated With Direct Oral Anticoagulants.

Importance: Intracerebral hemorrhage (ICH) associated with direct oral anticoagulant (DOAC) use carries extremely high morbidity and mortality. The clinical effectiveness of hemostatic therapy is unclear. Objective: To compare the clinical and radiological outcomes of DOAC-associated ICH treated with prothrombin complex concentrate (PCC) vs conservative management. Design, Setting, and Participants: In this population-based, propensity score-weighted retrospective cohort study, patients who developed DOAC-associated ICH from January 1, 2016, to December 31, 2021, in Hong Kong were identified. The outcomes of patients who received 25 to 50 IU/kg PCC with those who received no hemostatic agents were compared. Data were analyzed from May 1, 2022, to June 30, 2023. Main Outcomes and Measures: The primary outcome was modified Rankin scale of 0 to 3 or returning to baseline functional status at 3 months. Secondary outcomes were mortality at 90 days, in-hospital mortality, and hematoma expansion. Weighted logistic regression was performed to evaluate the association of PCC with study outcomes. In unweighted logistic regression models, factors associated with good neurological outcome and hematoma expansion in DOAC-associated ICH were identified. Results: A total of 232 patients with DOAC-associated ICH, with a mean (SD) age of 77.2 (9.3) years and 101 (44%) female patients, were included. Among these, 116 (50%) received conservative treatment and 102 (44%) received PCC. Overall, 74 patients (31%) patients had good neurological recovery and 92 (39%) died within 90 days. Median (IQR) baseline hematoma volume was 21.7 mL (3.6-66.1 mL). Compared with conservative management, PCC was not associated with improved neurological recovery (adjusted odds ratio [aOR], 0.62; 95% CI, 0.33-1.16; P = .14), mortality at 90 days (aOR, 1.03; 95% CI, 0.70-1.53; P = .88), in-hospital mortality (aOR, 1.11; 95% CI, 0.69-1.79; P = .66), or reduced hematoma expansion (aOR, 0.94; 95% CI, 0.38-2.31; P = .90). Higher baseline hematoma volume, lower Glasgow coma scale, and intraventricular hemorrhage were associated with lower odds of good neurological outcome but not hematoma expansion. Conclusions and Relevance: In this cohort study, Chinese patients with DOAC-associated ICH had large baseline hematoma volumes and high rates of mortality and functional disability. PCC treatment was not associated with improved functional outcome, hematoma expansion, or mortality. Further studies on novel hemostatic agents as well as neurosurgical and adjunctive medical therapies are needed to identify the best management algorithm for DOAC-associated ICH.

Tracing determinants of dual substrate specificity in glycoside hydrolase family 5.

Enzymes are traditionally viewed as having exquisite substrate specificity; however, recent evidence supports the notion that many enzymes have evolved activities against a range of substrates. The diversity of activities across glycoside hydrolase family 5 (GH5) suggests that this family of enzymes may contain numerous members with activities on multiple substrates. In this study, we combined structure- and sequence-based phylogenetic analysis with biochemical characterization to survey the prevalence of dual specificity for glucan- and mannan-based substrates in the GH5 family. Examination of amino acid profile differences between the subfamilies led to the identification and subsequent experimental confirmation of an active site motif indicative of dual specificity. The motif enabled us to successfully discover several new dually specific members of GH5, and this pattern is present in over 70 other enzymes, strongly suggesting that dual endoglucanase-mannanase activity is widespread in this family. In addition, reinstatement of the conserved motif in a wild type member of GH5 enhanced its catalytic efficiency on glucan and mannan substrates by 175 and 1,600%, respectively. Phylogenetic examination of other GH families further indicates that the prevalence of enzyme multispecificity in GHs may be greater than has been experimentally characterized. Single domain multispecific GHs may be exploited for developing improved enzyme cocktails or facile engineering of microbial hosts for consolidated bioprocessing of lignocellulose.

Trace Ratio Linear Discriminant Analysis for Medical Diagnosis: A Case Study of Dementia.

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering dementia is important to the administration of early treatment in order to slow down the progression of dementia symptoms. However, to achieve accurate classification, significant amount of subject feature information are involved. Hence identification of demented subjects can be transformed into a pattern recognition problem with high-dimensional nonlinear datasets. In this paper, we introduce trace ratio linear discriminant analysis (TR-LDA) for dementia diagnosis. An improved ITR algorithm (iITR) is developed to solve the TR-LDA problem. This novel method can be integrated with advanced missing value imputation method and utilized for the analysis of the nonlinear datasets in many real-world medical diagnosis problems. Finally, extensive simulations are conducted to show the effectiveness of the proposed method. The results demonstrate that our method can achieve higher accuracies for identifying the demented patients than other state-of-art algorithms.

Comparing the Graphical Features of Simple Artificial Neural Networks and Cortical Development.

