{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "

Tutorial: Synaptic connectivity and dynamics in mouse and human cortex

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\n", "\n", "This tutorial will show you how to access the [Synaptic Physiology Dataset](https://portal.brain-map.org/explore/connectivity/synaptic-physiology) from the Allen Institute for Brain Science. This dataset describes properties of synapses that were recorded using patch-clamp electrophysiology in mouse and human neocortical tissue. The main purpose is to understand the relationship between _cell types_ and _synapse properties_ in local microcircuits:\n", "\n", "- What is the probability of finding a synaptic connection between two cells, and how is that affected by cell type? \n", "- How is connectivity affected by the distance between two cells, or by other experimental parameters?\n", "- How is cell type related to other synapse properties such as strength, latency, kinetics (PSP shape), and dynamics (stochasticity and short-term plasticity)?\n", "- How can we best model synaptic connectivity and dynamics, and what can these models tell us about the function of the microcircuit?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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Multi-patch electrophysiology

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\n", "\n", "The experiments in the Synaptic Physiology Dataset are performed using patch clamp electrophysiology in brain slices with up to 8 electrodes simultaneously. The resulting data are complex; understanding the limitations of the experimental methods is necessary in order to avoid analysis mistakes. For complete details on our methods, see our [website](https://portal.brain-map.org/explore/connectivity/synaptic-physiology) and recent [bioRxiv publication](https://www.biorxiv.org/content/10.1101/2021.03.31.437553v2). When in doubt, you can ask questions on our [forum](https://community.brain-map.org/) with the tag `synaptic-physiology`.\n", "\n", "In patch-clamp electrophysiology, we use glass electrodes to gain direct electrical access to the _interior_ of a neuron. This allows us to precisely control the spiking of individual cells and to record synaptic currents that are too small to be observed by any other method (for now). \n", "\n", "![multipatch](source/images/multipatch.svg)\n", "\n", "In a single experiment, we patch up to 8 neurons simultaneously. Each neuron is stimulated to fire patterns of action potentials while we record the membrane potential of the other neurons. If two neurons are connected by a synapse, we should be able to see synaptic currents within a few ms of each presynaptic spike (although many synapses require averaging to reduce noise before the response is visible)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
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Synapse characterization

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\n", "\n", "Each synapse in our dataset is characterized by their responses to many presynaptic spikes -- each spike elicits a \"postsynaptic potential\" (PSP), and by looking at many responses we can determine properties such as the average amplitude, the latency from spike to response, the PSP shape, and the trial-to-trial variance. \n", "\n", "![psp_measurements](source/images/psp_measurements.svg)\n", "\n", "We also rapidly stimulate each cell to determine how the synapse reacts dynamically -- many synapses will either *facilitate* or *depress* in this situation.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
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Dataset and database

