Biology subjects

Anderson, D. N.

Publications and source records attributed to Anderson, D. N..

6 recordsLinked to original sources

bioRxiv2025

The nucleus accumbens to ventral pallidum pathway regulates social play behavior via sex-specific mechanisms in juvenile rats

Social play behavior is a rewarding behavior predominantly displayed by juveniles of various mammalian species, including humans and rats. Although the mesolimbic reward system is involved in the regulation of social play, how brain regions in this system interact to regulate social play behavior is unknown. Here, we determined the involvement of the ventral pallidum (VP) as well as inputs from the nucleus accumbens (NAc) to the VP in the regulation of social play in male and female juvenile rats. We show that acute pharmacological inactivation of the VP, via microinfusion of the GABA-A receptor agonist muscimol, decreased social play behaviors in both sexes. Next, using Gad1-iCre rats, we show that chemogenetic stimulation of NAcGABA terminals in the VP decreased VP neuronal activation and decreased social play behaviors in both sexes. These findings together indicate that reduced inhibitory NAc input to the VP permits activation of the VP which facilitates the expression of social play behaviors. Lastly, we show that the equal expression of social play behavior in males and females is associated with a female-specific increase in NAc shell activation and a male-specific decrease in activation of the NAc shell neurons projecting to the VP. These sex-specific changes in NAc activity following social play exposure eliminated baseline sex differences in NAc activity. In conclusion, these findings support a model in which the sex-specific modulation of NAc inhibitory input to the VP facilitates activation of the VP that is necessary for the typical and equal expression of social play behavior in male and female juvenile rats.

neuroscience↗

bioRxiv2025

DynamoSort: Using machine learning approaches for the automatic classification of seizure dynamotypes

ObjectiveEpilepsy is characterised by unprovoked and recurring seizures, which can be electrically measured using electroencephalograms (EEG). To better understand the underlying mechanisms of seizures, researchers are exploring their temporal dynamics through the lens of dynamical systems modelling. Seizure initiation and termination patterns of spiking amplitude and frequency can be sorted into "dynamotypes", which may be able to serve as biomarkers for intervention. However, manual classification of these dynamotypes requires trained raters and is prone to variability. To address this, we developed DynamoSort, a machine-learning algorithm for automatic seizure onset and offset classification. MethodsWe used approximately 2100 seizures from an intra-amygdala kainic acid (IAK) mouse model of mesial temporal lobe epilepsy, categorized by five trained raters. MATLABs classification learner application was used to create an ensemble model to score and label dynamotypes of individual seizures based on spiking and frequency features. ResultsDynamotype classification of real EEG data lacks a definitive ground truth, with mean inter-rater agreement at 73.4% for onset and 64.2% for offset types. Despite this, DynamoSort achieved a mean area under the curve (AUC) of 0.81 for onset and a mean AUC of 0.75 for offset types. Machine-human agreement was not significantly different from human-to-human agreement. To address the lack of ground truth in ratings, DynamoSort assigns probabilistic scores (-20 to 20), to indicate similarity to each seizure dynamotype based on spiking features, allowing for a characterization of seizure dynamics on a spectrum rather than the traditional qualitative taxonomy. SignificanceAutomating the classification of dynamotypes is a critical step for their inclusion as a biomarker in clinical and research applications. DynamoSort is a straightforward, open-access tool that uses automatic labelling and probabilistic scoring to quantify subtle changes in seizure onset and offset dynamics. Key PointsO_LIDynamotypes are a promising seizure categorization system, but is prone to interrater variability and lacks a ground truth. C_LIO_LIMachine learning can be used to automatically classify seizure onsets and offsets into appropriate dynamotypes based on spike features. C_LIO_LIAgreement between DynamoSort and human raters rivals typical agreement rates in trained human raters. C_LIO_LIDynamoSort uses probabilistic scoring to quantify subtle changes in seizure onset and offset, allowing for a quantitative characterisation. C_LI

neuroscience↗

bioRxiv2023

A novel naive Bayes approach to identifying grooming behaviors in the force-plate actometric platform