Brain development is characterized by changes in connections and information processing complexity. These changes inspire the training process of artificial neural network (ANN), which requires adjusting the neuron weights and biases to enhance efficiency in performing a specific task. In this work, we found affinities in the ratio of positive and negative weights in simple ANNs during training with that of excitatory and inhibitory synapses in the cortex. Additionally, we present a graphical representation of simple ANNs formed by pruning unimportant weights and aligning neurons and connections of different layers. Our findings suggest a strong relationship between the accuracy of simple neural network and graphical representation features, with graphical features at the inflection point resembling the graphical representation of the cortex.

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology.

Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of feature points on each slice image, which is time-consuming. To speed up the process in large-scale studies, we propose a method called Deep Learning-Assisted Transformation Alignment (DLATA), which uses deep learning to automatically identify feature points in images after training on a few labeled samples. DLATA only requires approximately 10% of the sample size of other semi-automated alignment workflows. Following feature point recognition, local weighted mean method is used as a geometrical transformation to align slice images for registration, achieving better results with about 4 fewer pixels of error than other semi-automated alignment workflows. DLATA can be retrained and successfully applied to the alignment of other biological tissue slices with different stains, including the typically challenging fluorescent stains. Reference codes and trained models for Nissl-stained coronal brain slices of mice can be found at https://github.com/ALIGNMENT2023/DLATA.

Reliability of Ultrasound Imaging in Examining Integrity and Inflammation in the Dorsalis Pedis Artery.

OBJECTIVE: Assessment of small vessel inflammatory diseases such as rheumatoid vasculitis is challenging. Small arteries such as the dorsalis pedis artery (DPA) are difficult to assess for changes in the arterial wall with medical imaging. Ultrasound imaging is a viable tool for examining the integrity and inflammatory changes in the arterial wall; however, no empirical data on its reliability have been described. METHODS: We measured the intra- and inter-rater reliability of ultrasound measurements across five parameters evaluating arterial integrity of the proximal DPA in participants with and without small vessel disease. We recruited 10 participants with rheumatoid arthritis and 10 healthy controls. Two sonographers using ultrasound independently measured DPA lumen diameter, artery diameter, lumen-to-arterial diameter ratio, arterial Doppler velocity and inflammatory changes in the proximal wall of the DPA. The intraclass correlation coefficient (ICC) was used to evaluate 95% confidence intervals within and between raters. Bland-Altman analyses were used to assess limits of agreement and were compared with minimal clinically important differences (MCID). RESULTS: Four of five selected parameters were found to have excellent intra- and inter-rater reliability within and between raters (ICC = 0.903-0.996). Acceptable reliability was found for measurement of arterial blood flow velocity within raters (ICC = 0.815-0.909), but not between raters (ICC = 0.634). Standard mean errors in all parameters were within minimal clinically important differences. CONCLUSION: Ultrasound imaging has been found to be a reliable method of assessment of arterial integrity and inflammation of the proximal DPA in people with small vessel disease. Evaluation of arterial blood flow velocity requires cautious interpretation.

Tracking Bilateral Lower Limb Kinematics of Distance Runners on Treadmill Using a Single Inertial Measurement Unit.

Distance running related injuries are common, and many ailments have been associated with faulty posture. Conventional measurement of running kinematics requires sophisticated motion capture system in laboratory. In this study, we developed a wearable solution to accurately predict lower limb running kinematics using a single inertial measurement unit placed on the left lower leg. The running data collected from participants was used to train a model using long short-term memory (LSTM) neural networks with an inter-subject approach that predicted lower limb kinematics with an average accuracy of 80.2%, 85.8%, and 69.4% for sagittal hip, knee and ankle joint angles respectively for the ipsilateral limb. A comparable accuracy range was observed for the contralateral limb. The average RMSE (root mean squared error) of sagittal hip, knee and ankle were 8.76°, 13.13°, and 9.67° respectively for the ipsilateral limb. Analysis of contralateral limb kinematics was performed. The model established in this study can be used as a monitoring device to track essential running kinematics in natural running environments. Besides, the wearable solution can be an integral part of a real-time gait retraining biofeedback system for injury prevention and rehabilitation.

An iterative neural processing sequence orchestrates feeding.

Feeding requires sophisticated orchestration of neural processes to satiate appetite in natural, capricious settings. However, the complementary roles of discrete neural populations in orchestrating distinct behaviors and motivations throughout the feeding process are largely unknown. Here, we delineate the behavioral repertoire of mice by developing a machine-learning-assisted behavior tracking system and show that feeding is fragmented and divergent motivations for food consumption or environment exploration compete throughout the feeding process. An iterative activation sequence of agouti-related peptide (AgRP)-expressing neurons in arcuate (ARC) nucleus, GABAergic neurons in the lateral hypothalamus (LH), and in dorsal raphe (DR) orchestrate the preparation, initiation, and maintenance of feeding segments, respectively, via the resolution of motivational conflicts. The iterative neural processing sequence underlying the competition of divergent motivations further suggests a general rule for optimizing goal-directed behaviors.