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\n", "\n", "The Synaptic Physiology Dataset contains the results of thousands of multipatch experiments. For each experiment, we store three major types of information:\n", "\n", "- **Experiment metadata:** species, brain region, and experimental conditions\n", "- **Cell properties:** location (including cortical layer), morphology, transgenic reporters, and intrinsic electrophysiological features\n", "- **Synapse properties:** strength, latency, kinetics (PSP shape), and dynamics (variance and short-term plasticity)\n", "\n", "These data are stored in a relational database (an sqlite file) and spread out over many tables. It is possible to access these tables using SQL or sqlalchemy; however, for this tutorial we will use helper methods that handle most of the queries for us.\n", "\n", "![database_schema](source/images/synphys_20_simple.svg)\n", "\n", "The diagram above shows a selection of more commonly-used resources in the relational database. The complete set of tables and columns is decribed in the [schema documentation](https://aisynphys.readthedocs.io/en/master/api_schema.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Querying synapse data\n", "---------------------\n", "\n", "As a simple starting point, let's get a dataframe that describes the properties of all human synapses in the dataset. We will use the function `db.pair_query`, which returns one row for each _cell pair_ in the database. Cell pairs are _ordered_, which means that the connections from cell A→B and cell B→A will have two different rows." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading https://aisynphys.s3-us-west-1.amazonaws.com/synphys_r2.0-pre4_small.sqlite =>\n", " /home/luke/ai_synphys_cache/database/synphys_r2.0-pre4_small.sqlite\n", " [####################] 100.00% (160.52 MB / 160.5 MB) 10.298 MB/s 0:00:00 remaining\n", " done.\n", "Loaded 376 synapses\n" ] }, { "data": { "text/html": [ "
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pair.idpair.experiment_idpair.pre_cell_idpair.post_cell_idpair.has_synapsepair.has_polysynapsepair.has_electricalpair.crosstalk_artifactpair.n_ex_test_spikespair.n_in_test_spikes...synapse_model.ml_variability_stp_induced_state_50hzsynapse_model.ml_variability_change_initial_50hzsynapse_model.ml_variability_change_induction_50hzsynapse_model.ml_paired_event_correlation_1_2_rsynapse_model.ml_paired_event_correlation_1_2_psynapse_model.ml_paired_event_correlation_2_4_rsynapse_model.ml_paired_event_correlation_2_4_psynapse_model.ml_paired_event_correlation_4_8_rsynapse_model.ml_paired_event_correlation_4_8_psynapse_model.meta
056768165098349829TrueFalseFalseNaN77259...-0.5373580.2383050.5225400.0044160.921544-0.0485640.2784350.0408240.362318{'pair_ext_id': '1525829536.548 7 2'}
156794165198419840TrueFalseFalseNaN15484...NaNNaNNaNNaNNaNNaNNaNNaNNaNNone
256787165198419836TrueFalseFalseNaN8484...NaNNaNNaNNaNNaNNaNNaNNaNNaNNone
356733165198379839TrueFalseFalseNaN51684...0.5518890.5025881.6335430.0226350.613610-0.0862330.053981-0.0505570.259160{'pair_ext_id': '1525841351.681 2 4'}
456718165198369837TrueFalseFalseNaN68084...1.5325160.0341941.4503140.0463530.300931-0.0610780.1726950.0563260.208631{'pair_ext_id': '1525841351.681 1 2'}
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5 rows × 363 columns

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" ], "text/plain": [ " pair.id pair.experiment_id pair.pre_cell_id pair.post_cell_id \\\n", "0 56768 1650 9834 9829 \n", "1 56794 1651 9841 9840 \n", "2 56787 1651 9841 9836 \n", "3 56733 1651 9837 9839 \n", "4 56718 1651 9836 9837 \n", "\n", " pair.has_synapse pair.has_polysynapse pair.has_electrical \\\n", "0 True False False \n", "1 True False False \n", "2 True False False \n", "3 True False False \n", "4 True False False \n", "\n", " pair.crosstalk_artifact pair.n_ex_test_spikes pair.n_in_test_spikes ... \\\n", "0 NaN 772 59 ... \n", "1 NaN 154 84 ... \n", "2 NaN 84 84 ... \n", "3 NaN 516 84 ... \n", "4 NaN 680 84 ... \n", "\n", " synapse_model.ml_variability_stp_induced_state_50hz \\\n", "0 -0.537358 \n", "1 NaN \n", "2 NaN \n", "3 0.551889 \n", "4 1.532516 \n", "\n", " synapse_model.ml_variability_change_initial_50hz \\\n", "0 0.238305 \n", "1 NaN \n", "2 NaN \n", "3 0.502588 \n", "4 0.034194 \n", "\n", " synapse_model.ml_variability_change_induction_50hz \\\n", "0 0.522540 \n", "1 NaN \n", "2 NaN \n", "3 1.633543 \n", "4 1.450314 \n", "\n", " synapse_model.ml_paired_event_correlation_1_2_r \\\n", "0 0.004416 \n", "1 NaN \n", "2 NaN \n", "3 0.022635 \n", "4 0.046353 \n", "\n", " synapse_model.ml_paired_event_correlation_1_2_p \\\n", "0 0.921544 \n", "1 NaN \n", "2 NaN \n", "3 0.613610 \n", "4 0.300931 \n", "\n", " synapse_model.ml_paired_event_correlation_2_4_r \\\n", "0 -0.048564 \n", "1 NaN \n", "2 NaN \n", "3 -0.086233 \n", "4 -0.061078 \n", "\n", " synapse_model.ml_paired_event_correlation_2_4_p \\\n", "0 0.278435 \n", "1 NaN \n", "2 NaN \n", "3 0.053981 \n", "4 0.172695 \n", "\n", " synapse_model.ml_paired_event_correlation_4_8_r \\\n", "0 0.040824 \n", "1 NaN \n", "2 NaN \n", "3 -0.050557 \n", "4 0.056326 \n", "\n", " synapse_model.ml_paired_event_correlation_4_8_p \\\n", "0 0.362318 \n", "1 NaN \n", "2 NaN \n", "3 0.259160 \n", "4 0.208631 \n", "\n", " synapse_model.meta \n", "0 {'pair_ext_id': '1525829536.548 7 2'} \n", "1 None \n", "2 None \n", "3 {'pair_ext_id': '1525841351.681 2 4'} \n", "4 {'pair_ext_id': '1525841351.681 1 2'} \n", "\n", "[5 rows x 363 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from aisynphys.database import SynphysDatabase\n", "db = SynphysDatabase.load_current('small')\n", "\n", "query = db.pair_query(\n", " experiment_type='standard_multipatch', # filter: just multipatch experiments\n", " species='human', # filter: only human data\n", " synapse=True, # filter: only cell pairs connected by synapse\n", " synapse_type='ex', # filter: only excitatory synapses\n", " preload=['synapse', 'cell'], # include extra tables that contain synapse AND cell properties\n", ")\n", "synapses = query.dataframe() # query all records and convert to pandas dataframe\n", "\n", "print(f\"Loaded {len(synapses)} synapses\")\n", "synapses.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['3', None, '2', '4', '5', '6', '1'], dtype=object)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "synapses['pre_cortical_cell_location.cortical_layer'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This dataframe contains many columns that describe the properties of each synapse. These columns are described in the [documentation](https://aisynphys.readthedocs.io/en/master/api_schema.html), but a few common ones are:\n", "\n", "| | |\n", "|:-----------------------------------|:----------------------------------------------|\n", "| **synapse.psp_amplitude** | Median amplitude of resting-state PSPs |\n", "| **synapse.latency** | Time between presynaptic spike and PSP onset |\n", "| **synapse.psp_rise_time** | Time from onset to peak of averaged PSP |\n", "| **synapse.psp_decay_tau** | Time constant of PSP decay phase |\n", "| **dynamics.stp_induction_50hz** | A metric of synaptic facilitation / depression induced by 50 Hz spike trains |\n", "| **dynamics.variability_resting_state** | Adjusted coefficient of variation of PSP amplitudes |\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn\n", "import matplotlib.pyplot as plt\n", "\n", "seaborn.histplot(synapses['synapse.psp_amplitude'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "
\n", "

Comparing synapse properties across cell types

\n", "
\n", "\n", "To make the most of this dataset, we will usually want to group synapses based on the cells they connect. For example, how do human synapses differ when their cells are in layer 2 versus layer 3? The dataset offers several sources of information for grouping cells together:\n", "\n", "![cell_selection](source/images/cell_selection.svg)\n", "\n", "\n", "To begin grouping the data, we need to know where to find the relevant cell properties in our dataframe. Again, we could check the [documentation](https://aisynphys.readthedocs.io/en/master/api_schema.html#cell), but the most commonly used cell features are:\n", "\n", "| | |\n", "|:----------------|:--------------|\n", "| **cell.cell_class** | 'ex' (excitatory) or 'in' (inhibitory) |\n", "| **cell.cre_type** | Cre reporter observed in cell (mouse only for now) |\n", "| **cortical_cell_location.cortical_layer** | Cortical layer of cell soma |\n", "| **morphology.dendrite_type** | 'spiny' (excitatory cells), 'aspiny' (inhibitory cells), or 'sparsely spiny' (ambiguous) |\n", "\n", "Note that, in the dataframe, each of the column names above is prepended with either `pre_` or `post_`, depending on which cell is described." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots()\n", "# ax.set_yscale('log')\n", "seaborn.swarmplot(\n", " data=synapses, \n", " x='pre_cortical_cell_location.cortical_layer', \n", " y='dynamics.stp_induction_50hz', \n", " order=['1', '2', '3', '4', '5', '6'],\n", " ax=ax,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Above we can see that most of the synapses in our human dataset are from layers 2 and 3, and also that layer 2 cells tend to express more synaptic facilitation compared to layer 3. \n", "\n", "\n", "### Defining more complex cell categories\n", "\n", "The approach above can take us a long way, but eventually we will want to categorize cells based on more complex criteria, which can get a little messy. To make this easier, we provide tools that take care of the most common tasks in cell categorization. In the example below, we define a set of mouse cell classes based on a combination of transgenic (CRE) reporters, cortical layer, and dendrite morphology:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from aisynphys.cell_class import CellClass, classify_pair_dataframe\n", "\n", "cell_categories = {\n", " 'L2/3 Excit.': CellClass(cell_class='ex', cortical_layer='2/3'),\n", " 'L2/3 Pvalb': CellClass(cre_type='pvalb', cortical_layer='2/3'),\n", " 'L2/3 Sst': CellClass(cre_type='sst', cortical_layer='2/3'),\n", " 'L2/3 Vip': CellClass(cre_type='vip', cortical_layer='2/3'),\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have defined 4 cell categories above. Three categories are layer 2/3 inhibitory cells that express either Pvalb, Sst, or Vip. The fourth category include all excitatory cells in layer 2/3. These cells might be identified by any combination of excitatory transgenic markers, dendritic morphology, or excitatory synapse projection. Next, we will load all mouse synapses from the database and create 2 new dataframe columns that indicate the categories of pre- and postsynaptic cells:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded 2555 synapses\n" ] } ], "source": [ "# load a dataframe of all all mouse synapses\n", "query = db.pair_query(\n", " experiment_type='standard_multipatch', # filter: just multipatch experiments\n", " species='mouse', # filter: only human data\n", " synapse=True, # filter: only cell pairs connected by synapse\n", " preload=['synapse', 'cell'], # include extra tables that contain synapse AND cell properties\n", ")\n", "synapses = query.dataframe() # query all records and convert to pandas dataframe\n", "print(f\"Loaded {len(synapses)} synapses\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([None, 'L2/3 Pvalb', 'L2/3 Sst', 'L2/3 Vip', 'L2/3 Excit.'],\n", " dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add columns giving categories for pre and postsynaptic cells\n", "classify_pair_dataframe(cell_categories, synapses, col_names=('pre_category', 'post_category'))\n", "\n", "# check:\n", "synapses['pre_category'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ".. and with our cells categorized, we can now display any metrics grouped by those categories. We have 4 categories of presynaptic cells, and 4 of postsynaptic cells, for a total of 16 pre/post combinations. If we ask pandas to make a pivot table based on these categories, we can easily visualize the matrix of results:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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post_categoryL2/3 Excit.L2/3 PvalbL2/3 SstL2/3 Vip
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L2/3 Excit.0.0019300.0013500.0015480.001717
L2/3 Pvalb0.0009360.0010910.001009NaN
L2/3 Sst0.0011190.0014200.0013990.001054
L2/3 Vip0.0017230.0014380.0015690.002233
