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This study presents a comparative analysis of two neuron touch detection algorithms, Brain Scaffold Builder (BSB) and Snudda, within the context of neuronal modeling. Neuron touch detection is critical for accurately mapping synaptic connections, a fundamental aspect of brain simulation and understanding neuronal circuits. By evaluating these algorithms on parameters such as synapse detection accuracy and runtime efficiency across different network sizes, we aimed to determine their limitations and strengths. Our findings indicate that Snudda have superior performance in terms of runtime to that of BSB. It was also found that Snudda detected more synapses than BSB, but to more precisely determine the accuracy this study highlights the need for future work. Furthermore, future work including a larger range of network sizes and the use of real anatomical data when constructing networks could aid in gaining more knowledge on this topic., Den här studien presenterar en jämförelseanalys av två nervcells-beröring-detektionsalgoritmer, Brain Scaffold Builder (BSB) och Snudda, i sammanhanget av neuromodellering. Nervcells-beröringsdetektion är kritiskt för att noggrant kartlägga synapser, en grundläggande aspekt av hjärnsimulering och för att få förståelse för nervcellsnätverk. Genom att utvärdera dessa algoritmer utifrån parametrar som körtidseffektivitet och noggrannheten på detektionen av synapser över olika storlekar på nätverk, syftar vi till att fastställa deras styrkor och svagheter. Resultaten visar att Snudda presterar bättre med hänsyn på körtidseffektivitet jämfört med BSB. Det visades även att Snudda fann fler synapser än BSB, men att det skulle krävas fler studier för att mer exakt bedöma träffsäkerheten hos algoritmerna. Vidare poängteras behovet av studier baserade på ett större spann av nätverksstorlekar och nätverk gjorda på verklig anatomisk data för att förbättra kunskapen inom området.

This study presents a comparative analysis of two neuron touch detection algorithms, Brain Scaffold Builder (BSB) and Snudda, within the context of neuronal modeling. Neuron touch detection is critical for accurately mapping synaptic connections, a fundamental aspect of brain simulation and understanding neuronal circuits. By evaluating these algorithms on parameters such as synapse detection accuracy and runtime efficiency across different network sizes, we aimed to determine their limitations and strengths. Our findings indicate that Snudda have superior performance in terms of runtime to that of BSB. It was also found that Snudda detected more synapses than BSB, but to more precisely determine the accuracy this study highlights the need for future work. Furthermore, future work including a larger range of network sizes and the use of real anatomical data when constructing networks could aid in gaining more knowledge on this topic., Den här studien presenterar en jämförelseanalys av två nervcells-beröring-detektionsalgoritmer, Brain Scaffold Builder (BSB) och Snudda, i sammanhanget av neuromodellering. Nervcells-beröringsdetektion är kritiskt för att noggrant kartlägga synapser, en grundläggande aspekt av hjärnsimulering och för att få förståelse för nervcellsnätverk. Genom att utvärdera dessa algoritmer utifrån parametrar som körtidseffektivitet och noggrannheten på detektionen av synapser över olika storlekar på nätverk, syftar vi till att fastställa deras styrkor och svagheter. Resultaten visar att Snudda presterar bättre med hänsyn på körtidseffektivitet jämfört med BSB. Det visades även att Snudda fann fler synapser än BSB, men att det skulle krävas fler studier för att mer exakt bedöma träffsäkerheten hos algoritmerna. Vidare poängteras behovet av studier baserade på ett större spann av nätverksstorlekar och nätverk gjorda på verklig anatomisk data för att förbättra kunskapen inom området.

Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Characterization Tool (SACT) is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. SACT automatically detects puncta of immunofluorescence labeling from candidate antibodies and determines whether a punctum belongs to a synapse. The molecular composition and size of the target synapses expected to contain the antigen is determined by the user, based on biological knowledge. Operationally, the presence of a synapse is defined by the colocalization or adjacency of the candidate antibody punctum to one or more reference antibody puncta. The outputs of SACT are automatically computed measurements such as target synapse density and target specificity ratio that reflect the sensitivity and specificity of immunolabeling with a given candidate antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.

Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Characterization Tool (SACT) is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. SACT automatically detects puncta of immunofluorescence labeling from candidate antibodies and determines whether a punctum belongs to a synapse. The molecular composition and size of the target synapses expected to contain the antigen is determined by the user, based on biological knowledge. Operationally, the presence of a synapse is defined by the colocalization or adjacency of the candidate antibody punctum to one or more reference antibody puncta. The outputs of SACT are automatically computed measurements such as target synapse density and target specificity ratio that reflect the sensitivity and specificity of immunolabeling with a given candidate antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.