Does Meta-Learning Improve EEG Motor Imagery Classification?

Deep learning has been applied to enhance the performance of EEG-based brain-computer interface applications. However, the cross-subject variations in EEG signals cause domain shifts and negatively affect the model performance and generalization. Meta-learning algorithms have shown fast new domain adaption in various fields, which may help solve the domain shift problems in EEG. Reptile, with satisfactory performance and low computational costs, stands out from other existing meta-learning algorithms. We integrated Reptile with a deep neural network as Reptile-EEG for the EEG motor imagery tasks, and compared Reptile-EEG with other state-of-the-art models in three motor imagery BCI benchmark datasets. Results show that Reptile-EEGdoes not outperform simple training of deep neural networks in motor imagery BCI tasks.

Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment.

Training deep neural networks (DNNs) typically requires massive computational power. Existing DNNs exhibit low time and storage efficiency due to the high degree of redundancy. In contrast to most existing DNNs, biological and social networks with vast numbers of connections are highly efficient and exhibit scale-free properties indicative of the power law distribution, which can be originated by preferential attachment in growing networks. In this work, we ask whether the topology of the best performing DNNs shows the power law similar to biological and social networks and how to use the power law topology to construct well-performing and compact DNNs. We first find that the connectivities of sparse DNNs can be modeled by truncated power law distribution, which is one of the variations of the power law. The comparison of different DNNs reveals that the best performing networks correlated highly with the power law distribution. We further model the preferential attachment in DNNs evolution and find that continual learning in networks with growth in tasks correlates with the process of preferential attachment. These identified power law dynamics in DNNs can lead to the construction of highly accurate and compact DNNs based on preferential attachment. Inspired by the discovered findings, two novel applications have been proposed, including evolving optimal DNNs in sparse network generation and continual learning tasks with efficient network growth using power law dynamics. Experimental results indicate that the proposed applications can speed up training, save storage, and learn with fewer samples than other well-established baselines. Our demonstration of preferential attachment and power law in well-performing DNNs offers insight into designing and constructing more efficient deep learning.

Age-Related Modifications of Muscle Synergies and Their Temporal Activations for Overground Walking.

Healthy ageing modifies neuromuscular control of human overground walking. Previous studies found that ageing changes gait biomechanics, but whether there is concurrent ageing-related modulation of neuromuscular control remains unclear. We analyzed gait kinematics and electromyographic signals (EMGs; 14 lower-limb and trunk muscles) collected at three speeds during overground walking in 11 healthy young adults (mean age of 23.4 years) and 11 healthy elderlies (67.2 years). Neuromuscular control was characterized by extracting muscle synergies from EMGs and the synergies of both groups were k -means-clustered. The synergies of the two groups were grossly similar, but we observed numerous cluster- and muscle-specific differences between the age groups. At the population level, some hip-motion-related synergy clusters were more frequently identified in elderlies while others, more frequent in young adults. Such differences in synergy prevalence between the age groups are consistent with the finding that elderlies had a larger hip flexion range. For the synergies shared between both groups, the elderlies had higher inter-subject variability of the temporal activations than young adults. To further explore what synergy characteristics may be related to this inter-subject variability, we found that the inter-subject variance of temporal activations correlated negatively with the sparseness of the synergies in elderlies but not young adults during slow walking. Overall, our results suggest that as humans age, not only are the muscle synergies for walking fine-tuned in structure, but their temporal activation patterns are also more heterogeneous across individuals, possibly reflecting individual differences in prior sensorimotor experience or ageing-related changes in limb neuro-musculoskeletal properties.

Memory encoding in hippocampal ensembles is negatively influenced by cannabinoid CB1 receptors.

It has previously been demonstrated that the detrimental effect on the performance of a delayed nonmatch to sample (DNMS) memory task by exogenously administered cannabinoid (CB1) receptor agonist, WIN 55212-2 (WIN), is reversed by the receptor antagonist rimonabant. In addition, rimonabant administered alone elevates DNMS performance, presumably through the suppression of negative modulation by released endocannabinoids during normal task performance. Other investigations have shown that rimonabant enhances encoding of DNMS task-relevant information on a trial-by-trial, delay-dependent basis. In this study, these reciprocal pharmacological actions were completely characterized by long-term, chronic intrahippocampal infusion of both agents (WIN and rimonabant) in successive 2-week intervals. Such long-term exposure allowed extraction and confirmation of task-related firing patterns, in which rimonabant reversed the effects of CB1 agonists. This information was then utilized to artificially impose the facilitatory effects of rimonabant and to reverse the effects of WIN on DNMS performance, by delivering multichannel electrical stimulation in the same firing patterns to the same hippocampal regions. Direct comparison of normal and WIN-injected subjects, in which rimonabant injections and ensemble firing facilitated performance, verified reversal of the modulation of hippocampal memory processes by CB1 receptor agonists, including released endocannabinoids.