Connectomics¶

[2]:

!pip install vfb-connect python-catmaid navis --upgrade

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[ ]:

# Import libs and initialise API objects
from vfb_connect.cross_server_tools import VfbConnect
import pandas as pd
vc = VfbConnect()

import pymaid
import navis

navis.set_pbars(jupyter=False)
pymaid.set_pbars(jupyter=False)

# Connect to the VFB CATMAID server hosting the FAFB data
rm = pymaid.connect_catmaid(server="https://fafb.catmaid.virtualflybrain.org/", api_token=None, max_threads=10)

# Test call to see if connection works
print(f'Server is running CATMAID version {rm.catmaid_version}')

WARNING: Could not load OpenGL library.
INFO  : Global CATMAID instance set. Caching is ON. (pymaid)
Server is running CATMAID version 2020.02.15-990-gb5aac7433

VFB provides a simple methods for exploring connectomics data¶

get_connected_neurons_by_type allows queries for connections between defined upstream and downstream neuron types/classes. This supports a level of abstraction not available without VFB, where grouping of neurons by higher level classifications is limited to extraction of classification from standardised names. The following examples take advantage of the abstract clasifications on VFB to explore direct synaptic connections.

[ ]:

# Find all GABA-regic inputs onto descending neurons

vc.get_connected_neurons_by_type(upstream_type='GABAergic neuron',
                                 downstream_type='adult descending neuron',
                                 weight=10).sort_values('weight', ascending=False, ignore_index=True)

	upstream_neuron_id	upstream_neuron_name	weight	downstream_neuron_id	downstream_neuron_name	upstream_class	downstream_class	up_data_source	up_accession	down_source	down_accession
0	VFB_jrchk7ji	VP4_vPN(mlALT)_R - 634759240	65	VFB_jrchjthu	DNp44_R - 542751938	adult antennal lobe projection neuron VP4 vPN	descending neuron of the posterior brain DNp44	neuprint_JRC_Hemibrain_1point1	634759240	neuprint_JRC_Hemibrain_1point1	542751938
1	VFB_jrchk7jh	VP4+_vPN(mlALT)_R - 543010474	50	VFB_jrchjthu	DNp44_R - 542751938	adult antennal lobe projection neuron VP4+ vPN	descending neuron of the posterior brain DNp44	neuprint_JRC_Hemibrain_1point1	543010474	neuprint_JRC_Hemibrain_1point1	542751938
2	VFB_jrchk7jh	VP4+_vPN(mlALT)_R - 543010474	47	VFB_jrchjthj	DNp25_R - 451689001	adult antennal lobe projection neuron VP4+ vPN	descending neuron of the posterior brain DNp25	neuprint_JRC_Hemibrain_1point1	543010474	neuprint_JRC_Hemibrain_1point1	451689001
3	VFB_jrchk8dp	oviIN_R - 423101189	43	VFB_jrchk8dm	oviDNa_R - 550655668	oviposition inhibitory neuron	oviposition descending neuron a	neuprint_JRC_Hemibrain_1point1	423101189	neuprint_JRC_Hemibrain_1point1	550655668
4	VFB_jrchk8dq	oviIN_L - 485934965	40	VFB_jrchk8do	oviDNb_R - 519949044	oviposition inhibitory neuron	oviposition descending neuron b	neuprint_JRC_Hemibrain_1point1	485934965	neuprint_JRC_Hemibrain_1point1	519949044
5	VFB_jrchk8dp	oviIN_R - 423101189	36	VFB_jrchk8do	oviDNb_R - 519949044	oviposition inhibitory neuron	oviposition descending neuron b	neuprint_JRC_Hemibrain_1point1	423101189	neuprint_JRC_Hemibrain_1point1	519949044
6	VFB_jrchk8dp	oviIN_R - 423101189	35	VFB_jrchk8dn	oviDNb(PDM15)_L - 642763374	oviposition inhibitory neuron	oviposition descending neuron b	neuprint_JRC_Hemibrain_1point1	423101189	neuprint_JRC_Hemibrain_1point1	642763374
7	VFB_jrchk8dq	oviIN_L - 485934965	32	VFB_jrchk8dn	oviDNb(PDM15)_L - 642763374	oviposition inhibitory neuron	oviposition descending neuron b	neuprint_JRC_Hemibrain_1point1	485934965	neuprint_JRC_Hemibrain_1point1	642763374
8	VFB_jrchk7jh	VP4+_vPN(mlALT)_R - 543010474	28	VFB_jrchjtg4	DNg30_R - 571346836	adult antennal lobe projection neuron VP4+ vPN	descending neuron of the gnathal ganglion DNg30	neuprint_JRC_Hemibrain_1point1	543010474	neuprint_JRC_Hemibrain_1point1	571346836
9	VFB_jrchk8dq	oviIN_L - 485934965	27	VFB_jrchk8dm	oviDNa_R - 550655668	oviposition inhibitory neuron	oviposition descending neuron a	neuprint_JRC_Hemibrain_1point1	485934965	neuprint_JRC_Hemibrain_1point1	550655668
10	VFB_jrchjza4	LHPV2c1_b_R - 825032139	13	VFB_jrchjthr	DNp32_R - 5813050455	adult lateral horn PV2c1 neuron	descending neuron of the posterior brain DNp32	neuprint_JRC_Hemibrain_1point1	825032139	neuprint_JRC_Hemibrain_1point1	5813050455
11	VFB_jrchjtd8	CT1_L - 1311993208	12	VFB_jrchjtgf	DNp11_R - 1281324958	tangential neuron CT1	descending neuron of the posterior brain DNp11	neuprint_JRC_Hemibrain_1point1	1311993208	neuprint_JRC_Hemibrain_1point1	1281324958
12	VFB_jrchjza6	LHPV2c1_c_R - 913345831	12	VFB_jrchjthr	DNp32_R - 5813050455	adult lateral horn PV2c1 neuron	descending neuron of the posterior brain DNp32	neuprint_JRC_Hemibrain_1point1	913345831	neuprint_JRC_Hemibrain_1point1	5813050455
13	VFB_jrchk7jh	VP4+_vPN(mlALT)_R - 543010474	12	VFB_jrchjthh	DNp24_R - 5813047199	adult antennal lobe projection neuron VP4+ vPN	descending neuron of the posterior brain DNp24	neuprint_JRC_Hemibrain_1point1	543010474	neuprint_JRC_Hemibrain_1point1	5813047199
14	VFB_jrchjza3	LHPV2c1_b_R - 856066807	11	VFB_jrchjthr	DNp32_R - 5813050455	adult lateral horn PV2c1 neuron	descending neuron of the posterior brain DNp32	neuprint_JRC_Hemibrain_1point1	856066807	neuprint_JRC_Hemibrain_1point1	5813050455
15	VFB_jrchk7ji	VP4_vPN(mlALT)_R - 634759240	10	VFB_jrchjtg4	DNg30_R - 571346836	adult antennal lobe projection neuron VP4 vPN	descending neuron of the gnathal ganglion DNg30	neuprint_JRC_Hemibrain_1point1	634759240	neuprint_JRC_Hemibrain_1point1	571346836
16	VFB_jrchjymc	LHAV1a3_R - 823999645	10	VFB_jrchjtht	DNp42_R - 981000564	adult lateral horn AV1a3 neuron	descending neuron of the posterior brain	neuprint_JRC_Hemibrain_1point1	823999645	neuprint_JRC_Hemibrain_1point1	981000564

[ ]:

# Get connections between pacemaker neurons
vc.get_connected_neurons_by_type(upstream_type='pacemaker neuron',
                                 downstream_type='pacemaker neuron',
                                 weight=10).sort_values('weight', ascending=False)

	upstream_neuron_id	upstream_neuron_name	weight	downstream_neuron_id	downstream_neuron_name	upstream_class	downstream_class	up_data_source	up_accession	down_source	down_accession
28	VFB_jrchjtex	DN1a_R - 264083994	79	VFB_jrchjzxy	LNd_R - 5813069648	DN1a neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	264083994	neuprint_JRC_Hemibrain_1point1	5813069648
26	VFB_jrchjtey	DN1a_R - 5813022274	75	VFB_jrchjzxy	LNd_R - 5813069648	DN1a neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	5813022274	neuprint_JRC_Hemibrain_1point1	5813069648
23	VFB_jrchjtey	DN1a_R - 5813022274	63	VFB_jrchk8e0	5th s-LNv - 511051477	DN1a neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	5813022274	neuprint_JRC_Hemibrain_1point1	511051477
22	VFB_jrchjtex	DN1a_R - 264083994	55	VFB_jrchk8e0	5th s-LNv - 511051477	DN1a neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	264083994	neuprint_JRC_Hemibrain_1point1	511051477
3	VFB_jrchk089	LPN_R - 480029788	43	VFB_jrchk08a	LPN_R - 450034902	LP neuron	LP neuron	neuprint_JRC_Hemibrain_1point1	480029788	neuprint_JRC_Hemibrain_1point1	450034902
7	VFB_jrchk8e0	5th s-LNv - 511051477	40	VFB_jrchjzxy	LNd_R - 5813069648	s-LNv neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	511051477	neuprint_JRC_Hemibrain_1point1	5813069648
1	VFB_jrchjzxy	LNd_R - 5813069648	37	VFB_jrchk8e0	5th s-LNv - 511051477	LNd neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	511051477
5	VFB_jrchjtf0	DN1pA_R - 324846570	37	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	324846570	neuprint_JRC_Hemibrain_1point1	5813069648
16	VFB_jrchjtf2	DN1pA_R - 387944118	34	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	387944118	neuprint_JRC_Hemibrain_1point1	5813069648
20	VFB_jrchjtf1	DN1pA_R - 325529237	33	VFB_jrchk8e0	5th s-LNv - 511051477	DN1p neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	325529237	neuprint_JRC_Hemibrain_1point1	511051477
29	VFB_jrchjtf3	DN1pA_R - 387166379	30	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	387166379	neuprint_JRC_Hemibrain_1point1	5813069648
11	VFB_jrchjtf1	DN1pA_R - 325529237	30	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	325529237	neuprint_JRC_Hemibrain_1point1	5813069648
21	VFB_jrchk08a	LPN_R - 450034902	30	VFB_jrchk089	LPN_R - 480029788	LP neuron	LP neuron	neuprint_JRC_Hemibrain_1point1	450034902	neuprint_JRC_Hemibrain_1point1	480029788
14	VFB_jrchjzxy	LNd_R - 5813069648	29	VFB_jrchjzxw	LNd_R - 5813056917	LNd neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	5813056917
10	VFB_jrchk8e0	5th s-LNv - 511051477	25	VFB_jrchjzxx	LNd_R - 5813021192	s-LNv neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	511051477	neuprint_JRC_Hemibrain_1point1	5813021192
8	VFB_jrchjtf3	DN1pA_R - 387166379	25	VFB_jrchk8e0	5th s-LNv - 511051477	DN1p neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	387166379	neuprint_JRC_Hemibrain_1point1	511051477
4	VFB_jrchjtf0	DN1pA_R - 324846570	25	VFB_jrchk8e0	5th s-LNv - 511051477	DN1p neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	324846570	neuprint_JRC_Hemibrain_1point1	511051477
12	VFB_jrchjtez	DN1pA_R - 5813010153	25	VFB_jrchk8e0	5th s-LNv - 511051477	DN1p neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	5813010153	neuprint_JRC_Hemibrain_1point1	511051477
15	VFB_jrchjtf2	DN1pA_R - 387944118	22	VFB_jrchk8e0	5th s-LNv - 511051477	DN1p neuron	s-LNv neuron	neuprint_JRC_Hemibrain_1point1	387944118	neuprint_JRC_Hemibrain_1point1	511051477
30	VFB_jrchjzxy	LNd_R - 5813069648	21	VFB_jrchjzxx	LNd_R - 5813021192	LNd neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	5813021192
2	VFB_jrchk8e0	5th s-LNv - 511051477	17	VFB_jrchjzxw	LNd_R - 5813056917	s-LNv neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	511051477	neuprint_JRC_Hemibrain_1point1	5813056917
13	VFB_jrchjtez	DN1pA_R - 5813010153	15	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	5813010153	neuprint_JRC_Hemibrain_1point1	5813069648
24	VFB_jrchk8e0	5th s-LNv - 511051477	14	VFB_jrchjtf3	DN1pA_R - 387166379	s-LNv neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	511051477	neuprint_JRC_Hemibrain_1point1	387166379
25	VFB_jrchjzxy	LNd_R - 5813069648	14	VFB_jrchjtf1	DN1pA_R - 325529237	LNd neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	325529237
0	VFB_jrchjtf0	DN1pA_R - 324846570	13	VFB_jrchjzxx	LNd_R - 5813021192	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	324846570	neuprint_JRC_Hemibrain_1point1	5813021192
17	VFB_jrchjzxy	LNd_R - 5813069648	13	VFB_jrchjtf3	DN1pA_R - 387166379	LNd neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	387166379
9	VFB_jrchk8e0	5th s-LNv - 511051477	12	VFB_jrchjtf1	DN1pA_R - 325529237	s-LNv neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	511051477	neuprint_JRC_Hemibrain_1point1	325529237
19	VFB_jrchjtf5	DN1pB_R - 5813071319	11	VFB_jrchjzxy	LNd_R - 5813069648	DN1p neuron	LNd neuron	neuprint_JRC_Hemibrain_1point1	5813071319	neuprint_JRC_Hemibrain_1point1	5813069648
31	VFB_jrchjtey	DN1a_R - 5813022274	11	VFB_jrchjtex	DN1a_R - 264083994	DN1a neuron	DN1a neuron	neuprint_JRC_Hemibrain_1point1	5813022274	neuprint_JRC_Hemibrain_1point1	264083994
18	VFB_jrchjzxy	LNd_R - 5813069648	10	VFB_jrchjtez	DN1pA_R - 5813010153	LNd neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	5813010153
27	VFB_jrchjtex	DN1a_R - 264083994	10	VFB_jrchjtey	DN1a_R - 5813022274	DN1a neuron	DN1a neuron	neuprint_JRC_Hemibrain_1point1	264083994	neuprint_JRC_Hemibrain_1point1	5813022274
6	VFB_jrchjzxy	LNd_R - 5813069648	10	VFB_jrchjtf2	DN1pA_R - 387944118	LNd neuron	DN1p neuron	neuprint_JRC_Hemibrain_1point1	5813069648	neuprint_JRC_Hemibrain_1point1	387944118

[ ]:

vc.get_connected_neurons_by_type(upstream_type='pacemaker neuron',
                                 downstream_type='pacemaker neuron',
                                 weight=10).sort_values('weight', ascending=False)

[ ]:

vc.get_connected_neurons_by_type(upstream_type='adult neuron',
                                 downstream_type='neurosecretory cell of pars intercerebralis',
                                 weight=10)

	upstream_neuron_id	upstream_neuron_name	weight	downstream_neuron_id	downstream_neuron_name	upstream_class	downstream_class	up_data_source	up_accession	down_source	down_accession
0	VFB_jrchk6pq	SMP302_R (FlyEM-HB:481410841)	18	VFB_jrchk3rl	PI1_c (FlyEM-HB:5813026416)	adult superior medial protocerebrum neuron 302	adult neurosecretory cell of pars intercerebralis	None	None	None	None
1	VFB_jrchk70e	SMP454(PDM12)_L (FlyEM-HB:328010530)	31	VFB_jrchk3rw	PI3 (FlyEM-HB:859339195)	adult superior medial protocerebrum neuron 454	adult neurosecretory cell of pars intercerebralis	None	None	None	None
2	VFB_jrchk541	SIFa(PDM34) (FlyEM-HB:699031185)	11	VFB_jrchk543	SIFa(PDM34) (FlyEM-HB:5813062866)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
3	VFB_jrchk59h	SIP076_R (FlyEM-HB:299954300)	13	VFB_jrchk3rc	PI1_b (FlyEM-HB:514530654)	adult superior intermediate protocerebrum neur...	adult neurosecretory cell of pars intercerebralis	None	None	None	None
4	VFB_jrchk72y	SMP484_R (FlyEM-HB:298254380)	12	VFB_jrchk3rd	PI1_b (FlyEM-HB:5813057164)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
5	VFB_jrchk6k0	SMP218_R (FlyEM-HB:357940977)	11	VFB_jrchk544	SIFa(PDM34) (FlyEM-HB:1137590785)	adult superior medial protocerebrum neuron 218	adult SIFamide cell of pars intercerebralis	None	None	None	None
6	VFB_jrchjti7	DSKMP3_R (FlyEM-HB:328559607)	10	VFB_jrchk3rd	PI1_b (FlyEM-HB:5813057164)	adult Drosulfakinin neuron	adult neurosecretory cell of pars intercerebralis	None	None	None	None
7	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	29	VFB_jrchk3re	PI1_b (FlyEM-HB:5813047717)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
8	VFB_jrchk736	SMP486_a_R (FlyEM-HB:297251765)	18	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 486	adult neurosecretory cell of pars intercerebralis	None	None	None	None
9	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	18	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
10	VFB_jrchk736	SMP486_a_R (FlyEM-HB:297251765)	10	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 486	adult neurosecretory cell of pars intercerebralis	None	None	None	None
11	VFB_jrchk6ph	SMP298_R (FlyEM-HB:727285326)	10	VFB_jrchk3re	PI1_b (FlyEM-HB:5813047717)	adult superior medial protocerebrum neuron 298	adult neurosecretory cell of pars intercerebralis	None	None	None	None
12	VFB_jrchjti0	DPM_R (FlyEM-HB:5813105172)	15	VFB_jrchk542	SIFa(PDM34) (FlyEM-HB:1418618235)	mushroom body dorsal paired medial cell	adult SIFamide cell of pars intercerebralis	None	None	None	None
13	VFB_jrchk6pg	SMP298_R (FlyEM-HB:5813011015)	14	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 298	adult neurosecretory cell of pars intercerebralis	None	None	None	None
14	VFB_jrchk6jx	SMP217_R (FlyEM-HB:5813063151)	10	VFB_jrchk544	SIFa(PDM34) (FlyEM-HB:1137590785)	adult superior medial protocerebrum neuron 217	adult SIFamide cell of pars intercerebralis	None	None	None	None
15	VFB_jrchk736	SMP486_a_R (FlyEM-HB:297251765)	10	VFB_jrchk3rd	PI1_b (FlyEM-HB:5813057164)	adult superior medial protocerebrum neuron 486	adult neurosecretory cell of pars intercerebralis	None	None	None	None
16	VFB_jrchk70f	SMP454_R (FlyEM-HB:326253554)	11	VFB_jrchk3rw	PI3 (FlyEM-HB:859339195)	adult superior medial protocerebrum neuron 454	adult neurosecretory cell of pars intercerebralis	None	None	None	None
17	VFB_jrchk734	SMP486_a_R (FlyEM-HB:297580512)	11	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 486	adult neurosecretory cell of pars intercerebralis	None	None	None	None
18	VFB_jrchk70f	SMP454_R (FlyEM-HB:326253554)	10	VFB_jrchk3rl	PI1_c (FlyEM-HB:5813026416)	adult superior medial protocerebrum neuron 454	adult neurosecretory cell of pars intercerebralis	None	None	None	None
19	VFB_jrchk543	SIFa(PDM34) (FlyEM-HB:5813062866)	13	VFB_jrchk544	SIFa(PDM34) (FlyEM-HB:1137590785)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
20	VFB_jrchk64p	SMP001_R (FlyEM-HB:5813056887)	20	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 001	adult neurosecretory cell of pars intercerebralis	None	None	None	None
21	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	16	VFB_jrchk3rd	PI1_b (FlyEM-HB:5813057164)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
22	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	19	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
23	VFB_jrchk72y	SMP484_R (FlyEM-HB:298254380)	12	VFB_jrchk3re	PI1_b (FlyEM-HB:5813047717)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
24	VFB_jrchk72y	SMP484_R (FlyEM-HB:298254380)	17	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
25	VFB_jrchk6h6	SMP169_R (FlyEM-HB:327246931)	11	VFB_jrchk3rc	PI1_b (FlyEM-HB:514530654)	adult superior medial protocerebrum neuron 169	adult neurosecretory cell of pars intercerebralis	None	None	None	None
26	VFB_jrchk542	SIFa(PDM34) (FlyEM-HB:1418618235)	14	VFB_jrchk543	SIFa(PDM34) (FlyEM-HB:5813062866)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
27	VFB_jrchjsj3	AstA1_L (FlyEM-HB:362473525)	16	VFB_jrchk3rc	PI1_b (FlyEM-HB:514530654)	adult neuron\|allatostatin A neuron	adult neurosecretory cell of pars intercerebralis	None	None	None	None
28	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	18	VFB_jrchk3ri	PI1_b (FlyEM-HB:359027729)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
29	VFB_jrchk72y	SMP484_R (FlyEM-HB:298254380)	13	VFB_jrchk3ri	PI1_b (FlyEM-HB:359027729)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
30	VFB_jrchk6my	SMP261_R (FlyEM-HB:390024952)	11	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 261	adult neurosecretory cell of pars intercerebralis	None	None	None	None
31	VFB_jrchk6ph	SMP298_R (FlyEM-HB:727285326)	15	VFB_jrchk3rj	PI1_b (FlyEM-HB:392445880)	adult superior medial protocerebrum neuron 298	adult neurosecretory cell of pars intercerebralis	None	None	None	None
32	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	11	VFB_jrchk3rl	PI1_c (FlyEM-HB:5813026416)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
33	VFB_jrchk6mt	SMP259_R (FlyEM-HB:328265182)	11	VFB_jrchk3rj	PI1_b (FlyEM-HB:392445880)	adult superior medial protocerebrum neuron 259	adult neurosecretory cell of pars intercerebralis	None	None	None	None
34	VFB_jrchk64p	SMP001_R (FlyEM-HB:5813056887)	11	VFB_jrchk3rf	PI1_b (FlyEM-HB:5813019567)	adult superior medial protocerebrum neuron 001	adult neurosecretory cell of pars intercerebralis	None	None	None	None
35	VFB_jrchk6mw	SMP261_R (FlyEM-HB:358290727)	13	VFB_jrchk3ri	PI1_b (FlyEM-HB:359027729)	adult superior medial protocerebrum neuron 261	adult neurosecretory cell of pars intercerebralis	None	None	None	None
36	VFB_jrchjti7	DSKMP3_R (FlyEM-HB:328559607)	11	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult Drosulfakinin neuron	adult neurosecretory cell of pars intercerebralis	None	None	None	None
37	VFB_jrchjti0	DPM_R (FlyEM-HB:5813105172)	10	VFB_jrchk541	SIFa(PDM34) (FlyEM-HB:699031185)	mushroom body dorsal paired medial cell	adult SIFamide cell of pars intercerebralis	None	None	None	None
38	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	14	VFB_jrchk3rj	PI1_b (FlyEM-HB:392445880)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
39	VFB_jrchk6pq	SMP302_R (FlyEM-HB:481410841)	11	VFB_jrchk3re	PI1_b (FlyEM-HB:5813047717)	adult superior medial protocerebrum neuron 302	adult neurosecretory cell of pars intercerebralis	None	None	None	None
40	VFB_jrchk72y	SMP484_R (FlyEM-HB:298254380)	10	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
41	VFB_jrchk731	SMP484_R (FlyEM-HB:329918685)	13	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None
42	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	12	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
43	VFB_jrchk6ph	SMP298_R (FlyEM-HB:727285326)	10	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 298	adult neurosecretory cell of pars intercerebralis	None	None	None	None
44	VFB_jrchk542	SIFa(PDM34) (FlyEM-HB:1418618235)	12	VFB_jrchk541	SIFa(PDM34) (FlyEM-HB:699031185)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
45	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	15	VFB_jrchk3rj	PI1_b (FlyEM-HB:392445880)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
46	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	47	VFB_jrchk3rw	PI3 (FlyEM-HB:859339195)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
47	VFB_jrchk541	SIFa(PDM34) (FlyEM-HB:699031185)	16	VFB_jrchk542	SIFa(PDM34) (FlyEM-HB:1418618235)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
48	VFB_jrchk79v	SMP598(SCB063)_R (FlyEM-HB:547978595)	10	VFB_jrchk3rf	PI1_b (FlyEM-HB:5813019567)	adult superior medial protocerebrum neuron 598	adult neurosecretory cell of pars intercerebralis	None	None	None	None
49	VFB_jrchk6mt	SMP259_R (FlyEM-HB:328265182)	10	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 259	adult neurosecretory cell of pars intercerebralis	None	None	None	None
50	VFB_jrchk6p7	SMP296_R (FlyEM-HB:727280915)	20	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 296	adult neurosecretory cell of pars intercerebralis	None	None	None	None
51	VFB_jrchk544	SIFa(PDM34) (FlyEM-HB:1137590785)	11	VFB_jrchk543	SIFa(PDM34) (FlyEM-HB:5813062866)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
52	VFB_jrchk6ph	SMP298_R (FlyEM-HB:727285326)	12	VFB_jrchk3ri	PI1_b (FlyEM-HB:359027729)	adult superior medial protocerebrum neuron 298	adult neurosecretory cell of pars intercerebralis	None	None	None	None
53	VFB_jrchjtei	DL5_adPN_R (FlyEM-HB:693483018)	11	VFB_jrchk541	SIFa(PDM34) (FlyEM-HB:699031185)	adult antennal lobe projection neuron DL5 adPN	adult SIFamide cell of pars intercerebralis	None	None	None	None
54	VFB_jrchk734	SMP486_a_R (FlyEM-HB:297580512)	12	VFB_jrchk3rg	PI1_b (FlyEM-HB:5813025545)	adult superior medial protocerebrum neuron 486	adult neurosecretory cell of pars intercerebralis	None	None	None	None
55	VFB_jrchk543	SIFa(PDM34) (FlyEM-HB:5813062866)	12	VFB_jrchk542	SIFa(PDM34) (FlyEM-HB:1418618235)	adult SIFamide cell of pars intercerebralis	adult SIFamide cell of pars intercerebralis	None	None	None	None
56	VFB_jrchk6p7	SMP296_R (FlyEM-HB:727280915)	16	VFB_jrchk3rh	PI1_b (FlyEM-HB:5813014245)	adult superior medial protocerebrum neuron 296	adult neurosecretory cell of pars intercerebralis	None	None	None	None
57	VFB_jrchk72x	SMP484_R (FlyEM-HB:299293552)	10	VFB_jrchk3r4	PI1_a (FlyEM-HB:330388864)	adult superior medial protocerebrum neuron 484	adult neurosecretory cell of pars intercerebralis	None	None	None	None

[ ]:

# Get connections between visual projection neurons and descending neurons
ds_vpn = vc.get_connected_neurons_by_type(upstream_type='visual projection neuron',
                                 downstream_type='adult neuron',
                                 weight=10).sort_values('weight', ascending=False)

Index(['upstream_neuron_id', 'upstream_neuron_name', 'weight',
       'downstream_neuron_id', 'downstream_neuron_name', 'upstream_class',
       'downstream_class', 'up_data_source', 'up_accession', 'down_source',
       'down_accession'],
      dtype='object')

[ ]:

ds_vpn[['upstream_class', 'upstream_neuron_name', 'weight', 'downstream_neuron_name', 'downstream_class']]

	upstream_class	upstream_neuron_name	weight	downstream_neuron_name	downstream_class
5217	lobula tangential neuron Lt1	LT1_R (FlyEM-HB:1189230639)	958	PVLP106_R (FlyEM-HB:447228814)	adult posterior ventrolateral protocerebrum ne...
13116	lobula tangential neuron	LT39_R (FlyEM-HB:5813061218)	793	mALC2_L (FlyEM-HB:2124762952)	adult neuron
17011	lobula tangential neuron Lt1	LT1_R (FlyEM-HB:1189912812)	600	AVLP535_R (FlyEM-HB:5813033371)	adult anterior ventrolateral protocerebrum neu...
3895	lobula tangential neuron	LT51(aSP29a)_R (FlyEM-HB:1282348408)	319	DNb01_R (FlyEM-HB:1566597156)	descending neuron of the anterior ventral brai...
17108	lobula tangential neuron	LT86_R (FlyEM-HB:1285849916)	295	mALD2_L (FlyEM-HB:701923413)	adult neuron
...	...	...	...	...	...
15568	lobula columnar neuron LC12	LC12 (FlyEM-HB:1754028266)	10	PVLP078_R (FlyEM-HB:1691630995)	adult posterior ventrolateral protocerebrum ne...
12381	lobula columnar neuron LC22	LC22_R (FlyEM-HB:1750712639)	10	PLP009_R (FlyEM-HB:1750419433)	adult posterior lateral protocerebrum neuron 009
17519	medulla columnar neuron	MC66 (FlyEM-HB:5812993478)	10	PS221_R (FlyEM-HB:1436667764)	adult posterior slope neuron 221
9483	lobula columnar neuron LC12	LC12 (FlyEM-HB:1785417481)	10	PVLP077_R (FlyEM-HB:1692299350)	adult posterior ventrolateral protocerebrum ne...
16929	lobula columnar neuron LC17	LC17 (FlyEM-HB:1751262627)	10	PVLP135_R (FlyEM-HB:1535082316)	adult posterior ventrolateral protocerebrum ne...

19472 rows × 5 columns

[ ]:

# Get connections between visual projection neurons and descending neurons
vpn2ds = vc.get_connected_neurons_by_type(upstream_type='visual projection neuron',
                                 downstream_type='adult descending neuron',
                                 weight=10).sort_values('weight', ascending=False)

[ ]:

print('descending neuron of the posterior brain DNp44' in vpn2ds['downstream_class'])

False

[ ]:

vpn2ds[['upstream_class', 'upstream_neuron_name', 'weight', 'downstream_neuron_name', 'downstream_class']]

	upstream_class	upstream_neuron_name	weight	downstream_neuron_name	downstream_class
233	lobula tangential neuron	LT51(aSP29a)_R (FlyEM-HB:1282348408)	319	DNb01_R (FlyEM-HB:1566597156)	descending neuron of the anterior ventral brai...
815	lobula tangential neuron	LT51_R (FlyEM-HB:1444351896)	221	DNa03_R (FlyEM-HB:1139909038)	descending neuron of the anterior dorsal brain...
610	lobula tangential neuron	LT51(aSP29a)_R (FlyEM-HB:1282348408)	169	DNp18_R (FlyEM-HB:5813068840)	descending neuron of the posterior brain DNp18
49	lobula tangential neuron	LT51_R (FlyEM-HB:1597493900)	141	DNa03_R (FlyEM-HB:1139909038)	descending neuron of the anterior dorsal brain...
154	lobula tangential neuron	LT51(aSP29a)_R (FlyEM-HB:1261229600)	104	DNb01_R (FlyEM-HB:1566597156)	descending neuron of the anterior ventral brai...
...	...	...	...	...	...
920	lobula complex columnar neuron	LLPC2b (FlyEM-HB:5813024821)	10	DNb05_R (FlyEM-HB:1406966879)	descending neuron of the anterior ventral brai...
556	lobula complex columnar neuron LPLC2	LPLC2_R (FlyEM-HB:1782369340)	10	DNp04_R (FlyEM-HB:1405231475)	descending neuron of the posterior brain DNp04
562	lobula complex columnar neuron LPLC2	LPLC2_R (FlyEM-HB:1815826155)	10	DNp04_R (FlyEM-HB:1405231475)	descending neuron of the posterior brain DNp04
580	medulla columnar neuron	MC66 (FlyEM-HB:1498414361)	10	DNp16_R (FlyEM-HB:1436703256)	descending neuron of the posterior brain DNp16
188	lobula complex columnar neuron LPLC1	LPLC1 (FlyEM-HB:1782714902)	10	DNp11_R (FlyEM-HB:1281324958)	descending neuron of the posterior brain DNp11

1085 rows × 5 columns

Get neurons downstream/downstream of a specific neuron¶

Starting from an individual neuron, in this case an individual neuron from hemibrain, find all neurons upstream or downstream, restricting to edges >= some specified wieght (synaptic connections) and optionally limiting the output by neuron type/class.

[ ]:

# Starting point - let's take one of the individual descending neurons from
# the previous exploratory queries and look for other inputs

vc.get_neurons_upstream_of('DNp04_R - 1405231475', weight = 20)

	query_neuron_id	query_neuron_name	weight	target_neuron_id	target_neuron_name
0	VFB_jrchk4u3	PVLP100_R - 1375845363	44	VFB_jrchjtg7	DNp04_R - 1405231475
1	VFB_jrchjy8u	LC4 - 1158864995	50	VFB_jrchjtg7	DNp04_R - 1405231475
2	VFB_jrchjy9f	LC4 - 5813055129	46	VFB_jrchjtg7	DNp04_R - 1405231475
3	VFB_jrchjyal	LC4 - 1907571222	78	VFB_jrchjtg7	DNp04_R - 1405231475
4	VFB_jrchjy9t	LC4 - 2215161310	86	VFB_jrchjtg7	DNp04_R - 1405231475
...	...	...	...	...	...
82	VFB_jrchjyak	LC4 - 1907933561	82	VFB_jrchjtg7	DNp04_R - 1405231475
83	VFB_jrchjy9d	LC4 - 1627117134	46	VFB_jrchjtg7	DNp04_R - 1405231475
84	VFB_jrchjya7	LC4 - 1876471221	58	VFB_jrchjtg7	DNp04_R - 1405231475
85	VFB_jrchk4tm	PVLP094_R - 1503733177	26	VFB_jrchjtg7	DNp04_R - 1405231475
86	VFB_jrchjya0	LC4 - 5812998136	92	VFB_jrchjtg7	DNp04_R - 1405231475

87 rows × 5 columns

Let’s try a similar query from one of the LC4 neurons to see what other descending neuron types are downstream of it.

[ ]:

vc.get_neurons_downstream_of('LC4 - 1249932198', classification="'adult descending neuron'", weight = 20)

Running query: FBbt:00047511
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00047511&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 139

	query_neuron_id	query_neuron_name	weight	target_neuron_id	target_neuron_name
0	VFB_jrchjy8y	LC4 - 1249932198	78	VFB_jrchjtg7	DNp04_R - 1405231475
1	VFB_jrchjy8y	LC4 - 1249932198	30	VFB_jrchjup1	Giant Fiber_R - 2307027729
2	VFB_jrchjy8y	LC4 - 1249932198	65	VFB_jrchjtgf	DNp11_R - 1281324958

More sophisticated connectomics queries require direct queries of connectomics DB APIs¶

CATMAID connectivity queries¶

Broadly speaking, using pymaid to query CATMAID servers lets you fetch connectivity data as either lists of up- and downstream partners or as whole adjacency matrices.

These examples use the VFB FAFB server.

[ ]:

# Using VFB to get neurons by type
DA1 = vc.get_instances("'adult antennal lobe projection neuron DA1'", summary=True)
DA1_tab = pd.DataFrame.from_records(DA1)
len(DA1_tab)
DA1_tab

Running query: FBbt:00048096
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00048096&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 31

	label	id	tags	parents_label	parents_id	data_source	accession	templates	dataset	license
0	Uniglomerular mALT DA1 lPN#L3 (FAFB:2345089)	VFB_0010122p	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2345089	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
1	ALv1_P02(DA1)_0_2018U	VFB_00102763	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372				Yu2013	https://creativecommons.org/licenses/by-nc-sa/...
2	Uniglomerular mALT DA1 lPN#R3 (FAFB:61221)	VFB_00101204	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	61221	adult brain template JFRC2\|JRC2018Unisex	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
3	Uniglomerular mALT DA1 lPN#L1 (FAFB:4207871)	VFB_0010126e	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	4207871	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
4	ALv1_P02(DA1)_0_2018U	VFB_00103943	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372				Yu2013	https://creativecommons.org/licenses/by-nc-sa/...
5	Uniglomerular mALT DA1 lPN#R1 (FAFB:57323)	VFB_00101201	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	57323	JRC2018Unisex\|adult brain template JFRC2	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
6	DA1_lPN_R - 1734350908	VFB_jrchjtdb	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	1734350908	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
7	Uniglomerular mALT DA1 lPN#R5 (FAFB:2863104)	VFB_0010124e	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2863104	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
8	Uniglomerular mALT DA1 lPN#R7 (FAFB:57353)	VFB_00101202	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	57353	JRC2018Unisex\|adult brain template JFRC2	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
9	ALv1_P02(DA1)_0_2018U	VFB_00102297	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372			JRC2018Unisex	Lee2020	https://creativecommons.org/licenses/by/4.0/le...
10	ALv1_P02(DA1)_0_2018U	VFB_00104629	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372			JRC2018Unisex	Yu2013	https://creativecommons.org/licenses/by-nc-sa/...
11	ALv1_P02(DA1)_0_2018U	VFB_00102294	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372				Yu2013	https://creativecommons.org/licenses/by-nc-sa/...
12	Uniglomerular mlALT DA1 vPN#R1 (FAFB:1811442)	VFB_0010121x	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372	catmaid_fafb	1811442	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
13	Uniglomerular mlALT DA1 vPN#L1 (FAFB:2334841)	VFB_0010122m	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372	catmaid_fafb	2334841	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
14	Uniglomerular mALT DA1 lPN#L5 (FAFB:2380564)	VFB_0010122z	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2380564	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
15	DA1_lPN_R - 754538881	VFB_jrchjtdg	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	754538881	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
16	Uniglomerular mALT DA1 lPN#R6 (FAFB:27295)	VFB_00101199	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	27295	JRC2018Unisex\|adult brain template JFRC2	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
17	Uniglomerular mALT DA1 lPN#R8 (FAFB:57381)	VFB_00101203	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	57381	adult brain template JFRC2\|JRC2018Unisex	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
18	DA1_lPN_R - 1765040289	VFB_jrchjtdc	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	1765040289	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
19	Uniglomerular mALT DA1 lPN#R2 (FAFB:57311)	VFB_00101200	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	57311	adult brain template JFRC2\|JRC2018Unisex	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
20	DA1_lPN_R - 5813039315	VFB_jrchjtdd	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	5813039315	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
21	Uniglomerular mALT DA1 lPN#L6 (FAFB:2381753)	VFB_0010123b	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2381753	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
22	DA1_lPN_R - 754534424	VFB_jrchjtde	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	754534424	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
23	DA1_lPN_R - 1734350788	VFB_jrchjtdf	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	1734350788	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
24	ALv1_P02(DA1)_0_2018U	VFB_00104624	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372				Yu2013	https://creativecommons.org/licenses/by-nc-sa/...
25	Uniglomerular mALT DA1 lPN#L2 (FAFB:2319457)	VFB_0010122k	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2319457	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
26	Uniglomerular mALT DA1 lPN#R4 (FAFB:755022)	VFB_00101205	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	755022	adult brain template JFRC2\|JRC2018Unisex	Zheng2018	https://creativecommons.org/licenses/by-sa/4.0...
27	Uniglomerular mALT DA1 lPN#L7 (FAFB:3239781)	VFB_0010124l	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	3239781	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
28	Uniglomerular mALT DA1 lPN#L4 (FAFB:2379517)	VFB_0010122y	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	catmaid_fafb	2379517	JRC2018Unisex	BatesSchlegel2020	https://creativecommons.org/licenses/by-sa/4.0...
29	DA1_vPN_R - 733316908	VFB_jrchjtdh	Entity\|GABAergic\|Adult\|Anatomy\|has_image\|Cell\|...	adult antennal lobe projection neuron DA1 vPN	FBbt_00067372	neuprint_JRC_Hemibrain_1point1	733316908	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
30	DA1_lPN_R - 722817260	VFB_jrchjtda	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DA1 lPN	FBbt_00067363	neuprint_JRC_Hemibrain_1point1	722817260	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...

[ ]:

# Map neurons to CATMAID Skeleton IDs (skids)

da1_skids = vc.neo_query_wrapper.vfb_id_2_xrefs(DA1_tab['id'], db='catmaid_fafb', reverse_return=True)
da1_skids

{'2863104': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010124e'}],
 '57353': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101202'}],
 '2381753': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010123b'}],
 '1811442': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010121x'}],
 '57323': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101201'}],
 '57311': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101200'}],
 '2345089': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122p'}],
 '27295': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101199'}],
 '2379517': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122y'}],
 '2380564': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122z'}],
 '755022': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101205'}],
 '61221': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101204'}],
 '57381': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_00101203'}],
 '3239781': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010124l'}],
 '4207871': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010126e'}],
 '2334841': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122m'}],
 '2319457': [{'db': 'catmaid_fafb', 'vfb_id': 'VFB_0010122k'}]}

[ ]:

# Map neurons to CATMAID Skeleton IDs (skids)

da1_skids = vc.neo_query_wrapper.vfb_id_2_xrefs(DA1_tab['id'], db='catmaid_fafb', reverse_return=True)
da1_skids_int = list(map(int, da1_skids))
da1_skids_int

Generate a connectivity table for neurons downstream of DA1 neurons

[ ]:

da1 = pymaid.get_neurons(da1_skids_int)
da1_ds = pymaid.get_partners(da1_skids_int,
                             threshold=3,  # anything with >= 3 synapses
                             directions=['outgoing']  # downstream partners only
                            )

# Result is a pandas DataFrame
da1_ds.head()

INFO  : Fetching connectivity table for 17 neurons (pymaid)
INFO  : Done. Found 0 pre-, 270 postsynaptic and 0 gap junction-connected neurons (pymaid)

	neuron_name	skeleton_id	num_nodes	relation	2863104	57353	2381753	1811442	57323	57311	...	2379517	755022	61221	57381	3239781	4207871	2319457	total
0	Uniglomerular mlALT DA1 vPN mlALTed Milk 18114...	1811442	11769	downstream	30	3	0	0	20	21	...	0	32	26	4	0	0	0	151.0
1	Uniglomerular mlALT DA1 vPN mlALTed Milk 23348...	2334841	6362	downstream	0	0	32	0	0	0	...	28	0	0	0	26	17	22	139.0
2	LHAV4a4#1 1911125 FML PS RJVR	1911124	6969	downstream	23	6	0	0	15	19	...	0	19	13	9	0	0	0	109.0
3	LHAV2a3#1 1870231 RJVR AJES PS	1870230	14820	downstream	5	23	0	1	7	5	...	0	19	7	28	0	0	0	105.0
4	LHAV4c1#1 488056 downstream DA1 GSXEJ	488055	12137	downstream	15	3	0	0	11	17	...	0	15	15	0	0	0	0	92.0

5 rows × 22 columns

[ ]:

# Get an adjacency matrix between all Bates, Schlegel et al. neurons
bates = pymaid.find_neurons(annotations='Paper: Bates and Schlegel et al 2020')
adj = pymaid.adjacency_matrix(bates)
adj.head()

INFO  : Found 583 neurons matching the search parameters (pymaid)

targets	2863104	57349	57353	16	57361	15738898	57365	4182038	3813399	11524119	...	57323	4624362	1853423	2842610	57333	4624374	3080183	57337	4624378	57341
sources
2863104	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	2.0	0.0	12.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
57349	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
57353	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
16	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
57361	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 583 columns

[ ]:

import seaborn as sns

ax = sns.clustermap(adj, vmax=10, cmap='Greys')

/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/matrix.py:649: UserWarning:

Clustering large matrix with scipy. Installing `fastcluster` may give better performance.

../_images/tutorials_connectomics_25_1.png

[ ]:

# Axo-axonic connections between two different types of DA1 PNs
cn = pymaid.get_connectors_between(2863104, 1811442)
cn.head()

	connector_id	connector_loc	node1_id	source_neuron	confidence1	creator1	node1_loc	node2_id	target_neuron	confidence2	creator2	node2_loc
0	6736296	[359448.44, 159319.03, 150560.0]	3163408	2863104	5	NaN	[359487.3, 159145.66, 150600.0]	6736298	1811442	5	NaN	[359611.9, 159541.48, 150560.0]
1	6795172	[356041.88, 149555.53, 147920.0]	6795195	2863104	5	NaN	[354724.44, 149284.1, 147920.0]	6795153	1811442	5	NaN	[356366.16, 149854.86, 147920.0]
2	6795291	[355189.5, 150232.48, 148240.0]	6795293	2863104	5	NaN	[354595.62, 149464.8, 148240.0]	6795214	1811442	5	NaN	[355472.28, 150294.75, 148160.0]
3	6795747	[355030.4, 154047.86, 145800.0]	6795749	2863104	5	NaN	[355045.38, 154180.1, 145800.0]	6795745	1811442	5	NaN	[355024.44, 153945.73, 145760.0]
4	6797452	[353221.4, 148570.9, 147320.0]	6797456	2863104	5	NaN	[354213.9, 148397.44, 147320.0]	6797437	1811442	5	NaN	[353447.6, 148704.88, 147560.0]

[ ]:

import numpy as np

points = np.vstack(cn.connector_loc)

navis.plot3d([da1.idx[[1811442, 57353]],  # plot the two neurons
              points],  # plot the points of synaptic contacts as scatter
              scatter_kws=dict(name="synaptic contacts")
              )

[ ]:

import os
# 'Auth Token' from https://neuprint.janelia.org/account
SECRET_CODE = os.getenv("NEUPRINTAUTHTOKEN")

# Connect to neuprint server
import neuprint as neu

client = neu.Client('https://neuprint.janelia.org', dataset='hemibrain:v1.1', token=SECRET_CODE)

[ ]:

neu_da1 = neu.fetch_skeletons(neu.NeuronCriteria(type='DA1_lPN.*', regex=True))
neu_da1

<class 'navis.core.neuronlist.NeuronList'> containing 7 neurons (1.3MiB)

	type	name	id	n_nodes	n_connectors	n_branches	n_leafs	cable_length	soma	units
0	navis.TreeNeuron	DA1_lPN_R	722817260	4336	3132	635	658	274910.568784	NaN	8 nanometer
1	navis.TreeNeuron	DA1_lPN_R	754538881	4890	2943	626	642	291434.992623	860.0	8 nanometer
...	...	...	...	...	...	...	...	...	...	...
5	navis.TreeNeuron	DA1_lPN_R	1765040289	5380	3099	624	642	324249.403013	2467.0	8 nanometer
6	navis.TreeNeuron	DA1_lPN_R	5813039315	4300	2954	625	643	273403.049029	NaN	8 nanometer

[ ]:

split = navis.split_axon_dendrite(neu_da1[0])
split

<class 'navis.core.neuronlist.NeuronList'> containing 4 neurons (185.3KiB)

	type	name	id	n_nodes	n_connectors	n_branches	n_leafs	cable_length	soma	units	compartment
0	navis.TreeNeuron	DA1_lPN_R	722817260	0	0	0	0	0.000000	None	8 nanometer	cellbodyfiber
1	navis.TreeNeuron	DA1_lPN_R	722817260	3575	2505	566	590	188806.744034	None	8 nanometer	dendrite
2	navis.TreeNeuron	DA1_lPN_R	722817260	193	14	0	1	30601.319501	None	8 nanometer	linker
3	navis.TreeNeuron	DA1_lPN_R	722817260	474	612	60	61	48872.448763	None	8 nanometer	axon

[ ]:

navis.plot3d(split, connectors=True)

WARNING : Skipping empty neuron: DA1_lPN_R (722817260) (navis)

[ ]:

cn = neu.fetch_synapse_connections(source_criteria=neu.NeuronCriteria(type='DA1_lPN.*', regex=True),
                                   target_criteria=neu.NeuronCriteria(type='DA1_vPN.*', regex=True))
cn.head()

	bodyId_pre	bodyId_post	roi_pre	roi_post	x_pre	y_pre	z_pre	x_post	y_post	z_post	confidence_pre	confidence_post
0	754534424	733316908	LH(R)	LH(R)	5015	22921	15043	5033	22941	15044	0.972	0.991824
1	754534424	733316908	AL(R)	AL(R)	14588	35361	26453	14582	35377	26444	0.991	0.932866
2	754534424	733316908	LH(R)	LH(R)	3826	22574	14689	3842	22576	14683	0.982	0.896622
3	754534424	733316908	LH(R)	LH(R)	4924	23750	14333	4927	23768	14324	0.993	0.961103
4	754534424	733316908	LH(R)	LH(R)	5017	23642	14531	4997	23622	14518	0.904	0.661779

[ ]:

cn.groupby(['bodyId_pre', 'bodyId_post', 'roi_pre']).size()

bodyId_pre  bodyId_post  roi_pre
722817260   733316908    AL(R)      26
                         LH(R)      28
754534424   733316908    AL(R)      13
                         LH(R)      31
754538881   733316908    AL(R)      33
                         LH(R)       5
1734350788  733316908    AL(R)      21
                         LH(R)      38
1734350908  733316908    AL(R)      24
                         LH(R)      37
1765040289  733316908    AL(R)      27
                         LH(R)       2
5813039315  733316908    AL(R)      12
                         LH(R)      33
dtype: int64

neuprint connectivity queries¶

To programmatically interface with neuprint, we will use neuprint-python (link). It requires an API token which you can get via the website and is bound to the Google account that you use to log into neuprint. For this workshop we provide such a token as environment variable but you will need to start using your own token after the workshop is over.

These examples use the hemibrain v1.1 dataset.

neuprint-python¶

First we have to initialize the connection.

[ ]:

import neuprint as neu
client = neu.Client('https://neuprint.janelia.org',
                    dataset='hemibrain:v1.1',
                    token='eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJlbWFpbCI6InZmYndvcmtzaG9wLm5ldXJvZmx5MjAyMEBnbWFpbC5jb20iLCJsZXZlbCI6Im5vYXV0aCIsImltYWdlLXVybCI6Imh0dHBzOi8vbGg2Lmdvb2dsZXVzZXJjb250ZW50LmNvbS8tWXFDN21NRXd3TlEvQUFBQUFBQUFBQUkvQUFBQUFBQUFBQUEvQU1adXVjbU5zaXhXZDRhM0VyTTQ0ODBMa2IzNDdvUlpfUS9zOTYtYy9waG90by5qcGc_c3o9NTA_c3o9NTAiLCJleHAiOjE3OTQwOTE4ODd9.ceg4mrj2o-aOhK0NHNGmBacg8R34PBPoLBwhCo4uOCQ')

Most functions in neuprint-python accept neu.NeuronCriteria which is effectively a filter for body IDs, types, cellbody fibers (~lineages), etc:

[ ]:

help(neu.NeuronCriteria)

Help on class NeuronCriteria in module neuprint.neuroncriteria:

class NeuronCriteria(builtins.object)
 |  NeuronCriteria(matchvar='n', *, bodyId=None, instance=None, type=None, regex=False, cellBodyFiber=None, status=None, cropped=None, min_pre=0, min_post=0, rois=None, inputRois=None, outputRois=None, min_roi_inputs=1, min_roi_outputs=1, label=None, roi_req='all', client=None)
 |
 |  Specifies which fields to filter by when searching for a Neuron (or Segment).
 |  This class does not send queries itself, but you use it to specify search
 |  criteria for various query functions.
 |
 |  Note:
 |      For simple queries involving only particular bodyId(s) or type(s)/instance(s),
 |      you can usually just pass the ``bodyId`` or ``type`` to the query function,
 |      without constructing a full ``NeuronCriteria``.
 |
 |      .. code-block:: python
 |
 |          from neuprint import fetch_neurons, NeuronCriteria as NC
 |
 |          # Equivalent
 |          neuron_df, conn_df = fetch_neurons(NC(bodyId=329566174))
 |          neuron_df, conn_df = fetch_neurons(329566174)
 |
 |          # Equivalent
 |          # (Criteria is satisfied if either type or instance matches.)
 |          neuron_df, conn_df = fetch_neurons(NC(type="OA-VPM3", instance="OA-VPM3"))
 |          neuron_df, conn_df = fetch_neurons("OA-VPM3")
 |
 |  Methods defined here:
 |
 |  __eq__(self, value)
 |      Implement comparison between criteria.
 |      Note: 'matchvar' is not considered during the comparison.
 |
 |  __init__(self, matchvar='n', *, bodyId=None, instance=None, type=None, regex=False, cellBodyFiber=None, status=None, cropped=None, min_pre=0, min_post=0, rois=None, inputRois=None, outputRois=None, min_roi_inputs=1, min_roi_outputs=1, label=None, roi_req='all', client=None)
 |      Except for ``matchvar``, all parameters must be passed as keyword arguments.
 |
 |      .. note::
 |
 |          **Options for specifying ROI criteria**
 |
 |          The ``rois`` argument merely matches neurons that intersect the given ROIs at all
 |          (without distinguishing between inputs and outputs).
 |
 |          The ``inputRois`` and ``outputRois`` arguments allow you to put requirements
 |          on whether or not neurons have inputs or outputs in the listed ROIs.
 |          It results a more expensive query, but its more powerful.
 |          It also enables you to require a minimum number of connections in the given
 |          ``inputRois`` or ``outputRois`` using the ``min_roi_inputs`` and ``min_roi_outputs``
 |          criteria.
 |
 |          In either case, use use ``roi_req`` to specify whether a neuron must match just
 |          one (``any``) of the listed ROIs, or ``all`` of them.
 |
 |      Args:
 |          matchvar (str):
 |              An arbitrary cypher variable name to use when this
 |              ``NeuronCriteria`` is used to construct cypher queries.
 |              To help catch errors (such as accidentally passing a ``type`` or
 |              ``instance`` name in the wrong argument position), we require that
 |              ``matchvar`` begin with a lowercase letter.
 |
 |          bodyId (int or list of ints):
 |              List of bodyId values.
 |
 |          instance (str or list of str):
 |              If ``regex=True``, then the instance will be matched as a regular expression.
 |              Otherwise, only exact matches are found. To search for neurons with no instance
 |              at all, use ``instance=[None]``. If both ``type`` and ``instance`` criteria are
 |              supplied, any neuron that matches EITHER criteria will match the overall criteria.
 |
 |          type (str or list of str):
 |              If ``regex=True``, then the type will be matched as a regular expression.
 |              Otherwise, only exact matches are found. To search for neurons with no type
 |              at all, use ``type=[None]``. If both ``type`` and ``instance`` criteria are
 |              supplied, any neuron that matches EITHER criteria will match the overall criteria.
 |
 |          regex (bool):
 |              If ``True``, the ``instance`` and ``type`` arguments will be interpreted as
 |              regular expressions, rather than exact match strings.
 |
 |          cellBodyFiber (str or list of str):
 |              Matches for the neuron ``cellBodyFiber`` field.  To search for neurons
 |              with no CBF at all, use ``cellBodyFiber=[None]``.
 |
 |          status (str or list of str):
 |              Matches for the neuron ``status`` field.  To search for neurons with no status
 |              at all, use ``status=[None]``.
 |
 |          cropped (bool):
 |              If given, restrict results to neurons that are cropped or not.
 |
 |          min_pre (int):
 |              Exclude neurons that don't have at least this many t-bars (outputs) overall,
 |              regardless of how many t-bars exist in any particular ROI.
 |
 |          min_post (int):
 |              Exclude neurons that don't have at least this many PSDs (inputs) overall,
 |              regardless of how many PSDs exist in any particular ROI.
 |
 |          rois (str or list of str):
 |              ROIs that merely intersect the neuron, without specifying whether
 |              they're intersected by input or output synapses.
 |              If not provided, will be auto-set from ``inputRois`` and ``outputRois``.
 |
 |          inputRois (str or list of str):
 |              Only Neurons which have inputs in EVERY one of the given ROIs will be matched.
 |              ``regex`` does not apply to this parameter.
 |
 |          outputRois (str or list of str):
 |              Only Neurons which have outputs in EVERY one of the given ROIs will be matched.
 |              ``regex`` does not apply to this parameter.
 |
 |          min_roi_inputs (int):
 |              How many input (post) synapses a neuron must have in each ROI to satisfy the
 |              ``inputRois`` criteria.  Can only be used if you provided ``inputRois``.
 |
 |          min_roi_outputs (int):
 |              How many output (pre) synapses a neuron must have in each ROI to satisfy the
 |              ``outputRois`` criteria.   Can only be used if you provided ``outputRois``.
 |
 |          roi_req (Either ``'any'`` or ``'all'``):
 |              Whether a neuron must intersect all of the listed input/output ROIs, or any of the listed input/output ROIs.
 |              When using 'any', each neuron must still match at least one input AND at least one output ROI.
 |
 |          label (Either ``'Neuron'`` or ``'Segment'``):
 |              Which node label to match with.
 |              (In neuprint, all ``Neuron`` nodes are also ``Segment`` nodes.)
 |              By default, ``'Neuron'`` is used, unless you provided a non-empty ``bodyId`` list.
 |              In that case, ``'Segment'`` is the default. (It's assumed you're really interested
 |              in the bodies you explicitly listed, whether or not they have the ``'Neuron'`` label.)
 |
 |          client (:py:class:`neuprint.client.Client`):
 |              Used to validate ROI names.
 |              If not provided, the global default ``Client`` will be used.
 |
 |  __repr__(self)
 |      Return repr(self).
 |
 |  all_conditions(self, *vars, prefix=0, comments=True)
 |
 |  basic_conditions(self, prefix=0, comments=True)
 |      Construct a WHERE clause based on the basic conditions
 |      in this criteria (i.e. everything except for the "directed ROI" conditions.)
 |
 |  basic_exprs(self)
 |      Return the list of expressions that correspond
 |      to the members in this NeuronCriteria object.
 |      They're intended be combined (via 'AND') in
 |      the WHERE clause of a cypher query.
 |
 |  bodyId_expr(self)
 |
 |  cbf_expr(self)
 |
 |  cropped_expr(self)
 |
 |  directed_rois_condition(self, *vars, prefix=0, comments=True)
 |      Construct the ```WITH...WHERE``` statements that apply the "directed ROI"
 |      conditions specified by this criteria's ``inputRois`` and ``outputRois``
 |      members.
 |
 |      These conditions are expensive to evaluate, so it's usually a good
 |      idea to position them LAST in your cypher query, once the result set
 |      has already been narrowed down by eariler filters.
 |
 |  global_vars(self)
 |
 |  global_with(self, *vars, prefix=0)
 |
 |  instance_expr(self)
 |
 |  post_expr(self)
 |
 |  pre_expr(self)
 |
 |  rois_expr(self)
 |
 |  status_expr(self)
 |
 |  type_expr(self)
 |
 |  typeinst_expr(self)
 |      Unlike all other fields, type and instance OR'd together.
 |      Either match satisfies the criteria.
 |
 |  ----------------------------------------------------------------------
 |  Class methods defined here:
 |
 |  combined_conditions(neuron_conditions, vars=[], prefix=0, comments=True) from builtins.type
 |      Combine the conditions from multiple NeuronCriteria into a single string,
 |      putting the "cheap" conditions first and the "expensive" conditions last.
 |      (That is, basic conditions first and the directed ROI conditions last.)
 |
 |  combined_global_with(neuron_conditions, vars=[], prefix=0) from builtins.type
 |
 |  ----------------------------------------------------------------------
 |  Data descriptors defined here:
 |
 |  __dict__
 |      dictionary for instance variables (if defined)
 |
 |  __weakref__
 |      list of weak references to the object (if defined)
 |
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |
 |  MAX_LITERAL_LENGTH = 3
 |
 |  __hash__ = None

Fetching neurons¶

Let’s say we want to find all antennnal lobe projection neurons (PNs). Their type nomenclature adheres to {glomerulus}_{lineage}PN (e.g. DA1_lPN)for uniglomerular PNs and a M_{lineage}PN{tract}{type} (e.g. M_vPNml50 = “multiglomerular ventral lineage PN mediolateral tract type 50) for multiglomerular PNs.

To get them all, we need to use regex patterns (see this cheatsheet):

[ ]:

# Define the filter criteria
nc = neu.NeuronCriteria(type='.*?_.*?PN.*?', regex=True)

# Get general info for these neurons
pns, roi_info = neu.fetch_neurons(nc)

print(f'{pns.shape[0]} PNs found.')

pns.head()

337 PNs found.

	bodyId	instance	type	pre	post	size	status	cropped	statusLabel	cellBodyFiber	somaRadius	somaLocation	inputRois	outputRois	roiInfo
0	294792184	M_vPNml53_R	M_vPNml53	92	344	420662445	Traced	False	Roughly traced	AVM04	336.5	[18923, 34319, 35424]	[AL(R), AL-D(R), AL-DA2(R), AL-DA4m(R), AL-DC1...	[AL(R), AL-DC1(R), LH(R), PLP(R), SIP(R), SLP(...	{'SNP(R)': {'pre': 70, 'post': 155, 'downstrea...
1	329599710	M_lvPNm32_R	M_lvPNm32	247	285	343478957	Traced	False	Roughly traced	AVM06	NaN	None	[AL(R), AL-DC4(R), AL-DL2v(R), AL-DM1(R), AL-D...	[AL(R), AL-DL2v(R), AL-DM1(R), AL-DM4(R), AL-D...	{'SNP(R)': {'pre': 180, 'post': 93, 'downstrea...
2	417199910	M_lvPNm36_R	M_lvPNm36	162	347	387058559	Traced	False	Roughly traced	AVM06	351.5	[13823, 33925, 34176]	[AL(R), AL-DL5(R), AL-DM4(R), AL-DP1m(R), AL-V...	[AL(R), AL-DL5(R), AL-DM4(R), AL-VP1d(R), AL-V...	{'SNP(R)': {'pre': 156, 'post': 95, 'downstrea...
3	480927537	M_vPNml70_R	M_vPNml70	82	276	240153322	Traced	False	Roughly traced	AVM04	NaN	None	[AL(R), AL-DA2(R), AL-DA4l(R), AL-DA4m(R), AL-...	[LH(R), SLP(R), SNP(R)]	{'SNP(R)': {'pre': 15, 'post': 18, 'downstream...
4	481268653	M_vPNml89_R	M_vPNml89	146	58	265085609	Traced	False	Roughly traced	AVM04	NaN	None	[AL(R), AL-VC3l(R), AL-VC4(R), AL-VP1m(R), LH(...	[LH(R), SLP(R), SNP(R)]	{'SNP(R)': {'pre': 10, 'post': 2, 'downstream'...

[ ]:

# Check that the regex did not have any accidental by-catch
pns['type'].unique()

array(['M_vPNml53', 'M_lvPNm32', 'M_lvPNm36', 'M_vPNml70', 'M_vPNml89',
       'VP1l+_lvPN', 'M_vPNml69', 'DM1_lPN', 'DM4_vPN', 'M_vPNml79',
       'VP4+_vPN', 'DA4l_adPN', 'M_vPNml87', 'DM4_adPN', 'M_vPNml83',
       'VA5_lPN', 'DA4m_adPN', 'M_lvPNm24', 'M_vPNml85', 'VP1l+VP3_ilPN',
       'M_vPNml77', 'M_vPNml84', 'VC1_lPN', 'M_lvPNm39', 'M_vPNml50',
       'DM2_lPN', 'VC5_lvPN', 'M_vPNml88', 'M_vPNml58', 'VP4_vPN',
       'DP1m_vPN', 'DP1m_adPN', 'DM5_lPN', 'VC5_adPN', 'M_vPNml80',
       'M_lvPNm25', 'VC3m_lvPN', 'VP3+_vPN', 'VP1m+_lvPN', 'DA3_adPN',
       'V_l2PN', 'M_vPNml56', 'VC3l_adPN', 'VM7v_adPN', 'DL5_adPN',
       'VM4_adPN', 'VM2_adPN', 'M_lvPNm40', 'DC4_vPN', 'V_ilPN',
       'M_vPNml74', 'Z_lvPNm1', 'DA1_lPN', 'DP1l_adPN', 'VM4_lvPN',
       'M_vPNml71', 'DP1l_vPN', 'M_lvPNm41', 'M_spPN5t10', 'DA1_vPN',
       'VC4_adPN', 'DM3_adPN', 'M_lvPNm45', 'VL1_vPN', 'M_lvPNm44',
       'M_vPNml78', 'M_vPNml67', 'M_adPNm5', 'M_smPNm1', 'DM6_adPN',
       'DL2d_adPN', 'M_adPNm6', 'M_adPNm8', 'M_lvPNm43', 'Z_vPNml1',
       'M_vPNml59', 'DA2_lPN', 'M_lPNm11A', 'M_vPNml52', 'DL2d_vPN',
       'VL2p_vPN', 'VA1d_adPN', 'M_lPNm11B', 'M_lvPNm48', 'M_lPNm11C',
       'M_lvPNm42', 'VA1v_vPN', 'M_vPNml68', 'M_vPNml55', 'M_vPNml62',
       'VL2a_vPN', 'M_vPNml60', 'M_vPNml65', 'VM5d_adPN', 'M_l2PNm16',
       'M_vPNml61', 'M_vPNml57', 'M_vPNml64', 'M_lv2PN9t49',
       'VP2+VC5_l2PN', 'M_spPN4t9', 'M_vPNml66', 'M_vPNml75', 'M_vPNml63',
       'M_vPNml72', 'M_lvPNm38', 'D_adPN', 'M_vPNml76', 'M_vPNml54',
       'DM3_vPN', 'M_vPNml86', 'DL3_lPN', 'VA4_lPN', 'VP1d_il2PN',
       'DC1_adPN', 'M_l2PN3t18', 'M_lvPNm35', 'DL4_adPN', 'M_lvPNm28',
       'M_lvPNm27', 'M_ilPNm90', 'M_l2PNl20', 'M_lvPNm29', 'VA7l_adPN',
       'M_lPNm13', 'M_l2PNl21', 'DL1_adPN', 'M_imPNl92', 'M_vPNml73',
       'M_ilPN8t91', 'M_l2PNm14', 'VP1d+VP4_l2PN1', 'M_lvPNm26',
       'DL2v_adPN', 'VP3+VP1l_ivPN', 'M_lvPNm33', 'VA1v_adPN',
       'VP3+_l2PN', 'M_l2PN10t19', 'VP4+VL1_l2PN', 'M_l2PNl22',
       'M_l2PNm15', 'M_lPNm11D', 'MZ_lv2PN', 'DC2_adPN', 'M_lvPNm46',
       'VC2_lPN', 'VM1_lPN', 'VM3_adPN', 'VM7d_adPN', 'M_lvPNm47',
       'M_lPNm12', 'DC3_adPN', 'VP2+_adPN', 'VP1m+VP2_lvPN2',
       'VP1m+VP2_lvPN1', 'VA6_adPN', 'VA7m_lPN', 'M_adPNm7', 'M_adPNm4',
       'VA1d_vPN', 'VA3_adPN', 'VL1_ilPN', 'M_l2PNl23', 'M_lvPNm31',
       'VP1m+VP5_ilPN', 'VL2p_adPN', 'MZ_lvPN', 'VP2_adPN', 'VA2_adPN',
       'VM5v_adPN', 'VP5+VP2_l2PN', 'VP5+VP3_l2PN', 'VP5+_l2PN',
       'M_vPNml51', 'M_smPN6t2', 'M_lvPNm37', 'M_vPNml82', 'M_adPNm3',
       'VP1m_l2PN', 'DC4_adPN', 'VP5+Z_adPN', 'VL2a_adPN', 'VP2_l2PN',
       'M_lvPNm34', 'VP2+Z_lvPN', 'M_lvPNm30', 'M_l2PNm17', 'M_vPNml81',
       'VP1d+VP4_l2PN2'], dtype=object)

Find neuron types with VFB instead (this allows curated neuron classes to be used):

[ ]:

#This will get all ALPNs from ALL datasets on VFB
ALPNs = vc.get_instances("'adult antennal lobe projection neuron'", summary=True)
ALPNs = pd.DataFrame.from_records(ALPNs)
#Select only rows from Hemibrain1.1 dataset
ALPNs=ALPNs[ALPNs['data_source'].str.match('neuprint_JRC_Hemibrain_1point1')]
ALPNs

Running query: FBbt:00067123
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00067123&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 1469

	label	symbol	id	tags	parents_label	parents_id	data_source	accession	templates	dataset	license
0	M_vPNml53_R - 294792184		VFB_jrchk0y5	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00007414	neuprint_JRC_Hemibrain_1point1	294792184	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
4	DC2_adPN_R - 1828206773		VFB_jrchjtdt	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron DC2 adPN	FBbt_00067354	neuprint_JRC_Hemibrain_1point1	1828206773	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
8	VM4_lvPN_R - 1850024931		VFB_jrchk7ib	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron VM4 lvPN	FBbt_00049775	neuprint_JRC_Hemibrain_1point1	1850024931	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
18	M_vPNml81_R - 5813090752		VFB_jrchk0zj	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00007414	neuprint_JRC_Hemibrain_1point1	5813090752	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
21	M_lvPNm48_R - 1975187554		VFB_jrchk0xt	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00049779	neuprint_JRC_Hemibrain_1point1	1975187554	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
...	...	...	...	...	...	...	...	...	...	...	...
1451	Z_vPNml1(noAL+SEZ)_R - 729608317		VFB_jrchk7zr	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult antennal lobe projection neuron	FBbt_00067123	neuprint_JRC_Hemibrain_1point1	729608317	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
1456	M_vPNml63_R - 1857143769		VFB_jrchk0yl	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00007414	neuprint_JRC_Hemibrain_1point1	1857143769	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
1461	M_vPNml66_R - 5813071409		VFB_jrchk0yr	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00007414	neuprint_JRC_Hemibrain_1point1	5813071409	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
1465	M_lPNm11A_R - 5813044177		VFB_jrchk0w9	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00049778	neuprint_JRC_Hemibrain_1point1	5813044177	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
1468	M_l2PNm14_R - 1723506321		VFB_jrchk0w4	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult multiglomerular antennal lobe projection...	FBbt_00049910	neuprint_JRC_Hemibrain_1point1	1723506321	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...

339 rows × 11 columns

Fetching synaptic partners¶

Looks good! Next: What’s downstream of those PNs?

[ ]:

ds = neu.fetch_simple_connections(upstream_criteria=list(map(int, ALPNs['accession'])))
#using hemibrain regex
#ds = neu.fetch_simple_connections(upstream_criteria=neu.NeuronCriteria(bodyId=pns.bodyId.values))
ds

	bodyId_pre	bodyId_post	weight	type_pre	type_post	instance_pre	instance_post	conn_roiInfo
0	635062078	1671292719	390	DP1m_adPN	lLN2T_c	DP1m_adPN_R	lLN2T_c(Tortuous)_R	{'AL(R)': {'pre': 390, 'post': 390}, 'AL-DP1m(...
1	635062078	1704347707	326	DP1m_adPN	lLN2T_c	DP1m_adPN_R	lLN2T_c(Tortuous)_R	{'AL(R)': {'pre': 324, 'post': 324}, 'AL-DP1m(...
2	542634818	1704347707	322	DM1_lPN	lLN2T_c	DM1_lPN_R	lLN2T_c(Tortuous)_R	{'AL(R)': {'pre': 322, 'post': 322}, 'AL-DM1(R...
3	635062078	1640922516	320	DP1m_adPN	lLN2T_e	DP1m_adPN_R	lLN2T_e(Tortuous)_R	{'AL(R)': {'pre': 317, 'post': 316}, 'AL-DP1m(...
4	724816115	1670916819	318	DP1l_adPN	lLN2P_a	DP1l_adPN_R	lLN2P_a(Patchy)_R	{'AL(R)': {'pre': 318, 'post': 318}, 'AL-DP1l(...
...	...	...	...	...	...	...	...	...
101862	5901222910	5813086037	1	DM2_lPN	None	DM2_lPN_R	None	{'LH(R)': {'pre': 1, 'post': 1}}
101863	5901222910	5813095915	1	DM2_lPN	KCg-m	DM2_lPN_R	KCg-m_R	{'MB(R)': {'pre': 1, 'post': 1}, 'CA(R)': {'pr...
101864	5901222910	5813129316	1	DM2_lPN	LHAV6a1_b	DM2_lPN_R	LHAV6a1_b_R	{'LH(R)': {'pre': 1, 'post': 1}}
101865	5901222910	5901193783	1	DM2_lPN	LHAV4g4_a	DM2_lPN_R	LHAV4g4_a_R	{'LH(R)': {'pre': 1, 'post': 1}}
101866	5901222910	5901203780	1	DM2_lPN	LHAV4g11	DM2_lPN_R	LHAV4g11_R	{'LH(R)': {'pre': 1, 'post': 1}}

101867 rows × 8 columns

Each row is now a connections from a single up- to a single downstream neuron. The “weight” is the number of synapses between the pre- and the postsynaptic neuron. Let’s simplify by grouping by type:

[ ]:

by_type = ds.groupby(['type_pre', 'type_post'], as_index=False).weight.sum()
by_type.sort_values('weight', ascending=False, inplace=True)
by_type.reset_index(drop=True, inplace=True)
by_type

	type_pre	type_post	weight
0	DC3_adPN	KCg-m	3670
1	VM5d_adPN	KCg-m	3219
2	DC1_adPN	KCg-m	3215
3	VL2a_adPN	KCg-m	3096
4	DA1_lPN	KCg-m	3078
...	...	...	...
40631	M_vPNml50	WEDPN4	1
40632	M_vPNml50	WEDPN12	1
40633	M_vPNml50	V_ilPN	1
40634	M_vPNml50	VP4+VL1_l2PN	1
40635	Z_vPNml1	mALD2	1

40636 rows × 3 columns

The strongest connections are between PNs and Kenyon Cells (KCs). That’s not much of a surprise since there are thousands of KCs. For the sake of the argument let’s say we want to know where these connections occur:

[ ]:

#First find KCs in VFB
KCs = pd.DataFrame.from_records(vc.get_instances("'adult Kenyon cell'", summary=True))
#Select only rows from Hemibrain1.1 dataset
KCs=KCs[KCs['data_source'].str.match('neuprint_JRC_Hemibrain_1point1')]
KCs

Running query: FBbt:00049825
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00049825&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 5107

	label	symbol	id	tags	parents_label	parents_id	data_source	accession	templates	dataset	license
0	KCg-m_R - 692810011		VFB_jrchjw74	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	692810011	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
2	KCg-m_R - 477909679		VFB_jrchjw36	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	477909679	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
5	KCab-m_R - 5812980222		VFB_jrchjvqk	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	alpha/beta Kenyon cell	FBbt_00100248	neuprint_JRC_Hemibrain_1point1	5812980222	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
6	KCg-m_R - 5812982832		VFB_jrchjw83	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	5812982832	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
7	KCab-m_R - 5812980806		VFB_jrchjvr2	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	alpha/beta Kenyon cell	FBbt_00100248	neuprint_JRC_Hemibrain_1point1	5812980806	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
...	...	...	...	...	...	...	...	...	...	...	...
5093	KCg-m_R - 664184060		VFB_jrchjwck	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	664184060	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
5096	KCg-m_R - 5812982620		VFB_jrchjwdd	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	5812982620	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
5100	KCg-m_R - 661430179		VFB_jrchjwdb	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	661430179	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
5101	KCg-m_R - 5812982436		VFB_jrchjwcz	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	5812982436	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
5102	KCg-m_R - 663834152		VFB_jrchjwa9	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	gamma main Kenyon cell	FBbt_00111061	neuprint_JRC_Hemibrain_1point1	663834152	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...

1927 rows × 11 columns

[ ]:

adj, roi_info2 = neu.fetch_adjacencies(sources=list(map(int, ALPNs['accession'])), targets=list(map(int, KCs['accession'])))
roi_info2.head()

	bodyId_pre	bodyId_post	roi	weight
0	542634818	301314208	CA(R)	6
1	542634818	331999156	CA(R)	1
2	542634818	332344592	CA(R)	2
3	542634818	332344908	CA(R)	9
4	542634818	332353106	CA(R)	13

[ ]:

# Group by region of interest (ROI)
by_roi = roi_info2.groupby('roi').weight.sum()
by_roi.head()

roi
CA(R)         181274
NotPrimary      2764
PLP(R)            11
SCL(R)           509
SLP(R)          2026
Name: weight, dtype: int64

[ ]:

ax = by_roi.plot.bar()
ax.set_xlabel('')
ax.set_ylabel('PN to KC synapses')

Text(0, 0.5, 'PN to KC synapses')

../_images/tutorials_connectomics_53_1.png

Querying paths¶

Let’s say we want to find out how to go from a PN (second order olfactory neurons) all the way to a descending neuron (presumably leading to motor neurons in the VNC).

[ ]:

#First find DNs in VFB
DNs = pd.DataFrame.from_records(vc.get_instances("'adult descending neuron'", summary=True))
#Select only rows from Hemibrain1.1 dataset
DNs=DNs[DNs['data_source'].str.match('neuprint_JRC_Hemibrain_1point1')]
DNs

Running query: FBbt:00047511
Query URL: http://owl.virtualflybrain.org/kbs/vfb/instances?object=FBbt%3A00047511&prefixes=%7B%22FBbt%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FFBbt_%22%2C+%22RO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FRO_%22%2C+%22BFO%22%3A+%22http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FBFO_%22%7D&direct=False
Query results: 139

	label	symbol	id	tags	parents_label	parents_id	data_source	accession	templates	dataset	license
2	DNp27_R - 1072063538		VFB_jrchjthm	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the posterior brain DNp27	FBbt_00047660	neuprint_JRC_Hemibrain_1point1	1072063538	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
3	DNp16/17_R - 1655626125		VFB_jrchjth0	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the posterior brain	FBbt_00047517	neuprint_JRC_Hemibrain_1point1	1655626125	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
7	DNb04_R - 1037393225		VFB_jrchjtfx	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the anterior ventral brai...	FBbt_00047585	neuprint_JRC_Hemibrain_1point1	1037393225	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
9	DNp32_R - 5813050455		VFB_jrchjthr	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the posterior brain DNp32	FBbt_00047665	neuprint_JRC_Hemibrain_1point1	5813050455	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
11	NPFP1_R - 1043117106		VFB_jrchk103	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult NPF P1 neuron	FBbt_00048120	neuprint_JRC_Hemibrain_1point1	1043117106	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
...	...	...	...	...	...	...	...	...	...	...	...
133	DNp17_R - 1498383456		VFB_jrchjth4	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the posterior brain DNp17	FBbt_00047650	neuprint_JRC_Hemibrain_1point1	1498383456	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
134	DNg30_R - 571346836		VFB_jrchjtg4	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the gnathal ganglion DNg30	FBbt_00047622	neuprint_JRC_Hemibrain_1point1	571346836	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
135	DNp27_L - 5813020347		VFB_jrchjthn	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	descending neuron of the posterior brain DNp27	FBbt_00047660	neuprint_JRC_Hemibrain_1point1	5813020347	JRC_FlyEM_Hemibrain\|JRC2018Unisex	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
137	MDN(PDM13)_L - 5813021079		VFB_jrchk0vf	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	adult moonwalker descending neuron	FBbt_00111308	neuprint_JRC_Hemibrain_1point1	5813021079	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...
138	Giant Fiber_R - 2307027729		VFB_jrchjup1	Entity\|has_image\|Adult\|Anatomy\|has_neuron_conn...	giant fiber neuron	FBbt_00004020	neuprint_JRC_Hemibrain_1point1	2307027729	JRC2018Unisex\|JRC_FlyEM_Hemibrain	Xu2020NeuronsV1point1	https://creativecommons.org/licenses/by/4.0/le...

101 rows × 11 columns

[ ]:

# First fetch the DNs
dns, _ = neu.fetch_neurons(list(map(int, DNs['accession'])))
#with regex
#dns, _ = neu.fetch_neurons(neu.NeuronCriteria(type='(.*DN[^1]{0,}.*|Giant Fiber)', regex=True))
dns

	bodyId	instance	type	pre	post	size	status	cropped	statusLabel	cellBodyFiber	somaRadius	somaLocation	inputRois	outputRois	roiInfo
0	451689001	DNp25_R	DNp25	165	803	948309595	Traced	False	Roughly traced	PDM05	361.0	[22185, 13374, 16352]	[ATL(R), CA(R), FLA(R), INP, LH(R), MB(+ACA)(R...	[ATL(R), CA(R), INP, MB(+ACA)(R), MB(R), SLP(R...	{'SNP(R)': {'pre': 158, 'post': 703, 'downstre...
1	512851433	DNp10(PDM27)_L	DNp10	25	2487	3010901036	Traced	False	Roughly traced	None	483.5	[27334, 21053, 7640]	[ATL(L), GC, GOR(L), IB, ICL(L), INP, SCL(L), ...	[ATL(L), GC, GOR(L), ICL(L), INP, SCL(L), SMP(...	{'SNP(L)': {'pre': 7, 'post': 512, 'downstream...
2	519949044	oviDNb_R	oviDNb	7	1089	1618057638	Traced	False	Roughly traced	PDM15	396.0	[19291, 15307, 11696]	[AVLP(R), CA(R), CRE(-ROB,-RUB)(R), CRE(-RUB)(...	[CRE(-RUB)(L), CRE(L), INP, SIP(R), SMP(L), SM...	{'SNP(R)': {'pre': 2, 'post': 656, 'downstream...
3	542751938	DNp44_R	DNp44	182	634	1674610618	Traced	False	Roughly traced	PVM09	321.0	[18875, 11698, 13136]	[AL(R), AL-VL1(R), AL-VL2p(R), AL-VP1d(R), AL-...	[AL(R), AL-DP1m(R), AL-VL1(R), AL-VL2p(R), AL-...	{'SNP(R)': {'pre': 21, 'post': 277, 'downstrea...
4	550655668	oviDNa_R	oviDNa	36	1111	1887226051	Traced	False	Roughly traced	PDM15	301.0	[19199, 15756, 13456]	[AOTU(R), CRE(-ROB,-RUB)(R), CRE(R), EPA(R), I...	[CRE(-ROB,-RUB)(R), CRE(R), INP, SCL(R), SIP(R...	{'SNP(R)': {'pre': 14, 'post': 772, 'downstrea...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	5813068915	NPFP1(PDM12)_L	NPFP1	123	108	4221447995	Traced	False	Roughly traced	None	539.5	[28242, 20366, 6808]	[ATL(L), AVLP(R), CAN(R), EPA(R), GOR(R), IB, ...	[AVLP(R), CAN(R), EPA(R), GOR(R), IB, ICL(L), ...	{'INP': {'pre': 34, 'post': 47, 'downstream': ...
97	5813077405	DNd01_L	DNd01	21	731	738596888	Traced	False	Roughly traced	None	342.0	[25173, 33207, 14176]	[SMP(L), SMP(R), SNP(L), SNP(R)]	[SMP(L), SMP(R), SNP(L), SNP(R)]	{'SNP(L)': {'pre': 13, 'post': 675, 'downstrea...
98	5813078134	DNp15_R	DNp15	21	2310	1288564222	Traced	False	Roughly traced	None	NaN	None	[CAN(R), GNG, IPS(R), PENP, SPS(R), VLNP(R), V...	[GNG, IPS(R), VMNP]	{'VMNP': {'pre': 7, 'post': 860, 'downstream':...
99	5813095193	DNp16_R	DNp16	70	683	292910198	Traced	False	Roughly traced	PVM02	276.0	[25941, 10730, 20000]	[CAN(R), GNG, IB, INP, IPS(R), PENP, SPS(R), V...	[CAN(R), GNG, IPS(R), PENP, SPS(R), VMNP]	{'INP': {'post': 34, 'upstream': 34}, 'IB': {'...
100	5813108230	DNp16/17_R	DNp16/17	5	177	304268206	Traced	False	Roughly traced	PVM02	321.5	[23677, 10111, 18544]	[GNG, IB, INP, IPS(R), SPS(R), VMNP]	[IPS(R), SPS(R), VMNP]	{'INP': {'post': 2, 'upstream': 2}, 'IB': {'po...

101 rows × 15 columns

Neuprint lets you query paths from a single source to a single target. For multi-source or -target queries, your best bet is to download the entire graph and run the queries locally using networkx or igraph.

[ ]:

# Find all paths from A PN to A DNs
paths = neu.fetch_shortest_paths(upstream_bodyId=list(map(int, ALPNs['accession']))[0],
                                 downstream_bodyId=list(map(int, DNs['accession']))[0],
                                 min_weight=10)
paths

	path	bodyId	type	weight
0	0	294792184	M_vPNml53	0
1	0	5813057148	SLP387	16
2	0	511349908	SMP183	19
3	0	545501062	SMP464	10
4	0	739256609	CL029	25
...	...	...	...	...
343	57	5813057148	SLP387	16
344	57	357515217	SLP365	12
345	57	479607277	CL102	10
346	57	449250041	CL026	22
347	57	1072063538	DNp27	22

348 rows × 4 columns

So it looks like there are three separate 7-hop paths to go from M_vPNml53 to DN1a. Let’s visualize the neurons involved!

Fetching meshes & skeletons¶

You can fetch skeletons as SWCs directly via neuprint-python. For visualization however it’s easiest to load neuron morphologies via navis. For that navis wraps neuprint-python and adds some convenience functions (see also the tutorial):

[ ]:

# Import the wrapped neuprint-python
# -> this exposes ALL base functions plus a couple navis-specific extras
import navis
import navis.interfaces.neuprint as neu

#client = neu.Client('https://neuprint.janelia.org', dataset='hemibrain:v1.1')

# Fetch neurons in the first path
nl = neu.fetch_skeletons(paths.loc[(paths.path == 0), 'bodyId'])
nl

<class 'navis.core.neuronlist.NeuronList'> containing 6 neurons (3.1MiB)

	type	name	id	n_nodes	n_connectors	n_branches	n_leafs	cable_length	soma	units
0	navis.TreeNeuron	M_vPNml53_R	294792184	3670	436	180	190	1.877807e+05	3649	8 nanometer
1	navis.TreeNeuron	SMP464_R	545501062	4507	454	183	193	1.888869e+05	4	8 nanometer
...	...	...	...	...	...	...	...	...	...	...
4	navis.TreeNeuron	DNp27_R	1072063538	47115	9298	1750	1888	2.720497e+06	25808	8 nanometer
5	navis.TreeNeuron	CL029_R	739256609	31953	7932	2660	2709	1.275071e+06	6033	8 nanometer

[ ]:

# Let's also get some ROI meshes
al = neu.fetch_roi('AL(R)')
lh = neu.fetch_roi('LH(R)')
ca = neu.fetch_roi('CA(R)')

[ ]:

# Plot
navis.plot3d([nl, lh, al, ca], width=1100)

Plotting graphs

There are various ways of plotting static graphs. In theory Jupyter notebooks lend themselves to interactive graphs too but unfortunately DeepNote does not yet support the required libraries (e.g. ipywidgets). That being said: if you want to run this locally or on Google colab, check out ipycytoscape.

There are numerous options to do this but we will use networkx to plot a static graph:

[ ]:

import networkx as nx
import numpy as np

# Initialize the graph
G = nx.DiGraph()

# Generate edges from the paths
edges = []
for p in paths.path.unique():
    this_path = paths.loc[(paths.path == p)]
    this_edges = list(zip(this_path.values[:-1], this_path.values[1:]))

    for i in range(this_path.shape[0] - 1):
        edges.append([this_path.bodyId.values[i], this_path.bodyId.values[i + 1], this_path.weight.values[i + 1]])

# Add the edges
G.add_weighted_edges_from(edges)

# Add some names to the nodes
nx.set_node_attributes(G, paths.set_index('bodyId')['type'].to_dict(), name='name')

[ ]:

import matplotlib.pyplot as plt

# Draw using a simple force-directed layout
pos = nx.kamada_kawai_layout(G)

# We could draw everything in one step but this way we have more control over the plot
fig, ax = plt.subplots(figsize=(10, 10))

# Draw nodes
nx.draw_networkx_nodes(G, pos=pos, ax=ax)

# Draw edges
weights = np.array([e[2]['weight'] for e in G.edges(data=True)])
nx.draw_networkx_edges(G, pos=pos, width=(weights / 12).tolist())

# Add node labels
nx.draw_networkx_labels(G, pos=pos, labels=dict(G.nodes('name')), font_size=14)

# Turn axes of
ax.set_axis_off()

../_images/tutorials_connectomics_65_0.png

In general, I recommend exporting your graph to e.g. graphml and importing it into e.g. cytoscape if you want to explore an interactive network graph.

[ ]:

nx.write_gml(G, "my_graph.gml")