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" ], "text/plain": [ "post_category L2/3 Excit. L2/3 Pvalb L2/3 Sst L2/3 Vip\n", "pre_category \n", "L2/3 Excit. 0.001930 0.001350 0.001548 0.001717\n", "L2/3 Pvalb 0.000936 0.001091 0.001009 NaN\n", "L2/3 Sst 0.001119 0.001420 0.001399 0.001054\n", "L2/3 Vip 0.001723 0.001438 0.001569 0.002233" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "latency = synapses.pivot_table(\n", " values='synapse.latency',\n", " index='pre_category',\n", " columns='post_category',\n", " aggfunc='mean',\n", " fill_value=float('nan'),\n", ")\n", "\n", "latency" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas generates the pivot table above by combining several steps:\n", "1. Create a table with all possible `pre_category` values as rows, and all possible `post_category` values as columns.\n", "2. For each element in this table, group together all rows from the dataframe that have this combination of `pre_category` and `post_category`. \n", "3. Insert into each table element the mean of `synapse.latency` for all rows that were grouped to that element.\n", "\n", "With seaborn, we can visualize this table as a heatmap:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "hm = sns.heatmap(\n", " latency * 1000, \n", " cmap='viridis', vmin=1, vmax=2, square=True,\n", " cbar_kws={\"ticks\":[1, 1.5, 2], 'label': 'Latency (ms)'}\n", ")\n", "hm.set_xlabel(\"postsynaptic\", fontsize=14)\n", "hm.set_ylabel(\"presynaptic\", fontsize=14);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The procedure above is common enough that a single function exists to run the database query, cell categorization, and plotting all in one. Let's try this with a less restrictive set of cell categories:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "cell_categories = {\n", " 'Excit.': CellClass(cell_class='ex'),\n", " 'Pvalb': CellClass(cre_type='pvalb'),\n", " 'Sst': CellClass(cre_type='sst'),\n", " 'Vip': CellClass(cre_type='vip'),\n", "}" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/luke/miniconda3/envs/aisynphys/lib/python3.9/site-packages/pandas/io/sql.py:1424: SAWarning: TypeDecorator FloatType() will not produce a cache key because the ``cache_ok`` flag is not set to True. Set this flag to True if this type object's state is safe to use in a cache key, or False to disable this warning.\n", " return self.connectable.execution_options().execute(*args, **kwargs)\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from aisynphys.ui.notebook import cell_class_matrix\n", "\n", "fig,ax = plt.subplots(1, 3, figsize=(16, 3))\n", "\n", "metrics = ['pulse_amp_90th_percentile', 'stp_induction_50hz', 'psc_rise_time']\n", "\n", "for i, metric in enumerate(metrics):\n", " cell_class_matrix(\n", " pre_classes=cell_categories, \n", " post_classes=cell_categories,\n", " metric=metric, \n", " class_labels=None, ax=ax[i],\n", " db=db, pair_query_args={\n", " 'experiment_type': 'standard multipatch',\n", " 'synapse': True,\n", " 'species': 'mouse',\n", " }\n", " );" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Measuring connectivity\n", "---------------------------------\n", "\n", "If we check two cells at random, what is the probability of finding a connection between them? If I want to build a network model of the cortex, which cells should I connect together? Measuring connectivity in a dataset like this can be surprisingly complex. Several factors can affect the connectivity we see:\n", "\n", "- Cell type-dependent differences in connectivity\n", "- Spatial effects of connectivity--intersomatic distance and relative positioning\n", "- False negatives:\n", " - Connections severed during tissue preparation\n", " - Synapses with low SNR\n", " \n", "The first approach we will make to measure connectivity considers only the first point--we will look at the _proportion_ of cell pairs that were connected, grouped by cell type." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded 23311 cell pairs\n" ] } ], "source": [ "mouse_pairs = db.pair_query(\n", " experiment_type='standard multipatch', # filter: just multipatch experiments\n", " species='mouse', # filter: only mouse data\n", " synapse_probed=True, # filter: only cell pairs that were checked for connectivity\n", " preload=['cell'] # include tables that describe cell properties\n", ").dataframe()\n", "\n", "print(f\"Loaded {len(mouse_pairs)} cell pairs\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A single row in this dataframe contains information about a cell pair, which represents a _possible_ connection from one cell to another.\n", "\n", "Note that in prior queries we used `synapse=True` to select only connected cell pairs. Here we are interested in measuring connectivity, so we also need to know about pairs that were checked for connectivity, but where no connection was found. So instead, we used `synapse_probed=True`, which returns all cell pairs that were checked for connectivity, regardless of whether a connection was found.\n", "\n", "Next, we categorize all cells like before, and then measure the proportion of connected pairs for each group:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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post_categoryExcit.PvalbSstVip
pre_category
Excit.NaNNaNNaNNaN
PvalbNaNNaNNaNNaN
SstNaNNaNNaNNaN
VipNaNNaNNaNNaN
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" ], "text/plain": [ "post_category Excit. Pvalb Sst Vip\n", "pre_category \n", "Excit. NaN NaN NaN NaN\n", "Pvalb NaN NaN NaN NaN\n", "Sst NaN NaN NaN NaN\n", "Vip NaN NaN NaN NaN" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add two new columns with pre- and postsynaptic cell categories\n", "classify_pair_dataframe(cell_categories, mouse_pairs, col_names=('pre_category', 'post_category'))\n", "\n", "# make a pivot table count the number of pairs in each matrix element\n", "probed = mouse_pairs.pivot_table(\n", " values='pair.id', index='pre_category', columns='post_category', \n", " aggfunc=len, fill_value=1\n", ")\n", "\n", "# pivot again, but this time count the connected pairs\n", "connected = mouse_pairs.pivot_table(\n", " values='pair.has_synapse', index='pre_category', columns='post_category', \n", " aggfunc=sum, fill_value=0\n", ")\n", "\n", "# show the table of connected proportions\n", "connected / probed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Connectivity across layer\n", "\n", "Now let's define a larger list of cell classes that combine cortical layer, transgenic types, and morphology:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from aisynphys.cell_class import CellClass\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cell_category_criteria = {\n", " 'l23pyr': {'dendrite_type': 'spiny', 'cortical_layer': '2/3'},\n", " 'l23pv': {'cre_type': 'pvalb', 'cortical_layer': '2/3'},\n", " 'l23sst': {'cre_type': 'sst', 'cortical_layer': '2/3'},\n", " 'l23vip': {'cre_type': 'vip', 'cortical_layer': '2/3'},\n", " 'l4pyr': {'cre_type': ('nr5a1', 'rorb'), 'cortical_layer': '4'},\n", " 'l4pv': {'cre_type': 'pvalb', 'cortical_layer': '4'},\n", " 'l4sst': {'cre_type': 'sst', 'cortical_layer': '4'},\n", " 'l4vip': {'cre_type': 'vip', 'cortical_layer': '4'},\n", " 'l5et': {'cre_type': ('sim1', 'fam84b'), 'cortical_layer': '5'},\n", " 'l5it': {'cre_type': 'tlx3', 'cortical_layer': '5'}, \n", " 'l5pv': {'cre_type': 'pvalb', 'cortical_layer': '5'},\n", " 'l5sst': {'cre_type': 'sst', 'cortical_layer': '5'},\n", " 'l5vip': {'cre_type': 'vip', 'cortical_layer': '5'},\n", " 'l6pyr': {'cre_type': 'ntsr1', 'cortical_layer': ('6a','6b')},\n", " 'l6pv': {'cre_type': 'pvalb', 'cortical_layer': ('6a','6b')},\n", " 'l6sst': {'cre_type': 'sst', 'cortical_layer': ('6a','6b')},\n", " 'l6vip': {'cre_type': 'vip', 'cortical_layer': ('6a','6b')},\n", "}\n", "\n", "cell_categories = {k:CellClass(name=k, **v) for k,v in cell_category_criteria.items()}" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\users\\stephanies\\aisynphys\\aisynphys\\connectivity.py:322: RuntimeWarning: Mean of empty slice.