BackgroundSelf-grooming behavior in rodents serves as a valuable model for investigating stereotyped and perseverative responses. Most current grooming analyses primarily rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. New MethodGrooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are then input into a naive Bayes classifier, trained with manual video observations. To validate the effectiveness of this method, we applied it to the behavioral analysis of the early-life striatal cholinergic interneuron depletion (CIN-d) mouse, a model of tic pathophysiology recently developed in our laboratory, which exhibits prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. ResultsThe naive Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations compared to controls. However, this elevation was not correlated with increases in grooming force. Notably, haloperidol, a benchmark therapy for tic disorders, reduced both grooming force and duration. Comparison with Existing MethodsIn contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. ConclusionsOur novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of tic disorders and other psychiatric conditions.

neuroscience↗

bioRxiv2020

Defining the impact of deep brain stimulation contact size and shape on neural selectivity

BackgroundUnderstanding neural selectivity is essential for optimizing medical applications of deep brain stimulation (DBS). We previously showed that modulation of the DBS waveform can induce changes in orientation-based selectivity, and that lengthening of DBS pulses or directional segmentation can reduce preferential selectivity for large axons. In this work, we sought to answer a simple, but important question: how do the size and shape of the contact influence neural selectivity? MethodsWe created multicompartment neuron models for several axon diameters and used finite element modeling with standard-sized cylindrical leads to determine the effects on changing contact size and shape on axon activation profiles and volumes of tissue activated. Contacts ranged in size from 0.04 to 16 mm2, compared with a standard size of 6 mm2. ResultsWe found that changes in contact size induce substantial changes in orientation-based selectivity in the context of a cylindrical lead, and rectangular shaping of the contact can alter this selectivity. Smaller contact sizes were more effective in constraining neural activation to small, nearby axons representative of grey matter. However, micro-scale contacts enable only limited spread of neural activation before exceeding standard charge density limitations; further, energetic efficiency is optimized by somewhat larger contacts. InterpretationsSmall-scale contacts are optimal for constraining stimulation in nearby grey matter and avoiding orientation-selective activation. However, given charge density limitations and energy inefficiency of micro-scale contacts, our results suggest that contacts about half the size of those on segmented clinical leads may optimize efficiency and charge density limitation avoidance.

neuroscience↗

bioRxiv2019

Neural Selectivity, Efficiency, and Dose Equivalence in Deep Brain Stimulation through Pulse Width Tuning and Segmented Electrodes.

BackgroundAchieving deep brain stimulation (DBS) dose equivalence is challenging, especially with pulse width tuning and directional contacts. Further, the precise effects of pulse width tuning are unknown.\n\nMethodsWe created multicompartment neuron models for two axon diameters and used finite element modeling to determine extracellular influence from standard and segmented electrodes. We analyzed axon activation profiles and calculated volumes of tissue activated.\n\nResultsLong pulse widths focus the stimulation effect on small, nearby fibers, suppressing white matter tract activation (responsible for some DBS side effects) and improving battery utilization. Directional leads enable similar benefits to a greater degree. We derive equations for equivalent activation with pulse width tuning and segmented contacts.\n\nInterpretationsWe find agreement with classic studies and reinterpret recent articles concluding that short pulse widths focus the stimulation effect on small, nearby fibers, decrease side effects, and improve power consumption. Our field should reconsider shortened pulse widths.

neuroscience↗

bioRxiv2018

A retrospective evaluation of automated optimization of deep brain stimulation parameters

ObjectiveWe performed a retrospective analysis of an optimization algorithm for the computation of patient-specific multipolar stimulation configurations employing multiple independent current/voltage sources. We evaluated whether the obtained stimulation configurations align with clinical data and whether the optimized stimulation configurations have the potential to lead to an equal or better stimulation of the target region as manual programming, while reducing the time required for programming sessions.\n\nMethodsFor three patients (five electrodes) diagnosed with essential tremor, we derived optimized multipolar stimulation configurations using an approach that is suitable for the application in clinical practice. To evaluate the automatically derived stimulation settings, we compared them to the results of the monopolar review.\n\nResultsWe observe a good agreement between the findings of the monopolar review and the optimized stimulation configurations, with the algorithm assigning the maximal voltage in the optimized multipolar pattern to the contact that was found to lead to the best therapeutic effect in the clinical monopolar review in all cases. Additionally, our simulation results predict that the optimized stimulation settings lead to the activation of an equal or larger volume fraction of the target compared to the manually determined settings in all cases.\n\nConclusionsOur results demonstrate the feasibility of an automatic determination of optimal DBS configurations and motivate a further evaluation of the applied optimization algorithm.

bioengineering↗