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Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures.

Charlesworth P, Cotterill E, Morton A, Grant SG, Eglen SJ - Neural Dev (2015)

Bottom Line: Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks.Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns.All code and data relating to this report are freely available for others to use.

View Article: PubMed Central - PubMed

Affiliation: Genes to Cognition Programme, Wellcome Trust Sanger Institute, Genome Campus, CB10 1SA, Hinxton, UK. pc451@cam.ac.uk.

ABSTRACT

Background: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks.

Results: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every 2 or 3 days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using 11 features. Three out of 11 features showed striking differences in activity between hippocampal and cortical networks: (1) interburst intervals are less variable in spike trains from hippocampal cultures; (2) hippocampal networks have higher correlations and (3) hippocampal networks generate more robust theta-bursting patterns. Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks.

Conclusions: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising the phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.

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Related in: MedlinePlus

Characterisation of spontaneous activity in hippocampal and cortical networks.(A–K) Values of one feature (named on the y-axis) as a function of age. Box plots show the median and interquartile range, with whiskers extending out to the most extreme values within 1.5 times the interquartile range. Individual points outside this range are regarded as outliers and drawn as points; in a few cases these outliers are not drawn to keep the y-axis within a meaningful range. Underneath each age, stars denote significant difference of median values for cortical and hippocampal networks at either 0.05 (*) or 0.01 (**) level (Mann–Whitney test, with P values corrected for multiple comparisons with false discovery rate method). (L) Number of arrays analysed at each age. CTX, cortex; CV, coefficient of variation; DIV, days in vitro; HPC, hippocampus; IBI, interburst interval; w/in, within.
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Fig3: Characterisation of spontaneous activity in hippocampal and cortical networks.(A–K) Values of one feature (named on the y-axis) as a function of age. Box plots show the median and interquartile range, with whiskers extending out to the most extreme values within 1.5 times the interquartile range. Individual points outside this range are regarded as outliers and drawn as points; in a few cases these outliers are not drawn to keep the y-axis within a meaningful range. Underneath each age, stars denote significant difference of median values for cortical and hippocampal networks at either 0.05 (*) or 0.01 (**) level (Mann–Whitney test, with P values corrected for multiple comparisons with false discovery rate method). (L) Number of arrays analysed at each age. CTX, cortex; CV, coefficient of variation; DIV, days in vitro; HPC, hippocampus; IBI, interburst interval; w/in, within.

Mentions: During development, there are slight, statistically significant differences in firing rates, with median firing rates being slightly higher for hippocampal networks, but overall there are no key differences at maturity (Figure 3A).Figure 3


Quantitative differences in developmental profiles of spontaneous activity in cortical and hippocampal cultures.

Charlesworth P, Cotterill E, Morton A, Grant SG, Eglen SJ - Neural Dev (2015)

Characterisation of spontaneous activity in hippocampal and cortical networks.(A–K) Values of one feature (named on the y-axis) as a function of age. Box plots show the median and interquartile range, with whiskers extending out to the most extreme values within 1.5 times the interquartile range. Individual points outside this range are regarded as outliers and drawn as points; in a few cases these outliers are not drawn to keep the y-axis within a meaningful range. Underneath each age, stars denote significant difference of median values for cortical and hippocampal networks at either 0.05 (*) or 0.01 (**) level (Mann–Whitney test, with P values corrected for multiple comparisons with false discovery rate method). (L) Number of arrays analysed at each age. CTX, cortex; CV, coefficient of variation; DIV, days in vitro; HPC, hippocampus; IBI, interburst interval; w/in, within.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4320829&req=5

Fig3: Characterisation of spontaneous activity in hippocampal and cortical networks.(A–K) Values of one feature (named on the y-axis) as a function of age. Box plots show the median and interquartile range, with whiskers extending out to the most extreme values within 1.5 times the interquartile range. Individual points outside this range are regarded as outliers and drawn as points; in a few cases these outliers are not drawn to keep the y-axis within a meaningful range. Underneath each age, stars denote significant difference of median values for cortical and hippocampal networks at either 0.05 (*) or 0.01 (**) level (Mann–Whitney test, with P values corrected for multiple comparisons with false discovery rate method). (L) Number of arrays analysed at each age. CTX, cortex; CV, coefficient of variation; DIV, days in vitro; HPC, hippocampus; IBI, interburst interval; w/in, within.
Mentions: During development, there are slight, statistically significant differences in firing rates, with median firing rates being slightly higher for hippocampal networks, but overall there are no key differences at maturity (Figure 3A).Figure 3

Bottom Line: Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks.Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns.All code and data relating to this report are freely available for others to use.

View Article: PubMed Central - PubMed

Affiliation: Genes to Cognition Programme, Wellcome Trust Sanger Institute, Genome Campus, CB10 1SA, Hinxton, UK. pc451@cam.ac.uk.

ABSTRACT

Background: Neural circuits can spontaneously generate complex spatiotemporal firing patterns during development. This spontaneous activity is thought to help guide development of the nervous system. In this study, we had two aims. First, to characterise the changes in spontaneous activity in cultures of developing networks of either hippocampal or cortical neurons dissociated from mouse. Second, to assess whether there are any functional differences in the patterns of activity in hippocampal and cortical networks.

Results: We used multielectrode arrays to record the development of spontaneous activity in cultured networks of either hippocampal or cortical neurons every 2 or 3 days for the first month after plating. Within a few days of culturing, networks exhibited spontaneous activity. This activity strengthened and then stabilised typically around 21 days in vitro. We quantified the activity patterns in hippocampal and cortical networks using 11 features. Three out of 11 features showed striking differences in activity between hippocampal and cortical networks: (1) interburst intervals are less variable in spike trains from hippocampal cultures; (2) hippocampal networks have higher correlations and (3) hippocampal networks generate more robust theta-bursting patterns. Machine-learning techniques confirmed that these differences in patterning are sufficient to classify recordings reliably at any given age as either hippocampal or cortical networks.

Conclusions: Although cultured networks of hippocampal and cortical networks both generate spontaneous activity that changes over time, at any given time we can reliably detect differences in the activity patterns. We anticipate that this quantitative framework could have applications in many areas, including neurotoxicity testing and for characterising the phenotype of different mutant mice. All code and data relating to this report are freely available for others to use.

Show MeSH
Related in: MedlinePlus