\n", " mean_cp = np.exp(-x_probed**2 / (2 * sigma**2)).mean()\n", "c:\\users\\stephanies\\appdata\\local\\continuum\\miniconda3\\envs\\py3\\lib\\site-packages\\numpy\\core\\_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n", " ret = ret.dtype.type(ret / rcount)\n", "c:\\users\\stephanies\\aisynphys\\aisynphys\\connectivity.py:328: RuntimeWarning: invalid value encountered in long_scalars\n", " est_pmax = (n_conn / n_test) / mean_cp\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from aisynphys.ui.notebook import generate_connectivity_matrix\n", "\n", "fig, ax = plt.subplots(figsize=(14,14))\n", "\n", "generate_connectivity_matrix(\n", " db=db,\n", " cell_classes=cell_categories,\n", " pair_query_args={\n", " 'experiment_type': 'standard multipatch',\n", " 'species': 'mouse',\n", " 'synapse_probed': 'True',\n", " },\n", " ax=ax,\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modeling connectivity versus intersomatic distance\n", "\n", "The analysis above gives us an estimate of the relative connectivities between cell types, but leaves out some important details. In particular, we know that the probability of finding a connection between any two cells is strongly related to the spatial relationship between the cells and their axo-dendritic morphology.\n", "\n", "As an approximation, we think of cell morphology as being cylindrically symmetrical around the axis perpendicular to the cortical surface. This means that the likelihood of two cells being connected by a synapse is strongly related to the _lateral_ distance between their cell bodies (the distance parallel to the cortical surface). \n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from aisynphys.ui.notebook import show_connectivity_profile\n", "from aisynphys.connectivity import GaussianModel\n", "\n", "ei_mask = (\n", " (mouse_pairs['pre_cell.cell_class'] == 'ex') & \n", " (mouse_pairs['post_cell.cell_class'] == 'in') &\n", " (mouse_pairs['pair.lateral_distance'] < 500e-6)\n", ")\n", "\n", "ee_mask = (\n", " (mouse_pairs['pre_cell.cell_class'] == 'ex') & \n", " (mouse_pairs['post_cell.cell_class'] == 'ex') &\n", " (mouse_pairs['pair.lateral_distance'] < 500e-6)\n", ")\n", "\n", "fig,ax = plt.subplots(1, 2, figsize=(15, 5))\n", "\n", "for i, (label, mask) in enumerate({'E->I': ei_mask, 'E->E': ee_mask}.items()):\n", " x_probed = mouse_pairs[mask]['pair.lateral_distance'].to_numpy(dtype=float)\n", " conn = mouse_pairs[mask]['pair.has_synapse'].to_numpy(dtype=bool)\n", "\n", " fit = GaussianModel.fit(x_probed, conn)\n", " show_connectivity_profile(x_probed, conn, ax[i], fit, ymax=0.25)\n", "\n", " ax[i].set_xlim(0, 250e-6)\n", " ax[i].set_title(f\"{label} Gaussian fit pmax={fit.pmax:0.2f}, σ={fit.size*1e6:0.2f}μm\")\n", "\n", "# print(f\"E->I Gaussian fit pmax={fit.pmax:0.2f}, σ={fit.size*1e6:0.2f}μm\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Correcting for bias in connectivity measurements\n", "\n", "We see above that there is a steep relationship between intersomatic distance and the probability of connectivity. A consequence is that, if two groups of connections are sampled with different intersomatic distances, then they may appear to have different rates of connectivity simply as an experimental artifact. This relationship is further influenced by cell type, in this case impacting E->I connections more so than E->E connections\n", "\n", "Likewise, we have two other major sources of experimental artifacts:\n", "- Connections damaged during tissue dissection\n", "- Connections missed due to low signal to noise ratio\n", "\n", "These artifacts are present within our dataset, and they are especially prominent when comparing results across studies that may use very different experimental protocols. It is possible, however, to model these effects and estimate the unbiased connectivity. \n", "\n", "For more on this, please see our [manuscript on bioRxiv](https://www.biorxiv.org/content/10.1101/2021.03.31.437553v2), as well as [this supplementary notebook]()." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "Stochastic release model\n", "---------------------------------------\n", "\n", "Above, we looked briefly at features that describe the dynamic behavior of synapses including stochasticity and short-term plasticity. Those features are useful for comparing synapses, but make an incomplete description of synapse behavior. To capture a more complete description of synaptic dynamics, we use a model of stochastic vesicle release.\n", "\n", "Conceptually, the model is simple: given a list of presynaptic spike times, predict the distribution of likely response amplitudes after each spike. The model has several parameters that combine standard quantal release and short-term plasticity features:\n", "\n", "| Parameter | Description |\n", "|----------:|:------------|\n", "| n_release_sites | Number of synaptic release zones |\n", "| base_release_probability | Resting-state synaptic release probability (0.0-1.0) |\n", "| mini_amplitude | Mean PSP amplitude evoked by a single vesicle release |\n", "| mini_amplitude_cv | Coefficient of variation of PSP amplitude evoked from single vesicle releases |\n", "| depression_amount | Amount of depression (0.0-1.0) to apply per spike. -1 enables vesicle depletion rather than Pr depression. |\n", "| depression_tau | Time constant for recovery from depression or vesicle depletion |\n", "| facilitation_amount | Release probability facilitation per spike (0.0-1.0) |\n", "| facilitation_tau | Time constant for facilitated release probability to recover toward resting state |\n", "| measurement_stdev | Extra variance in PSP amplitudes purely as a result of membrane noise / measurement error |\n", "\n", "Many synapses in the database include maximum-likelihood parameters that can be used to seed this model and make predictions about how the synapse would respond to any input." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "from aisynphys.stochastic_release_model import StochasticReleaseModel, StochasticModelRunner\n", "\n", "synapses = db.pair_query(\n", " experiment_type='standard multipatch', # filter: just multipatch experiments\n", " species='human', # filter: only human data\n", " synapse=True, # filter: only cell pairs that are connected by a synapse\n", " preload=['cell', 'synapse'] # include tables that describe cell and synapse properties\n", ").dataframe()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'n_release_sites': 3,\n", " 'base_release_probability': 0.762698585902344,\n", " 'mini_amplitude': 0.000333520496496931,\n", " 'mini_amplitude_cv': 0.251188643150958,\n", " 'depression_amount': 0.125,\n", " 'depression_tau': 0.107672015410588,\n", " 'facilitation_amount': 0.375,\n", " 'facilitation_tau': 2.56,\n", " 'measurement_stdev': 8.62167096775792e-05}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# select only synapses with max likelihood model parameters\n", "mask = ~synapses['synapse_model.ml_n_release_sites'].isna()\n", "\n", "# pick a random synapse\n", "synapse = synapses[mask].iloc[123]\n", "\n", "# make a dictionary of model parameters\n", "model_params = {param:synapse[f'synapse_model.ml_{param}'] for param in StochasticReleaseModel.param_names}\n", "model_params['n_release_sites'] = int(model_params['n_release_sites'])\n", "model_params" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "\n", "# Instantiate a model with the ML parameters chosen above\n", "model = StochasticReleaseModel(model_params)\n", "\n", "# Make up a list of presynaptic spike times to test\n", "spike_times = np.array([0.1, 0.11, 0.12, 0.14, 0.2, 0.21, 0.22, 0.23, 0.5, 0.51, 0.52])\n", "\n", "# Run the model many times\n", "n_trials = 500\n", "psp_amps = np.empty((n_trials, len(spike_times)))\n", "for i in range(n_trials):\n", " model_result = model.run_model(spike_times, amplitudes='random')\n", " psp_amps[i] = model_result.result['amplitude']\n", " \n", "# Plot the results\n", "fig,ax = plt.subplots(figsize=(10, 4))\n", "for i in range(n_trials):\n", " ax.scatter(\n", " spike_times * 1000 + np.random.normal(size=len(spike_times)), \n", " psp_amps[i] * 1000, \n", " color=(0, 0, 0, 0.1)\n", " )\n", " \n", "ax.plot(spike_times * 1000, psp_amps.mean(axis=0) * 1000)\n", "ax.set_xlabel('spike time (ms)')\n", "ax.set_ylabel('PSP amplitude (mV)');" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }