Allan Jones: A map of the brain

164,682 views ใƒป 2011-11-10

TED


์•„๋ž˜ ์˜๋ฌธ์ž๋ง‰์„ ๋”๋ธ”ํด๋ฆญํ•˜์‹œ๋ฉด ์˜์ƒ์ด ์žฌ์ƒ๋ฉ๋‹ˆ๋‹ค.

๋ฒˆ์—ญ: Davis Bae ๊ฒ€ํ† : Bianca Lee
00:15
Humans have long held a fascination
0
15260
2000
์ธ๊ฐ„์€ ์•„์ฃผ ์˜ค๋žซ๋™์•ˆ ์ธ๊ฐ„์˜ ๋‡Œ์˜ ์‹ ๋น„์—
00:17
for the human brain.
1
17260
2000
๋งค๋ฃŒ๋˜์–ด ์™”์Šต๋‹ˆ๋‹ค.
00:19
We chart it, we've described it,
2
19260
3000
์šฐ๋ฆฌ๋Š” ๋‡Œ๋ฅผ ํ‘œ๋กœ ๊ทธ๋ฆฌ๊ณ , ์„ค๋ช…์„ ๋ง๋ถ™์ด๊ณ ,
00:22
we've drawn it,
3
22260
2000
๊ทธ๋ฆผ์œผ๋กœ ํ‘œํ˜„ํ•˜์˜€์œผ๋ฉฐ,
00:24
we've mapped it.
4
24260
3000
์ง€๋„๋กœ ๋งŒ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค.
00:27
Now just like the physical maps of our world
5
27260
3000
์ด์ œ ์˜ค๋Š˜๋‚ ์˜ ๊ธฐ์ˆ ์— ์˜ํ–ฅ์„ ๋ฐ›์•„
00:30
that have been highly influenced by technology --
6
30260
3000
๋ณ€ํ™”ํ•˜๋Š” ์‹ค์ œ ์ง€๋„๋“ค์„ ์ƒ๊ฐํ•ด๋ณด์‹œ์ฃ 
00:33
think Google Maps,
7
33260
2000
๊ตฌ๊ธ€ ์ง€๋„๋ฅผ ๋– ์˜ฌ๋ ค ๋ณด์‹œ๊ณ ,
00:35
think GPS --
8
35260
2000
GPS ๋ฅผ ์ƒ๊ฐํ•ด ๋ณด์„ธ์š”
00:37
the same thing is happening for brain mapping
9
37260
2000
์ด๋Ÿฐ ๊ธฐ์ˆ ์˜ ๋ณ€ํ™”๊ฐ€ ๋‘๋‡Œ ์ง€๋„ ์ƒ์„ฑ์—๋„
00:39
through transformation.
10
39260
2000
์‘์šฉ๋˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
00:41
So let's take a look at the brain.
11
41260
2000
์ž ์ด์ œ ๋‡Œ๋ฅผ ๊ด€์ฐฐํ•ด ๋ด…์‹œ๋‹ค.
00:43
Most people, when they first look at a fresh human brain,
12
43260
3000
๋Œ€๋ถ€๋ถ„์˜ ์‚ฌ๋žŒ๋“ค์€ ์ธ๊ฐ„์˜ ๋‡Œ๋ฅผ ์‹ค์ œ๋กœ ๋ณด๊ณ ๋‚˜์„œ
00:46
they say, "It doesn't look what you're typically looking at
13
46260
3000
์ด๋ ‡๊ฒŒ ๋งํ•ฉ๋‹ˆ๋‹ค, "์ด์ „์— ๋ณด์•˜๋˜ ๋‘๋‡Œ์™€๋Š”
00:49
when someone shows you a brain."
14
49260
2000
๋ญ”๊ฐ€ ๋‹ฌ๋ผ ๋ณด์—ฌ์š”."
00:51
Typically, what you're looking at is a fixed brain. It's gray.
15
51260
3000
๋ณดํ†ต, ์—ฌ๋Ÿฌ๋ถ„์ด ๋ณด์•„ ์™”๋˜ ๋‘๋‡Œ๋Š” ๋ฉˆ์ถฐ ์žˆ๋Š” ๋‘๋‡Œ์ž…๋‹ˆ๋‹ค. ํšŒ์ƒ‰์ด์ฃ .
00:54
And this outer layer, this is the vasculature,
16
54260
2000
๊ทธ๋ฆฌ๊ณ  ์ด ๋ฐ”๊นฅ๋ง‰์€, ๋‘๋‡Œ๋ฅผ ๋‘˜๋Ÿฌ์‹ธ๊ณ  ์žˆ๋Š”
00:56
which is incredible, around a human brain.
17
56260
2000
๊ฒƒ์€ ๋งฅ๊ด€๊ตฌ์กฐ๋กœ์„œ ๋Œ€๋‹จํ•œ ์กด์žฌ์ž…๋‹ˆ๋‹ค.
00:58
This is the blood vessels.
18
58260
2000
์ด๊ฒƒ์€ ํ˜ˆ๊ด€๋“ค ์ž…๋‹ˆ๋‹ค.
01:00
20 percent of the oxygen
19
60260
3000
์—ฌ๋Ÿฌ๋ถ„์˜ ํ์—์„œ ๋‚˜์˜จ
01:03
coming from your lungs,
20
63260
2000
20%์˜ ์‚ฐ์†Œ์™€
01:05
20 percent of the blood pumped from your heart,
21
65260
2000
์—ฌ๋Ÿฌ๋ถ„์˜ ์‹ฌ์žฅ์—์„œ ๋งŒ๋“ค์–ด๋‚ธ 20%์˜ ํ˜ˆ์•ก์ด
01:07
is servicing this one organ.
22
67260
2000
์ด ์žฅ๊ธฐ ํ•œ๊ฐœ๋ฅผ ์ง€์›ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
01:09
That's basically, if you hold two fists together,
23
69260
2000
์‰ฝ๊ฒŒ๋งํ•ด, ๋‘ ์ฃผ๋จน์„ ์ฅ์–ด๋ณด์‹œ๋ฉด
01:11
it's just slightly larger than the two fists.
24
71260
2000
๋‡Œ๋Š” ๊ทธ ๋‘ ์ฃผ๋จน๋ณด๋‹ค ์•ฝ๊ฐ„ ๋” ํฐ ํฌ๊ธฐ์ž…๋‹ˆ๋‹ค.
01:13
Scientists, sort of at the end of the 20th century,
25
73260
3000
20์„ธ๊ธฐ ๋ง๊ฒฝ ๊ณผํ•™์ž๋“ค์€
01:16
learned that they could track blood flow
26
76260
2000
ํ˜ˆ์•ก์˜ ํ๋ฆ„์„ ๋น„์นจ๋ฒ”์ ์œผ๋กœ ์ถ”์ ํ•˜์—ฌ
01:18
to map non-invasively
27
78260
3000
์—ฌ๋Ÿฌ ํ™œ๋™๋“ค์ด ์ด๋ค„์ง€๊ณ  ์žˆ๋Š” ๋‘๋‡Œ ๋‚ด๋ถ€์˜ ์ง€๋„๋ฅผ
01:21
where activity was going on in the human brain.
28
81260
3000
๊ทธ๋ฆด ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ฐœ๊ฒฌ ํ•˜์˜€์Šต๋‹ˆ๋‹ค.
01:24
So for example, they can see in the back part of the brain,
29
84260
3000
์˜ˆ๋ฅผ ๋“ค์–ด, ๊ทธ๋“ค์€ ๋‘๋‡Œ์˜ ๋’ท ๋ถ€๋ถ„์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ
01:27
which is just turning around there.
30
87260
2000
๋ฐ”๋กœ ์ด ๋ถ€๋ถ„์ด์ฃ . ์—ฌ๋Ÿฌ๋ถ„์ด ๋˜‘๋ฐ”๋กœ ์„œ ์žˆ์„์ˆ˜ ์žˆ๊ฒŒ
01:29
There's the cerebellum; that's keeping you upright right now.
31
89260
2000
๋„์™€์ฃผ๋Š” ์ด๊ฒƒ์€ ์†Œ๋‡Œ์ด๋ฉฐ
01:31
It's keeping me standing. It's involved in coordinated movement.
32
91260
3000
์กฐํ™”๋กœ์šด ๋™์ž‘์„ ํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.
01:34
On the side here, this is temporal cortex.
33
94260
3000
์ด ์˜†๋ถ€๋ถ„์€ ์ธก๋‘์—ฝ ํ”ผ์งˆ ์ž…๋‹ˆ๋‹ค.
01:37
This is the area where primary auditory processing --
34
97260
3000
์—ฌ๊ธฐ์„œ ์ผ์ฐจ ์ฒญ๊ฐ ์ฒ˜๋ฆฌ๊ฐ€ ์ด๋ค„์ง€๊ฒŒ ๋˜๋ฉฐ
01:40
so you're hearing my words,
35
100260
2000
์—ฌ๋Ÿฌ๋ถ„๋“ค์ด ์ œ ๋ง์„ ๋“ฃ๊ณ  ๋‚œ ๋’ค, ๊ทธ๊ฒƒ์„
01:42
you're sending it up into higher language processing centers.
36
102260
2000
๋” ๋†’์€ ์ˆ˜์ค€์˜ ์ค‘์•™ ์–ธ์–ด์ฒ˜๋ฆฌ ์žฅ์น˜๋กœ ๋ณด๋‚ด๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค.
01:44
Towards the front of the brain
37
104260
2000
๋‘๋‡Œ์˜ ์•ž ๋ฐฉํ–ฅ์—์„œ๋Š”
01:46
is the place in which all of the more complex thought, decision making --
38
106260
3000
๋” ๋ณต์žกํ•œ ์ƒ๊ฐ๊ณผ ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ๋ถ€๋ถ„์ด
01:49
it's the last to mature in late adulthood.
39
109260
4000
์œ„์น˜ํ•˜๋ฉฐ ์„ฑ์ธ๊ธฐ ๋ง์— ๋งˆ์ง€๋ง‰์œผ๋กœ ์„ฑ์ˆ™ํ•ด์ง€๋Š” ๋ถ€๋ถ„ ์ž…๋‹ˆ๋‹ค.
01:53
This is where all your decision-making processes are going on.
40
113260
3000
๋ชจ๋“  ์˜์‚ฌ ๊ฒฐ์ •์„ ์—ฌ๊ธฐ์„œ ๋‚ด๋ฆฝ๋‹ˆ๋‹ค.
01:56
It's the place where you're deciding right now
41
116260
2000
ํ˜„์žฌ ์—ฌ๋Ÿฌ๋ถ„์ด ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๊ณ  ์žˆ๋Š” ๋ถ€๋ถ„์ด๋ฉฐ
01:58
you probably aren't going to order the steak for dinner.
42
118260
3000
์˜ค๋Š˜ ์ €๋…์—” ์Šคํ…Œ์ดํฌ๋ฅผ ๋จน์ง€ ๋ง์•„์•ผ์ง€ ๋ผ๋Š” ๊ฒฐ์ •๋„ ๋‚ด๋ฆฝ๋‹ˆ๋‹ค.
02:01
So if you take a deeper look at the brain,
43
121260
2000
์ž ์ด์ œ ๋” ๊นŠ๊ฒŒ ๋‘๋‡Œ๋ฅผ ๋“ค์—ฌ๋‹ค ๋ณด๋ฉด
02:03
one of the things, if you look at it in cross-section,
44
123260
2000
์•„์‹œ๊ฒ ์ง€๋งŒ, ๋‘๋‡Œ์˜ ๋‹จ๋ฉด์„ ํ†ตํ•˜์—ฌ
02:05
what you can see
45
125260
2000
์ œ๋Œ€๋กœ ๋œ ๋‘๋‡Œ์˜ ๊ตฌ์กฐ๋ฅผ
02:07
is that you can't really see a whole lot of structure there.
46
127260
3000
์•Œ์•„๋ณด๊ธฐ๊ฐ€ ์‰ฝ์ง€ ์•Š๋‹ค๋Š” ๊ฒƒ ์ž…๋‹ˆ๋‹ค.
02:10
But there's actually a lot of structure there.
47
130260
2000
์‚ฌ์‹ค, ์ด๊ณณ์—๋Š” ๋งŽ์€ ์กฐ์ง๋“ค์ด ์กด์žฌํ•˜๊ณ  ์žˆ์œผ๋ฉฐ
02:12
It's cells and it's wires all wired together.
48
132260
2000
์„ธํฌ๋“ค๊ณผ ์„ ๋“ค์ด ๋‹ค๊ฐ™์ด ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
02:14
So about a hundred years ago,
49
134260
2000
๊ทธ๋ž˜์„œ ์•ฝ 100๋…„ ์ „,
02:16
some scientists invented a stain that would stain cells.
50
136260
2000
๋ช‡๋ช‡ ๊ณผํ•™์ž๋“ค์€ ์„ธํฌ ์—ผ์ƒ‰์ œ๋ฅผ ๊ฐœ๋ฐœํ–ˆ์Šต๋‹ˆ๋‹ค.
02:18
And that's shown here in the the very light blue.
51
138260
3000
๋ณด์‹œ๋Š” ๋ฐ์€ ํŒŒ๋ž€์ƒ‰์ด ๊ทธ๊ฒƒ์ž…๋‹ˆ๋‹ค.
02:21
You can see areas
52
141260
2000
์ •์ƒ์ ์ธ ์„ธํฌ๊ธฐ๊ด€๋“ค์ด ์—ผ์ƒ‰๋˜๋Š”
02:23
where neuronal cell bodies are being stained.
53
143260
2000
๋ถ€๋ถ„๋“ค์„ ์ง€๊ธˆ ๋ณด์‹œ๊ณ  ๊ณ„์‹ญ๋‹ˆ๋‹ค.
02:25
And what you can see is it's very non-uniform. You see a lot more structure there.
54
145260
3000
๋งค์šฐ ์ผ๊ด€์„ฑ์ด ์—†์–ด ๋ณด์ด์ฃ . ํ›จ์”ฌ ๋” ๋งŽ์€ ์กฐ์ง์ด ๋ณด์ž…๋‹ˆ๋‹ค.
02:28
So the outer part of that brain
55
148260
2000
์ง€๊ธˆ ๋ณด์‹œ๋Š” ๋‘๋‡Œ์˜ ๋ฐ”๊นฅ๋ถ€๋ถ„์€
02:30
is the neocortex.
56
150260
2000
์‹ ํ”ผ์งˆ ์ด๋ผ๊ณ  ํ•ฉ๋‹ˆ๋‹ค.
02:32
It's one continuous processing unit, if you will.
57
152260
3000
์ง€์†์ ์ธ ์ž‘์—…์„ ์ˆ˜ํ–‰ํ•˜๋Š” ๋‹จ์ผ์ฒด๋ผ๊ณ  ์ƒ๊ฐํ•˜์‹œ๋ฉด ๋ฉ๋‹ˆ๋‹ค.
02:35
But you can also see things underneath there as well.
58
155260
2000
ํ•˜์ง€๋งŒ ๊ทธ ์•„๋ž˜์— ์žˆ๋Š” ๊ฒƒ๋“ค๋„ ๊ฐ™์ด ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
02:37
And all of these blank areas
59
157260
2000
๊ทธ๋ฆฌ๊ณ  ์ด ๋น„์–ด์žˆ๋Š” ์˜์—ญ๋“ค ์—ญ์‹œ
02:39
are the areas in which the wires are running through.
60
159260
2000
๋งŽ์€ ๊ฒƒ๋“ค์ด ์—ฐ๊ฒฐ๋˜์–ด ์žˆ๋Š” ์˜์—ญ๋“ค ์ž…๋‹ˆ๋‹ค.
02:41
They're probably less cell dense.
61
161260
2000
์„ธํฌ ๋ฐ€๋„๊ฐ€ ์ข€ ๋” ๋‚ฎ๊ฒ ์ฃ .
02:43
So there's about 86 billion neurons in our brain.
62
163260
4000
์ธ๊ฐ„์˜ ๋‡Œ์—๋Š” ์•ฝ 860์–ต๊ฐœ์˜ ๋‰ด๋Ÿฐ์ด ์žˆ์Šต๋‹ˆ๋‹ค.
02:47
And as you can see, they're very non-uniformly distributed.
63
167260
3000
๋ณด์‹œ๋‹ค์‹œํ”ผ, ๋งค์šฐ ๋น„๊ท ์ผ์ ์œผ๋กœ ๋ถ„ํฌ๋˜์–ด ์žˆ์œผ๋ฉฐ
02:50
And how they're distributed really contributes
64
170260
2000
์–ด๋–ป๊ฒŒ ๋ถ„ํฌ๋˜์–ด ์žˆ๋Š”์ง€๊ฐ€ ๊ทธ๋“ค์˜
02:52
to their underlying function.
65
172260
2000
๊ธฐ๋ณธ์ ์ธ ๊ธฐ๋Šฅ์— ์ค‘์š”ํ•œ ๊ธฐ์—ฌ๋ฅผ ํ•ฉ๋‹ˆ๋‹ค.
02:54
And of course, as I mentioned before,
66
174260
2000
๊ทธ๋ฆฌ๊ณ  ๋ฌผ๋ก , ์ œ๊ฐ€ ๋งํ–ˆ๋‹ค์‹œํ”ผ,
02:56
since we can now start to map brain function,
67
176260
3000
๋‡Œ ๊ธฐ๋Šฅ์„ ์ง€๋„๋กœ ๊ทธ๋ ค๋‚ด๋Š” ๊ฒƒ์ด ๊ฐ€๋Šฅํ•˜๊ธฐ์—,
02:59
we can start to tie these into the individual cells.
68
179260
3000
๊ฐ๊ฐ์˜ ์„ธํฌ์— ์—ฐ๊ด€์ง€์–ด ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
03:02
So let's take a deeper look.
69
182260
2000
์ž ๊ทธ๋Ÿผ ํ•œ๋‹จ๊ณ„ ๋” ๋“ค์–ด๊ฐ€ ๋ณผ๊นŒ์š”.
03:04
Let's look at neurons.
70
184260
2000
๋‰ด๋Ÿฐ์„ ์‚ดํŽด๋ด…์‹œ๋‹ค.
03:06
So as I mentioned, there are 86 billion neurons.
71
186260
2000
๋ง์”€๋“œ๋ ธ๋‹ค์‹œํ”ผ, 860์–ต๊ฐœ์˜ ๋‰ด๋Ÿฐ์ด ์กด์žฌํ•˜๊ณ  ์žˆ์œผ๋ฉฐ
03:08
There are also these smaller cells as you'll see.
72
188260
2000
๊ทธ๋ณด๋‹ค ๋” ์ž‘์€ ์„ธํฌ๋“ค๋„ ์—ฌ๋Ÿฌ๋ถ„์€ ๋ณด์‹œ๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค.
03:10
These are support cells -- astrocytes glia.
73
190260
2000
์•„๊ต์„ธํฌ ๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ์ง€์›์„ธํฌ๋“ค ์ž…๋‹ˆ๋‹ค.
03:12
And the nerves themselves
74
192260
3000
๊ทธ๋ฆฌ๊ณ  ์‹ ๊ฒฝ๋“ค ์ž์ฒด๊ฐ€
03:15
are the ones who are receiving input.
75
195260
2000
์ž…๋ ฅ์„ ๋ฐ›๊ณ  ์žˆ๋Š” ์ค‘์ด๋ฉฐ
03:17
They're storing it, they're processing it.
76
197260
2000
๋ณด๊ด€ํ•˜๊ณ , ์ฒ˜๋ฆฌํ•˜๋Š” ์ž‘์—…๋„ ํ•ฉ๋‹ˆ๋‹ค.
03:19
Each neuron is connected via synapses
77
199260
4000
๊ฐ ๋‰ด๋Ÿฐ์€ ์—ฐ์ ‘์„ ํ†ตํ•ด ๋‘๋‡Œ์˜
03:23
to up to 10,000 other neurons in your brain.
78
203260
3000
์ตœ๋Œ€ 1๋งŒ๊ฐœ ๊นŒ์ง€์˜ ๋‹ค๋ฅธ ๋‰ด๋Ÿฐ๋“ค๊ณผ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
03:26
And each neuron itself
79
206260
2000
๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ฐ์˜ ๋‰ด๋Ÿฐ์€
03:28
is largely unique.
80
208260
2000
๋งค์šฐ ํŠน์ดํ•ฉ๋‹ˆ๋‹ค.
03:30
The unique character of both individual neurons
81
210260
2000
๊ฐ๊ฐ์˜ ๋‰ด๋Ÿฐ๋“ค๊ณผ ๋‘๋‡Œ ์•ˆ์—
03:32
and neurons within a collection of the brain
82
212260
2000
์œ„์น˜ํ•œ ๋‰ด๋Ÿฐ์ง‘ํ•ฉ์˜ ํŠน์„ฑ์€
03:34
are driven by fundamental properties
83
214260
3000
์ƒํ™”ํ•™ ๊ณ ์œ ์˜ ์„ฑ์งˆ์— ๋”ฐ๋ผ
03:37
of their underlying biochemistry.
84
217260
2000
ํ–‰๋™ํ•˜๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
03:39
These are proteins.
85
219260
2000
์ด๊ฒƒ๋“ค์€ ๋‹จ๋ฐฑ์งˆ ์ž…๋‹ˆ๋‹ค.
03:41
They're proteins that are controlling things like ion channel movement.
86
221260
3000
์ด ๋‹จ๋ฐฑ์งˆ๋“ค์€ ์ด์˜จ ์ฑ„๋„์˜ ์›€์ง์ž„์„ ํ†ต์ œํ•˜๋ฉฐ
03:44
They're controlling who nervous system cells partner up with.
87
224260
4000
์‹ ๊ฒฝ์‹œ์Šคํ…œ ์„ธํฌ์™€ ๋ˆ„๊ฐ€ ๊ต์‹ ์„
03:48
And they're controlling
88
228260
2000
์ด๋ฃจ๋Š”๊ฐ€๋ฅผ ํ†ต์ œํ•จ๊ณผ ๋”๋ถˆ์–ด
03:50
basically everything that the nervous system has to do.
89
230260
2000
๋‹ค๋ฅธ ๋ชจ๋“  ์‹ ๊ฒฝ ์‹œ์Šคํ…œ์ด ํ•˜๋Š”์ผ์„ ๊ด€์žฅํ•ฉ๋‹ˆ๋‹ค.
03:52
So if we zoom in to an even deeper level,
90
232260
3000
์ž ์—ฌ๊ธฐ์„œ ํ•œ๋‹จ๊ณ„ ๋” ๊นŠ์€ ๋ ˆ๋ฒจ๋กœ ํ™•๋Œ€ํ•ด์„œ ๋ณด๋ฉด,
03:55
all of those proteins
91
235260
2000
์ด ๋ชจ๋“  ๋‹จ๋ฐฑ์งˆ์ด
03:57
are encoded by our genomes.
92
237260
2000
๊ฐ์ž์˜ ๊ฒŒ๋†ˆ์— ์˜ํ•˜์—ฌ ์•”ํ˜ธํ™” ๋ฉ๋‹ˆ๋‹ค.
03:59
We each have 23 pairs of chromosomes.
93
239260
3000
์—ฌ๋Ÿฌ๋ถ„์€ ๊ฐ๊ฐ 23์Œ์˜ ์—ผ์ƒ‰์ฒด๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
04:02
We get one from mom, one from dad.
94
242260
2000
์šฐ๋ฆฌ๋Š” ํ•œ๊ฐœ๋Š” ์–ด๋จธ๋‹ˆ๋กœ๋ถ€ํ„ฐ, ํ•œ๊ฐœ๋Š” ์•„๋ฒ„์ง€๋กœ๋ถ€ํ„ฐ ๋ฐ›์Šต๋‹ˆ๋‹ค.
04:04
And on these chromosomes
95
244260
2000
๊ทธ๋ฆฌ๊ณ  ์ด ์—ผ์ƒ‰์ฒด์—๋Š”
04:06
are roughly 25,000 genes.
96
246260
2000
2๋งŒ 5์ฒœ๊ฐœ์˜ ์œ ์ „์ž๊ฐ€ ์กด์žฌํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
04:08
They're encoded in the DNA.
97
248260
2000
DNA ์†์— ์ธ์‹๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
04:10
And the nature of a given cell
98
250260
3000
๊ทธ๋ฆฌ๊ณ  ๊ธฐ์ดˆ ์ƒํ™”ํ•™
04:13
driving its underlying biochemistry
99
253260
2000
์ž‘์šฉ์„ ์ด๋„๋Š” ์„ธํฌ์˜ ๋ณธ์„ฑ์€
04:15
is dictated by which of these 25,000 genes
100
255260
3000
2๋งŒ 5์ฒœ๊ฐœ์˜ ์œ ์ „์ž์ค‘ ์–ด๋–ค ์œ ์ „์ž๊ฐ€
04:18
are turned on
101
258260
2000
๋ฐ˜์‘ํ•˜๋Š”๊ฐ€์— ๋”ฐ๋ผ
04:20
and at what level they're turned on.
102
260260
2000
๊ทธ๋ฆฌ๊ณ  ์–ด๋–ค ๋ ˆ๋ฒจ์—์„œ ๋ฐ˜์‘ํ•˜๋Š”์ง€์— ๋”ฐ๋ผ ์ •ํ•ด์ง€๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
04:22
And so our project
103
262260
2000
๊ทธ๋ž˜์„œ ์šฐ๋ฆฌ์˜ ํ”„๋กœ์ ํŠธ๋Š”
04:24
is seeking to look at this readout,
104
264260
3000
์ด๋Ÿฐ 2๋งŒ 5์ฒœ๊ฐœ์˜ ์œ ์ „์ž์ค‘ ์–ด๋–ค ์œ ์ „์ž๊ฐ€ ๋ฐ˜์‘ํ•˜๋Š”์ง€๋ฅผ
04:27
understanding which of these 25,000 genes is turned on.
105
267260
3000
์ž๋ฃŒํ•ด๋…์„ ํ†ตํ•˜์—ฌ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์— ์žˆ์Šต๋‹ˆ๋‹ค.
04:30
So in order to undertake such a project,
106
270260
3000
์ด๋Ÿฐ ํ”„๋กœ์ ํŠธ์— ์ฐฉ์ˆ˜ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š”,
04:33
we obviously need brains.
107
273260
3000
๋ฌผ๋ก  ๋˜‘๋˜‘ํ•œ ๋‘๋‡Œ๋“ค์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.
04:36
So we sent our lab technician out.
108
276260
3000
๊ทธ๋ฆฌ์„œ ์šฐ๋ฆฌ์˜ ์—ฐ๊ตฌ์›์„ ๋ณด๋ƒˆ์Šต๋‹ˆ๋‹ค.
04:39
We were seeking normal human brains.
109
279260
2000
์šฐ๋ฆฌ๋Š” ํ‰๋ฒ”ํ•œ ์ธ๊ฐ„์˜ ๋‡Œ๋ฅผ ์ฐพ๊ณ  ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค.
04:41
What we actually start with
110
281260
2000
์šฐ๋ฆฌ๊ฐ€ ์‹ค์ œ๋กœ ์‹œ์ž‘ํ•œ ๊ณณ์€
04:43
is a medical examiner's office.
111
283260
2000
ํ•œ ๋ถ€๊ฒ€์†Œ ์˜€์Šต๋‹ˆ๋‹ค.
04:45
This a place where the dead are brought in.
112
285260
2000
์ด๊ณณ์€ ์‹œ์ฒด๊ฐ€ ๋ชจ์ด๋Š” ์žฅ์†Œ์ž…๋‹ˆ๋‹ค.
04:47
We are seeking normal human brains.
113
287260
2000
์šฐ๋ฆฌ๋Š” ํ‰๋ฒ”ํ•œ ์ธ๊ฐ„ ๋‡Œ๋“ค์„ ์ฐพ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
04:49
There's a lot of criteria by which we're selecting these brains.
114
289260
3000
ํ‰๋ฒ”ํ•œ ๋‡Œ๋ž€ ๋งŽ์€ ๊ธฐ์ค€์— ์˜ํ•˜์—ฌ ์ •ํ•ด์ง‘๋‹ˆ๋‹ค.
04:52
We want to make sure
115
292260
2000
์šฐ๋ฆฌ๊ฐ€ ํ™•์‹คํžˆ ํ•˜๋ ค๋Š” ๊ฒƒ์€
04:54
that we have normal humans between the ages of 20 to 60,
116
294260
3000
20์„ธ๋ถ€ํ„ฐ 60์„ธ ์‚ฌ์ด์˜
04:57
they died a somewhat natural death
117
297260
2000
์ž์—ฐ์‚ฌ๋กœ ์ˆจ์กŒ์œผ๋ฉฐ
04:59
with no injury to the brain,
118
299260
2000
๋‡Œ์— ์ถฉ๊ฒฉ์„ ๋ฐ›์ง€ ์•Š์•˜์œผ๋ฉฐ
05:01
no history of psychiatric disease,
119
301260
2000
์ •์‹ ๋ณ‘๋ ฅ๋„ ์—†์œผ๋ฉฐ
05:03
no drugs on board --
120
303260
2000
์•ฝ๋ฌผ ๋ณต์šฉ์ค‘๋„ ์•„๋‹Œ ์ƒํƒœ์—์„œ ์‚ฌ๋งํ•œ ๊ฒฝ์šฐ์ด๋ฉฐ
05:05
we do a toxicology workup.
121
305260
2000
๋…๊ทน๋ฌผ์˜ ์กด์žฌ๋„ ํ™•์ธํ•ฉ๋‹ˆ๋‹ค.
05:07
And we're very careful
122
307260
2000
๊ทธ๋ฆฌ๊ณ  ์ €ํฌ๋Š” ๋งค์šฐ ์กฐ์‹ฌ์Šค๋Ÿฝ๊ฒŒ
05:09
about the brains that we do take.
123
309260
2000
์ด ๋‘๋‡Œ๋“ค์„ ๋‹ค๋ฃจ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
05:11
We're also selecting for brains
124
311260
2000
ํ•œํŽธ, ๋‡Œ ์กฐ์ง์„ ์–ป์„์ˆ˜ ์žˆ๋Š”
05:13
in which we can get the tissue,
125
313260
2000
๋‘๋‡Œ๋“ค์„ ๊ณจ๋ผ๋‚ด๊ณ  ์žˆ์œผ๋ฉฐ
05:15
we can get consent to take the tissue
126
315260
2000
์‚ฌํ›„ 24์‹œ๊ฐ„ ์•ˆ์— ๋‡Œ ์กฐ์ง์„ ์ถ”์ถœํ• ์ˆ˜ ์žˆ๋Š”
05:17
within 24 hours of time of death.
127
317260
2000
ํ—ˆ๊ฐ€๋ฅผ ๋ฐ›์€ ํ›„ ์ถ”์ถœ์ด ์ด๋ค„์ง‘๋‹ˆ๋‹ค.
05:19
Because what we're trying to measure, the RNA --
128
319260
3000
์œ ์ „์ž์— ์ €์žฅ๋˜์–ด์žˆ๋Š” ๋งค์šฐ ๋ถˆ์•ˆ์ •ํ•œ
05:22
which is the readout from our genes --
129
322260
2000
RNA (Ribonucleic Acid: ๋ฆฌ๋ณดํ•ต์‚ฐ) ์„
05:24
is very labile,
130
324260
2000
์ธก์ •ํ•ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์—
05:26
and so we have to move very quickly.
131
326260
2000
๋น ๋ฅธ ์ž‘์—…์ˆ˜ํ–‰์ด ์ด๋ค„์ ธ์•ผ ํ•ฉ๋‹ˆ๋‹ค.
05:28
One side note on the collection of brains:
132
328260
3000
ํ•œ๊ฐ€์ง€ ๋‘๋‡Œ๋“ค์˜ ๋ชจ์Œ์— ๊ด€ํ•˜์—ฌ ์ด์•ผ๊ธฐ ๋ง๋ถ™์ด์ž๋ฉด,
05:31
because of the way that we collect,
133
331260
2000
์ €ํฌ๊ฐ€ ์ˆ˜์ง‘ํ•˜๋Š” ๋ฐฉ์‹์ด
05:33
and because we require consent,
134
333260
2000
๋ฒ•์ ์ธ ๋™์˜๋ฅผ ์š”๊ตฌํ•˜๊ธฐ ๋•Œ๋ฌธ์—,
05:35
we actually have a lot more male brains than female brains.
135
335260
3000
์—ฌ์„ฑ๋“ค์˜ ๋‘๋‡Œ๋ณด๋‹ค ์•„์ฃผ ๋งŽ์€ ๋‚จ์„ฑ๋“ค์˜ ๋‘๋‡Œ๋ฅผ ๋ณด์œ ํ•˜๊ฒŒ ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.
05:38
Males are much more likely to die an accidental death in the prime of their life.
136
338260
3000
๋‚จ์„ฑ๋“ค์€ ์—ฌ์„ฑ๋ณด๋‹ค ์ธ์ƒ์˜ ํ™ฉ๊ธˆ๊ธฐ๋•Œ ์‚ฌ๊ณ ์‚ฌ๋กœ ์ˆจ์งˆ ํ™•๋ฅ ์ด ํ›จ์”ฌ ๋” ๋†’๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค.
05:41
And men are much more likely
137
341260
2000
๊ทธ๋ฆฌ๊ณ  ๋‚จ์ž๋“ค์€ ์—ฌ์ž๋“ค๋ณด๋‹ค
05:43
to have their significant other, spouse, give consent
138
343260
3000
์ž์‹ ์˜ ๋ฐ˜๋ ค์ž๋‚˜, ๋ฐฐ์šฐ์ž์—๊ฒŒ ์ž์‹ ์˜ ๋‘๋‡Œ๊ธฐ์ฆ ํ—ˆ๊ฐ€๋ฅผ ํ•  ํ™•๋ฅ ์ด
05:46
than the other way around.
139
346260
2000
์›”๋“ฑํžˆ ๋†’๊ธฐ ๋•Œ๋ฌธ์ž…๋‹ˆ๋‹ค.
05:48
(Laughter)
140
348260
4000
(์›ƒ์Œ)
05:52
So the first thing that we do at the site of collection
141
352260
2000
๋‘๋‡Œ์ˆ˜์ง‘์˜ ์ฒซ ๋‹จ๊ณ„๋Š”
05:54
is we collect what's called an MR.
142
354260
2000
Magnetic Resonance (์ž๊ธฐ๊ณต๋ช…) ์„ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค.
05:56
This is magnetic resonance imaging -- MRI.
143
356260
2000
์ด๊ฒƒ์€ MRI (Magnetic Resonance Imaging: ์ž๊ธฐ๊ณต๋ช…์˜์ƒ) ์ž…๋‹ˆ๋‹ค.
05:58
It's a standard template by which we're going to hang the rest of this data.
144
358260
3000
์ด๊ฒƒ์€ ์šฐ๋ฆฌ๊ฐ€ ์•ž์œผ๋กœ ์ด ์ž๋ฃŒ๋“ค์„ ์˜ฌ๋ ค ๋†“์„ ํ‘œ์ค€ ๊ฒฌ๋ณธ์ž…๋‹ˆ๋‹ค.
06:01
So we collect this MR.
145
361260
2000
์ด๋ ‡๊ฒŒ ์ž๊ธฐ๊ณต๋ช… ์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•ฉ๋‹ˆ๋‹ค.
06:03
And you can think of this as our satellite view for our map.
146
363260
2000
๋งˆ์น˜ ์œ„์„ฑ์—์„œ ๋ณด๋Š” ์ง€๋„์™€ ๊ฐ™์€ ๋งฅ๋ฝ์ž…๋‹ˆ๋‹ค.
06:05
The next thing we do
147
365260
2000
๋‹ค์Œ์œผ๋กœ ์šฐ๋ฆฌ๊ฐ€ ์ˆ˜์ง‘ํ•˜๋Š” ๊ฒƒ์€
06:07
is we collect what's called a diffusion tensor imaging.
148
367260
3000
Diffusion Tensor Imaging (DTI :ํ™•์‚ฐํ…์„œ์˜์ƒ) ์ด๋ฉฐ
06:10
This maps the large cabling in the brain.
149
370260
2000
๋‘๋‡Œ์˜ ํฐ ์—ฐ๊ฒฐ์„ ๋“ค์„ ๋ณด์—ฌ์ค๋‹ˆ๋‹ค.
06:12
And again, you can think of this
150
372260
2000
๊ทธ๋ฆฌ๊ณ , ์ด๊ฒƒ์„ ๋ฏธ๊ตญ์˜ ์ฃผ์™€ ์ฃผ ์‚ฌ์ด๋ฅผ ์ž‡๋Š” ๊ณ ์†๋„๋กœ์—
06:14
as almost mapping our interstate highways, if you will.
151
374260
2000
๋น—๋Œ€์–ด ์ƒ์ƒํ•ด ๋ณด์‹ ๋‹ค๋ฉด ์•„๋งˆ ์ดํ•ด๊ฐ€ ๋น ๋ฅด์‹ค ๊ฒ๋‹ˆ๋‹ค.
06:16
The brain is removed from the skull,
152
376260
2000
๋‘๋‡Œ๋Š” ๋‘๊ฐœ๊ณจ์—์„œ ๋ถ„๋ฆฌ๋˜์–ด
06:18
and then it's sliced into one-centimeter slices.
153
378260
3000
1์„ผํ‹ฐ๋ฏธํ„ฐ ๋‘๊ป˜์˜ ๋‹จ๋ฉด๋“ค๋กœ ์ž˜๋ฆฝ๋‹ˆ๋‹ค.
06:21
And those are frozen solid,
154
381260
2000
๊ทธ๋ฆฌ๊ณ  ๋”ฑ๋”ฑํ•˜๊ฒŒ ์–ผ๋ ค์ง€๊ณ  ๋‚œ ๋’ค,
06:23
and they're shipped to Seattle.
155
383260
2000
์‹œ์• ํ‹€๋กœ ๋ฐฐ์†ก๋˜์–ด์ง‘๋‹ˆ๋‹ค.
06:25
And in Seattle, we take these --
156
385260
2000
๊ทธ๋ฆฌ๊ณ  ์‹œ์• ํ‹€์—์„œ๋Š”, ์ด๊ฒƒ๋“ค์„ ๋ฐ›์•„์„œ --
06:27
this is a whole human hemisphere --
157
387260
2000
๋ณด์‹œ๋Š” ๊ฒƒ์€ ์ธ๊ฐ„ ๋ฐ˜๊ตฌ ์ „์ฒด ์ž…๋‹ˆ๋‹ค --
06:29
and we put them into what's basically a glorified meat slicer.
158
389260
2000
๊ธฐ๋ณธ์ ์œผ๋กœ ๊ณ ๊ธฐ๋ฅผ ์–‡๊ฒŒ ์ฐ์–ด๋‚ด๋Š” ๊ธฐ๊ตฌ์— ์˜ฌ๋ฆฌ๊ณ 
06:31
There's a blade here that's going to cut across
159
391260
2000
์—ฌ๊ธฐ์žˆ๋Š” ์นผ๋‚ ์ด ๋‡Œ ์กฐ์ง์˜
06:33
a section of the tissue
160
393260
2000
ํ•œ ๋ถ€๋ถ„์„ ๊ฐ€๋กœ์งˆ๋Ÿฌ ์ž˜๋ผ ๋‚ด์–ด
06:35
and transfer it to a microscope slide.
161
395260
2000
ํ˜„๋ฏธ๊ฒฝ ์Šฌ๋ผ์ด๋“œ ์œ„๋กœ ์˜ฎ๊น๋‹ˆ๋‹ค.
06:37
We're going to then apply one of those stains to it,
162
397260
2000
๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ๋Š” ์—ผ๋ฃŒ์ค‘ ํ•œ๊ฐ€์ง€ ์ƒ‰์„ ๊ทธ๊ณณ์— ์ž…ํžˆ๊ณ 
06:39
and we scan it.
163
399260
2000
์Šค์บ”์„ ํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค.
06:41
And then what we get is our first mapping.
164
401260
3000
๊ทธ๋ฆฌ๊ณ  ๋‚˜๋ฉด ์ฒซ๋ฒˆ์งธ ์ง€๋„์ œ์ž‘์ด ์™„์„ฑ๋ฉ๋‹ˆ๋‹ค.
06:44
So this is where experts come in
165
404260
2000
์ด์ œ ์ „๋ฌธ๊ฐ€๋“ค์ด ์ฐธ์—ฌํ•˜์—ฌ
06:46
and they make basic anatomic assignments.
166
406260
2000
๊ธฐ๋ณธ์ ์ธ ํ•ด๋ถ€ ๋ช…์นญ์ด ๋ฐฐ์ •๋˜์–ด์ง€๋ฉฐ ์ด๋Ÿฐ
06:48
You could consider this state boundaries, if you will,
167
408260
3000
๊ฝค ๊ด‘๋ฒ”์œ„ํ•œ ์œค๊ณฝ์„ ๋“ค์€ ๋ฏธ๊ตญ์˜ ์ฃผ ์‚ฌ์ด์˜
06:51
those pretty broad outlines.
168
411260
2000
๊ฒฝ๊ณ„์„ ๋“ค ์ด๋ผ๊ณ ๋„ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
06:53
From this, we're able to then fragment that brain into further pieces,
169
413260
4000
์ด๊ฒƒ์„ ํ†ตํ•˜์—ฌ ๋‡Œ๋ฅผ ๋” ์ž‘์€ ์กฐ๊ฐ์œผ๋กœ ๋‚˜๋ˆˆ ๋’ค์—
06:57
which then we can put on a smaller cryostat.
170
417260
2000
๋” ์ž‘์€ ์ €์˜จ์œ ์ง€์žฅ์น˜์— ๋„ฃ์Šต๋‹ˆ๋‹ค.
06:59
And this is just showing this here --
171
419260
2000
๊ทธ๋ฆฌ๊ณ  ์ง€๊ธˆ ๋ณด์‹œ๋‹ค์‹œํ”ผ --
07:01
this frozen tissue, and it's being cut.
172
421260
2000
์ด ์–ผ์–ด๋ถ™์€ ์กฐ์ง์€ ์ž˜๋ฆฌ๊ณ  ์žˆ๋Š” ์ค‘์ž…๋‹ˆ๋‹ค.
07:03
This is 20 microns thin, so this is about a baby hair's width.
173
423260
3000
๋‘๊ป˜๋Š” 20 ๋งˆ์ดํฌ๋ก ์œผ๋กœ ์•„๊ธฐ๋“ค์˜ ๋จธ๋ฆฌ์นด๋ฝ ๋‘๊ป˜ ์ •๋„๋กœ
07:06
And remember, it's frozen.
174
426260
2000
์•„์ง ์–ผ์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
07:08
And so you can see here,
175
428260
2000
์—ฌ๊ธฐ์„œ ๋ณด์‹œ๋‹ค์‹œํ”ผ
07:10
old-fashioned technology of the paintbrush being applied.
176
430260
2000
๋ถ“์„ ์ด์šฉํ•œ ๊ตฌ์‹์ ์ธ ๊ธฐ์ˆ ์˜ ์ž‘์—…์„ ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
07:12
We take a microscope slide.
177
432260
2000
ํ˜„๋ฏธ๊ฒฝ ์Šฌ๋ผ์ด๋“œ๋ฅผ ๊ฐ€์ง€๊ณ  ์™€์„œ
07:14
Then we very carefully melt onto the slide.
178
434260
3000
๋งค์šฐ ์กฐ์‹ฌ์Šค๋Ÿฝ๊ฒŒ ์Šฌ๋ผ์ด๋“œ ์œ„์— ๋…น์ž…๋‹ˆ๋‹ค.
07:17
This will then go onto a robot
179
437260
2000
๊ทธ๋ฆฌ๊ณ  ๋กœ๋ด‡์—๊ฒŒ ๋ณด๋‚ด
07:19
that's going to apply one of those stains to it.
180
439260
3000
๊ทธ๊ณณ์—์„œ ์ƒ‰์„ ์ž…ํžˆ๋Š” ์ž‘์—…์ด ์ง„ํ–‰๋ฉ๋‹ˆ๋‹ค.
07:26
And our anatomists are going to go in and take a deeper look at this.
181
446260
3000
ํ•ด๋ถ€ํ•™์ž๋“ค์€ ์ด๊ฒƒ์„ ๊ฐ€์ง€๊ณ  ๋” ๊นŠ์€ ๊ด€์ฐฐ์„ ํ•ฉ๋‹ˆ๋‹ค.
07:29
So again this is what they can see under the microscope.
182
449260
2000
ํ˜„๋ฏธ๊ฒฝ์œผ๋กœ ๋ณด๋ฉด ์–ด๋ ‡๊ฒŒ ๋ณด์ž…๋‹ˆ๋‹ค.
07:31
You can see collections and configurations
183
451260
2000
ํฌ๊ณ  ์ž‘์€ ์„ธํฌ๋“ค์˜ ๋ชจ์Œ๊ณผ ๊ตฌ์„ฑ์ด
07:33
of large and small cells
184
453260
2000
์—ฌ๋Ÿฌ ์ง€์—ญ์— ๋ญ‰์ณ์žˆ๋Š” ๊ฒƒ์„
07:35
in clusters and various places.
185
455260
2000
๋ณด์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
07:37
And from there it's routine. They understand where to make these assignments.
186
457260
2000
๊ทธ๋ฆฌ๊ณ  ์—ฌ๊ธฐ์„œ๋ถ€ํ„ฐ๋Š” ๊ณ„์† ๋ฐ˜๋ณต์ž…๋‹ˆ๋‹ค. ํ•ด๋ถ€ํ•™์ž๋“ค์€ ์–ด๋””์— ๋ฌด์—‡์„
07:39
And they can make basically what's a reference atlas.
187
459260
3000
๋ฐฐ์น˜ํ•ด์•ผ ํ• ์ง€ ์•Œ๊ณ  ์žˆ์œผ๋ฉฐ ์ด๋ ‡๊ฒŒ ๋‘๋‡Œ ์ง€๋„์ฑ…์ด ์™„์„ฑ๋ฉ๋‹ˆ๋‹ค.
07:42
This is a more detailed map.
188
462260
2000
์ด๊ฒƒ์€ ๋”์šฑ ์ž์„ธํ•œ ์ง€๋„์ž…๋‹ˆ๋‹ค.
07:44
Our scientists then use this
189
464260
2000
์ €ํฌ ๊ณผํ•™์ž๋“ค์€ ์ด๊ฒƒ์„ ์ด์šฉํ•˜์—ฌ
07:46
to go back to another piece of that tissue
190
466260
3000
๊ทธ ์กฐ์ง์˜ ๋‹ค๋ฅธ ์กฐ๊ฐ์œผ๋กœ ๋Œ์•„๊ฐ€์„œ
07:49
and do what's called laser scanning microdissection.
191
469260
2000
๋ ˆ์ด์ € ์Šค์บ” ํ˜„๋ฏธํ•ด๋ถ€ ์ž‘์—…์„ ํ•ฉ๋‹ˆ๋‹ค.
07:51
So the technician takes the instructions.
192
471260
3000
์ง€์‹œ๋ฅผ ๋ฐ›์€ ํ›„, ๊ธฐ์ˆ ์ž๊ฐ€
07:54
They scribe along a place there.
193
474260
2000
ํ•œ ์ง€์—ญ์„ ํ‘œ์‹œํ•˜๊ฒŒ ๋˜๋ฉด
07:56
And then the laser actually cuts.
194
476260
2000
์‹ค์ œ๋กœ ๊ทธ ๋ถ€๋ถ„์ด ๋ ˆ์ด์ €๋กœ ์ž˜๋ฆฝ๋‹ˆ๋‹ค
07:58
You can see that blue dot there cutting. And that tissue falls off.
195
478260
3000
์ €๊ธฐ ํŒŒ๋ž€ ์ ์ด ์ง€๊ธˆ ์กฐ์ง์„ ์ž๋ฅด๊ณ , ์ž˜๋ฆฐ ์กฐ์ง์€ ๋–จ์–ด์ง€๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
08:01
You can see on the microscope slide here,
196
481260
2000
์—ฌ๊ธฐ ํ˜„๋ฏธ๊ฒฝ ์Šฌ๋ผ์ด๋“œ ์œ„์—์„œ ๋ณด์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
08:03
that's what's happening in real time.
197
483260
2000
์ง€๊ธˆ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ณด์‹œ๊ณ  ๊ณ„์‹ญ๋‹ˆ๋‹ค.
08:05
There's a container underneath that's collecting that tissue.
198
485260
3000
์กฐ์ง์„ ๋ชจ์œผ๋Š” ์šฉ๊ธฐ๊ฐ€ ์•„๋ž˜์— ์žˆ๊ณ 
08:08
We take that tissue,
199
488260
2000
๊ฑฐ๊ธฐ์„œ ๊ทธ ์กฐ์ง์„ ๊ฐ€์ ธ์™€์„œ
08:10
we purify the RNA out of it
200
490260
2000
๊ฐ„๋‹จํ•œ ๊ธฐ์ˆ ์„ ํ†ตํ•ด
08:12
using some basic technology,
201
492260
2000
๋ฆฌ๋ณดํ•ต์‚ฐ์„ ์ •ํ™”์‹œํ‚จ ํ›„
08:14
and then we put a florescent tag on it.
202
494260
2000
ํ˜•๊ด‘ ํƒœ๊ทธ๋ฅผ ๊ทธ ์œ„์— ๋ถ™์ž…๋‹ˆ๋‹ค.
08:16
We take that tagged material
203
496260
2000
์šฐ๋ฆฌ๋Š” ๊ทธ ํƒœ๊ทธ๊ฐ€ ๋ถ™์€ ๋ฌผ์งˆ์„ ๊ฐ€์ ธ๋‹ค๊ฐ€
08:18
and we put it on to something called a microarray.
204
498260
3000
๋ฏธ์„ธ๋ฐฐ์—ด๊ธฐ ์œ„์— ์˜ฌ๋ ค๋†“์Šต๋‹ˆ๋‹ค.
08:21
Now this may look like a bunch of dots to you,
205
501260
2000
์˜๋ฏธ์—†๋Š” ์  ๋ฌถ์Œ ๊ฐ™์•„ ๋ณด์ด์ง€๋งŒ
08:23
but each one of these individual dots
206
503260
2000
์ด ๊ฐ๊ฐ์˜ ์ ๋“ค์€
08:25
is actually a unique piece of the human genome
207
505260
2000
์Šฌ๋ผ์ด๋“œ ์œ„์—์„œ ๋ณด์•˜๋˜
08:27
that we spotted down on glass.
208
507260
2000
์ธ๊ฐ„ ๊ฒŒ๋†ˆ ๊ณ ์œ ์˜ ์กฐ๊ฐ ์ž…๋‹ˆ๋‹ค.
08:29
This has roughly 60,000 elements on it,
209
509260
3000
์•ฝ 6๋งŒ๊ฐœ์˜ ์š”์†Œ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์—,
08:32
so we repeatedly measure various genes
210
512260
3000
์ €ํฌ๋Š” ๋ฐ˜๋ณตํ•˜์—ฌ 2๋งŒ 5์ฒœ๊ฐœ์˜ ์œ ์ „์ž์ค‘
08:35
of the 25,000 genes in the genome.
211
515260
2000
์—ฌ๋Ÿฌ๊ฐ€์ง€ ์œ ์ „์ž๋“ค์„ ์ธก์ • ํ›„
08:37
And when we take a sample and we hybridize it to it,
212
517260
3000
์ƒ˜ํ”Œ์„ ์ฑ„์ทจํ•˜์—ฌ ํ˜ผํ•ฉ๋ฌผ์„ ๋งŒ๋“ค์—ˆ๊ณ 
08:40
we get a unique fingerprint, if you will,
213
520260
2000
๋‹ค์‹œ๋งํ•ด, ๋…ํŠนํ•œ ์ง€๋ฌธ์„ ๋งŒ๋“ค์–ด ๋ƒˆ์œผ๋ฉฐ ์–‘์ ์œผ๋กœ
08:42
quantitatively of what genes are turned on in that sample.
214
522260
3000
์–ด๋–ค ์œ ์ „์ž๋“ค์ด ๊ทธ ์ƒ˜ํ”Œ๋‚ด์—์„œ ๋ฐ˜์‘ํ•˜๋Š”์ง€ ์•Œ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
08:45
Now we do this over and over again,
215
525260
2000
์ด์ œ ์šฐ๋ฆฌ๋Š” ์ด ์ž‘์—…์„ ์–ด๋–ค ๋‘๋‡Œ๊ฐ€ ์ฃผ์–ด์ ธ๋„
08:47
this process for any given brain.
216
527260
3000
๊ณ„์† ๋ฐ˜๋ณตํ•ด์„œ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
08:50
We're taking over a thousand samples for each brain.
217
530260
3000
๊ฐ๊ฐ์˜ ๋‘๋‡Œ์—์„œ๋Š” ์ฒœ๊ฐœ๊ฐ€ ๋„˜๋Š” ์ƒ˜ํ”Œ์„ ์ฑ„์ทจํ•ฉ๋‹ˆ๋‹ค.
08:53
This area shown here is an area called the hippocampus.
218
533260
3000
์ง€๊ธˆ ๋ณด์‹œ๋Š” ๋ถ€๋ถ„์€ ํ•ด๋งˆ๋ผ๊ณ  ๋ถˆ๋ฆฌ๋Š” ๋ถ€๋ถ„์ž…๋‹ˆ๋‹ค.
08:56
It's involved in learning and memory.
219
536260
2000
ํ•™์Šต๊ณผ ๊ธฐ์–ต๋ ฅ์— ๊ด€์—ฌ ํ•˜๋Š” ๋ถ€๋ถ„์ด์ฃ .
08:58
And it contributes to about 70 samples
220
538260
3000
๊ทธ๋ฆฌ๊ณ  ์ฒœ๊ฐœ์˜ ์ƒ˜ํ”Œ์ค‘
09:01
of those thousand samples.
221
541260
2000
70๊ฐœ ์ •๋„์˜ ๊ฒฌ๋ณธ์„ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
09:03
So each sample gets us about 50,000 data points
222
543260
4000
๊ทธ๋ž˜์„œ ๊ฐ๊ฐ์˜ ์ƒ˜ํ”Œ์€ ๋ฐ˜๋ณต ์ธก์ •์„ ํ†ตํ•˜์—ฌ ์•ฝ 5๋งŒ๊ฐœ์˜
09:07
with repeat measurements, a thousand samples.
223
547260
3000
์ž๋ฃŒํฌ์ธํŠธ์™€ ์ฒœ๊ฐœ์˜ ์ƒ˜ํ”Œ์„
09:10
So roughly, we have 50 million data points
224
550260
2000
์ œ๊ณต ํ•ฉ๋‹ˆ๋‹ค. ์ธ๊ฐ„์˜ ๋‘๋‡Œ๋ณ„๋กœ
09:12
for a given human brain.
225
552260
2000
์•ฝ 500์–ต๊ฐœ์˜ ์ž๋ฃŒ ํฌ์ธํŠธ๊ฐ€ ์ƒ๊ธฐ๊ฒŒ ๋ฉ๋‹ˆ๋‹ค.
09:14
We've done right now
226
554260
2000
์ €ํฌ๋Š” ํ˜„์žฌ ๋‘๊ฐœ์˜ ์ธ๊ฐ„ ๋‘๋‡Œ์— ํ•ด๋‹นํ•˜๋Š”
09:16
two human brains-worth of data.
227
556260
2000
์ž๋ฃŒ๋ฅผ ์ˆ˜์ง‘ํ•˜์˜€์Šต๋‹ˆ๋‹ค.
09:18
We've put all of that together
228
558260
2000
๊ทธ๋ฆฌ๊ณ  ๊ทธ ์ž๋ฃŒ๋“ค์„ ๋‹ค ๋ชจ์•„์„œ
09:20
into one thing,
229
560260
2000
ํ•˜๋‚˜์˜ ํ†ตํ•ฉ๋œ ์ž๋ฃŒ๋กœ ๋งŒ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค.
09:22
and I'll show you what that synthesis looks like.
230
562260
2000
์ด์ œ ์ €๋Š” ์—ฌ๋Ÿฌ๋ถ„์—๊ฒŒ ํ†ตํ•ฉ๋œ ์ž๋ฃŒ๋ฅผ ๋ณด์—ฌ๋“œ๋ฆฌ๊ฒ ์Šต๋‹ˆ๋‹ค.
09:24
It's basically a large data set of information
231
564260
3000
๊ธฐ๋ณธ์ ์œผ๋กœ ์ด๊ฒƒ์€ ๋งŽ์€ ์ •๋ณด์˜ ๋ชจ์Œ ์ด๋ฉฐ
09:27
that's all freely available to any scientist around the world.
232
567260
3000
์ „์„ธ๊ณ„์˜ ๋ชจ๋“  ๊ณผํ•™์ž๋“ค์—๊ฒŒ ๋ฌด๋ฃŒ๋กœ ์—ด๋ ค์žˆ์Šต๋‹ˆ๋‹ค.
09:30
They don't even have to log in to come use this tool,
233
570260
3000
์ด ๋„๊ตฌ๋ฅผ ์‚ฌ์šฉํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋กœ๊ทธ์ธ ํ•  ํ•„์š”๋„ ์—†์œผ๋ฉฐ,
09:33
mine this data, find interesting things out with this.
234
573260
4000
์ด ์ž๋ฃŒ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ƒˆ๋กญ๊ณ  ํฅ๋ฏธ๋กœ์šด ๋ฐœ๊ฒฌ๋„ ํ•˜์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
09:37
So here's the modalities that we put together.
235
577260
3000
์—ฌ๊ธฐ ์šฐ๋ฆฌ๊ฐ€ ๊ตฌ์„ฑํ•ด๋ณธ ์–‘์ƒ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค.
09:40
You'll start to recognize these things from what we've collected before.
236
580260
3000
์ข€์ „์— ์ €ํฌ๊ฐ€ ๋ชจ์€ ์ž๋ฃŒ๋“ค์„ ํ†ตํ•ด ๋ณด์…จ๋“ฏ์ด
09:43
Here's the MR. It provides the framework.
237
583260
2000
์ž๊ธฐ๊ณต๋ช…๊ณผ ํ•จ๊ป˜ ๊ด€๋žŒํ‹€์ด ์ œ๊ณต๋ฉ๋‹ˆ๋‹ค.
09:45
There's an operator side on the right that allows you to turn,
238
585260
3000
์šฐ์ธก์—๋Š” ๋‡Œ๋ฅผ ๋Œ๋ ค๋ณผ ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋Šฅ๋„ ์žˆ๊ณ 
09:48
it allows you to zoom in,
239
588260
2000
ํ™•๋Œ€ํ•ด์„œ ๋ณผ ์ˆ˜๋„ ์žˆ๊ณ 
09:50
it allows you to highlight individual structures.
240
590260
3000
๊ฐ๊ฐ์˜ ๊ตฌ์กฐ๋ฌผ์„ ๋ฐ๊ฒŒ ํ‘œ์‹œํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค.
09:53
But most importantly,
241
593260
2000
ํ•˜์ง€๋งŒ ์ œ์ผ ์ค‘์š”ํ•œ ๊ฒƒ์€,
09:55
we're now mapping into this anatomic framework,
242
595260
3000
ํ•ด๋ถ€ํ•™์ ์ธ ํ‹€์„ ์ง€๋„ํ™” ํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋ฉฐ
09:58
which is a common framework for people to understand where genes are turned on.
243
598260
3000
์ด๊ฒƒ์„ ํ†ตํ•ด ์œ ์ „์ž์˜ ๋ฐ˜์‘๋“ค์„ ํ•œ๋ˆˆ์— ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
10:01
So the red levels
244
601260
2000
๊ทธ๋ž˜์„œ ๋นจ๊ฐ„์ƒ‰์œผ๋กœ ํ‘œ์‹œ๋œ ๋ถ€๋ถ„์€ ์œ ์ „์ž์˜
10:03
are where a gene is turned on to a great degree.
245
603260
2000
๋ฐ˜์‘์ด ํ›จ์”ฌ ๋” ํ™œ๋ฐœํ•˜๋‹ค๋Š” ํ‘œ์‹œ์ด๋ฉฐ
10:05
Green is the sort of cool areas where it's not turned on.
246
605260
3000
์ดˆ๋ก์ƒ‰์€ ์นจ์ฐฉํ•œ ๋ถ€๋ถ„๋“ค๋กœ ๋งŽ์€ ๋ฐ˜์‘์ด ๋ณด์ด์ง€ ์•Š๋Š” ๋ถ€๋ถ„์ž…๋‹ˆ๋‹ค.
10:08
And each gene gives us a fingerprint.
247
608260
2000
๊ทธ๋ฆฌ๊ณ  ๊ฐ๊ฐ์˜ ์œ ์ „์ž๋Š” ์ง€๋ฌธ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค.
10:10
And remember that we've assayed all the 25,000 genes in the genome
248
610260
5000
์ค‘์š”ํ•œ ๊ฒƒ์€, ์ €ํฌ๊ฐ€ ๊ฒŒ๋†ˆ๋‚ด์˜ 2๋งŒ 5์ฒœ๊ฐœ์˜ ๋ชจ๋“  ์œ ์ „์ž๋ฅผ ๋ถ„์„ํ•˜์˜€์œผ๋ฉฐ
10:15
and have all of that data available.
249
615260
4000
๊ทธ ๋ฐฉ๋Œ€ํ•œ ์ž๋ฃŒ๋ฅผ ๋ชจ๋‘์—๊ฒŒ ์—ด์–ด ๋†“์•˜๋‹ค๋Š” ๊ฒƒ ์ž…๋‹ˆ๋‹ค.
10:19
So what can scientists learn about this data?
250
619260
2000
๊ณผํ•™์ž๋“ค์€ ์ด ์ž๋ฃŒ๋ฅผ ํ†ตํ•ด ๋ฌด์—‡์„ ๋ฐฐ์šธ ์ˆ˜ ์žˆ์„๊นŒ์š”?
10:21
We're just starting to look at this data ourselves.
251
621260
3000
์ €ํฌ๋„ ์ด์ œ ์ด ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์‹œ์ž‘ํ•˜์˜€์Šต๋‹ˆ๋‹ค.
10:24
There's some basic things that you would want to understand.
252
624260
3000
์—ฌ๋Ÿฌ๋ถ„์ด ์•„์…”์•ผํ•  ๋ช‡๊ฐ€์ง€ ๊ธฐ์ดˆ์ ์ธ ๊ฒƒ๋“ค์ด ์žˆ์Šต๋‹ˆ๋‹ค.
10:27
Two great examples are drugs,
253
627260
2000
ํ›Œ๋ฅญํ•œ ์˜ˆ๋กœ์จ ๋‘๊ฐ€์ง€ ์•ฝ,
10:29
Prozac and Wellbutrin.
254
629260
2000
ํ”„๋กœ์žญ๊ณผ ์›ฐ๋ถ€ํŠธ๋ฆฐ์ด ์žˆ์Šต๋‹ˆ๋‹ค.
10:31
These are commonly prescribed antidepressants.
255
631260
3000
์ด๋“ค์€ ์ผ๋ฐ˜์ ์œผ๋กœ ์ฒ˜๋ฐฉ๋˜๋Š” ํ•ญ์šฐ์šธ์ œ ์ž…๋‹ˆ๋‹ค.
10:34
Now remember, we're assaying genes.
256
634260
2000
๊ธฐ์–ตํ•˜์‹ค ๊ฒƒ์€, ์šฐ๋ฆฌ๋Š” ์œ ์ „์ž๋ฅผ ์ธก์ •ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ
10:36
Genes send the instructions to make proteins.
257
636260
3000
์œ ์ „์ž๋“ค์€ ๋‹จ๋ฐฑ์งˆ์„ ๋งŒ๋“ค๋ผ๋Š” ์ง€์‹œ๋ฅผ ๋‚ด๋ฆฝ๋‹ˆ๋‹ค.
10:39
Proteins are targets for drugs.
258
639260
2000
์•ฝ๋“ค์€ ๋‹จ๋ฐฑ์งˆ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๊ธฐ๋•Œ๋ฌธ์—
10:41
So drugs bind to proteins
259
641260
2000
๋‹จ๋ฐฑ์งˆ๊ณผ ๊ฒฐํ•ฉ์„ ํ•˜๊ฒŒ๋˜๊ณ 
10:43
and either turn them off, etc.
260
643260
2000
๊ทธ๋“ค์„ ํ•ด์ œํ•˜๊ฑฐ๋‚˜ ๋‹ค๋ฅธ ๋ฐ˜์‘์„ ํ•ฉ๋‹ˆ๋‹ค.
10:45
So if you want to understand the action of drugs,
261
645260
2000
๊ทธ๋ž˜์„œ ์•ฝ์ด ์–ด๋–ป๊ฒŒ ์ž‘์šฉํ•˜๋Š”์ง€ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ์„œ๋Š”
10:47
you want to understand how they're acting in the ways you want them to,
262
647260
3000
๊ทธ ์•ฝ๋“ค์ด ์–ด๋–ป๊ฒŒ ๋‹น์‹ ์ด ์›ํ•˜๋Š”๋Œ€๋กœ ์ž‘์šฉํ•˜๋Š”์ง€ ์ดํ•ดํ•ด์•ผ ํ•˜๊ณ 
10:50
and also in the ways you don't want them to.
263
650260
2000
๋˜ํ•œ ์›ํ•˜์ง€ ์•Š๋Š”๋Œ€๋กœ ์ž‘์šฉํ•˜๋Š”์ง€๋„ ๋ง์ž…๋‹ˆ๋‹ค.
10:52
In the side effect profile, etc.,
264
652260
2000
๋ถ€์ž‘์šฉ ๋ฐ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ƒํ™ฉ์— ๋”ฐ๋ผ
10:54
you want to see where those genes are turned on.
265
654260
2000
์–ด๋–ค ์œ ์ „์ž๊ฐ€ ๋ฐ˜์‘ํ•˜๋Š”์ง€ ๋งค์šฐ ์•Œ๊ณ  ์‹ถ์œผ์‹ค ๊ฒ๋‹ˆ๋‹ค.
10:56
And for the first time, we can actually do that.
266
656260
2000
๊ทธ๋ฆฌ๊ณ  ์‚ฌ์ƒ ์ตœ์ดˆ๋กœ, ๊ทธ๊ฒƒ์ด ๊ฐ€๋Šฅํ•ด์กŒ์Šต๋‹ˆ๋‹ค.
10:58
We can do that in multiple individuals that we've assayed too.
267
658260
3000
์ €ํฌ๊ฐ€ ์ธก์ •ํ–ˆ๋˜ ๋งŽ์€ ๊ฐœ์ธ๋“ค์˜ ๋ฐ˜์‘๋„ ๊ด€์ฐฐ์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค.
11:01
So now we can look throughout the brain.
268
661260
3000
์ด์ œ ๋‘๋‡Œ๋‚ด๋ถ€๋ฅผ ์„ธ์„ธํžˆ ๋ณผ ์ˆ˜ ์žˆ์œผ๋ฉฐ
11:04
We can see this unique fingerprint.
269
664260
2000
๊ณ ์œ ์˜ ์ง€๋ฌธ์„ ๋ณผ์ˆ˜ ์žˆ๊ณ 
11:06
And we get confirmation.
270
666260
2000
ํ™•์ธ๋„ ๊ฐ€๋Šฅ ํ•ฉ๋‹ˆ๋‹ค.
11:08
We get confirmation that, indeed, the gene is turned on --
271
668260
3000
๊ทธ ์œ ์ „์ž๊ฐ€ ์ •๋ง ๋ฐ˜์‘ํ•˜๊ณ  ์žˆ๋‹ค๋Š” ํ™•์ธ์„ ๋ง์ด์ฃ 
11:11
for something like Prozac,
272
671260
2000
ํ”„๋กœ์žญ๊ณผ ๊ฐ™์€ ๊ฒฝ์šฐ์—๋„
11:13
in serotonergic structures, things that are already known be affected --
273
673260
3000
์„ธ๋กœํ† ๋‹Œ์„ฑ ๊ตฌ์กฐ๋ฐ ์ด๋ฏธ ์˜ํ–ฅ์„ ๋ฐ›๋Š”๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ ๋‹ค๋ฅธ๊ฒƒ๋“ค๊ณผ ํ•จ๊ป˜
11:16
but we also get to see the whole thing.
274
676260
2000
์ „์ฒด์ ์ธ ๋ฐ˜์‘๋„ ๊ด€์ฐฐํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
11:18
We also get to see areas that no one has ever looked at before,
275
678260
2000
์•„๋ฌด๋„ ๋ณผ ์ˆ˜ ์—†์—ˆ๋˜ ๋ถ€๋ถ„๋“ค๋„ ๊ด€์ฐฐ์ด ๊ฐ€๋Šฅํ•˜๋ฉฐ,
11:20
and we see these genes turned on there.
276
680260
2000
์œ ์ „์ž๋“ค์ด ๊ทธ๊ณณ์—์„œ ๋ฐ˜์‘ํ•˜๋Š” ๊ฒƒ์„ ๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
11:22
It's as interesting a side effect as it could be.
277
682260
3000
์ด๋ณด๋‹ค ๋” ํฅ๋ฏธ๋กœ์šด ๋ถ€์ž‘์šฉ์ด ์žˆ์„๊นŒ์š”.
11:25
One other thing you can do with such a thing
278
685260
2000
๋˜ ํ•˜๋‚˜ ๋” ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋œ ๊ฒƒ์€
11:27
is you can, because it's a pattern matching exercise,
279
687260
3000
ํŒจํ„ด ๋งค์นญ ์ž‘์—…์„ ํ†ตํ•˜์—ฌ
11:30
because there's unique fingerprint,
280
690260
2000
๊ณ ์œ ์˜ ์ง€๋ฌธ์ด ์กด์žฌํ•˜๊ธฐ์—
11:32
we can actually scan through the entire genome
281
692260
2000
์‹ค์ œ๋กœ ์ „์ฒด ๊ฒŒ๋†ˆ์„ ์Šค์บ”ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ
11:34
and find other proteins
282
694260
2000
๋น„์Šทํ•œ ์ง€๋ฌธ์„ ๊ฐ€์ง„
11:36
that show a similar fingerprint.
283
696260
2000
๋‹ค๋ฅธ ๋‹จ๋ฐฑ์งˆ๋“ค์„ ์ฐพ์„ ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค.
11:38
So if you're in drug discovery, for example,
284
698260
3000
์˜ˆ๋ฅผ๋“ค์–ด ์‹ ์•ฝ์„ ๊ฐœ๋ฐœํ•ด์•ผ ํ•˜๋Š” ์ž„๋ฌด๊ฐ€ ์ฃผ์–ด์ง„๋‹ค๋ฉด,
11:41
you can go through
285
701260
2000
๊ฒŒ๋†ˆ์ด ๋ณด์—ฌ์ค„ ์ˆ˜ ์žˆ๋Š”
11:43
an entire listing of what the genome has on offer
286
703260
2000
์ „์ฒด ๋ชฉ๋ก์„ ๋‹ค ํ™•์ธํ•˜์—ฌ
11:45
to find perhaps better drug targets and optimize.
287
705260
4000
์•ฝ์˜ ๋ชฉํ‘œ๋ฌผ๋“ค์„ ์„ค์ •ํ•˜๊ณ  ์ตœ์ ํ™” ์‹œํ‚ฌ ์ˆ˜ ์žˆ์„ ๊ฒƒ ์ž…๋‹ˆ๋‹ค.
11:49
Most of you are probably familiar
288
709260
2000
์—ฌ๋Ÿฌ๋ถ„ ์ค‘ ๋Œ€๋ถ€๋ถ„์€ ์•„๋ž˜ ๋ฌธ๊ตฌ์™€ ๊ฐ™์€ ๋ฐฉ์‹์˜
11:51
with genome-wide association studies
289
711260
2000
๊ฒŒ๋†ˆ๋ถ„์•ผ ์—ฐ๊ตฌ์ž๋ฃŒ๋ฅผ ๋‰ด์Šค๋ฅผ ํ†ตํ•ด ์ ‘ํ•ด ๋ณด์…จ์„ ๊ฒ๋‹ˆ๋‹ค.
11:53
in the form of people covering in the news
290
713260
3000
โ€œ๊ณผํ•™์ž๋“ค์€ ์ตœ๊ทผ X ๋ผ๋Š” ์ฃผ์ œ์—
11:56
saying, "Scientists have recently discovered the gene or genes
291
716260
3000
์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์œ ์ „์ž๋‚˜ ์œ ์ „์ž๋“ค์„
11:59
which affect X."
292
719260
2000
๋ฐœ๊ฒฌ ํ•˜์˜€์Šต๋‹ˆ๋‹คโ€
12:01
And so these kinds of studies
293
721260
2000
์ด๋Ÿฐ ํ•™๋ฌธ์€ ์ •๊ทœ์ ์œผ๋กœ ๊ณผํ•™์ž๋“ค์— ์˜ํ•ด
12:03
are routinely published by scientists
294
723260
2000
๋ฐœํ‘œ๋˜๋ฉฐ ๊ฝค ๊ดœ์ฐฎ์€ ๋‚ด์šฉ์„
12:05
and they're great. They analyze large populations.
295
725260
2000
ํฌํ•จํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋“ค์€ ๋งŽ์€ ์–‘์˜
12:07
They look at their entire genomes,
296
727260
2000
์ง‘๋‹จ์„ ๋ถ„์„ํ•˜๋ฉฐ ์ „์ฒด ๊ฒŒ๋†ˆ๋“ค์„
12:09
and they try to find hot spots of activity
297
729260
2000
๊ด€์ฐฐํ•จ๊ณผ ๋™์‹œ์— ๋œจ๊ฑฐ์šด ๋ฐ˜์‘์ด
12:11
that are linked causally to genes.
298
731260
3000
์ผ์–ด๋‚˜๊ณ  ์žˆ๋Š” ๊ณณ์„ ์ฐพ์Šต๋‹ˆ๋‹ค.
12:14
But what you get out of such an exercise
299
734260
2000
ํ•˜์ง€๋งŒ ์ด๋Ÿฐ ์ž‘์—…์„ ํ†ตํ•ด ์–ป๋Š” ๊ฒƒ์€
12:16
is simply a list of genes.
300
736260
2000
๋‹จ์ง€ ์œ ์ „์ž ๋ชฉ๋ก์— ๋ถˆ๊ณผํ•ฉ๋‹ˆ๋‹ค.
12:18
It tells you the what, but it doesn't tell you the where.
301
738260
3000
๋ฌด์Šจ์ผ์ด ์ผ์–ด๋‚ฌ๋Š”์ง€๋Š” ๋งํ•ด์ฃผ๊ฒ ์ง€๋งŒ, ์–ด๋””์„œ ์ผ์–ด๋‚˜๋Š”์ง€ ์•Œ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.
12:21
And so it's very important for those researchers
302
741260
3000
๊ทธ๋ž˜์„œ ์—ฐ๊ตฌ์›๋“ค์—๊ฒŒ๋Š” ์ด๋Ÿฐ ์ž๋ฃŒ๋ฅผ ๋งŒ๋“ค์—ˆ๋‹ค๋Š”๊ฒŒ
12:24
that we've created this resource.
303
744260
2000
๋งค์šฐ ์ค‘์š”ํ•˜๊ฒŒ ๋‹ค๊ฐ€์˜ต๋‹ˆ๋‹ค.
12:26
Now they can come in
304
746260
2000
๊ทธ๋“ค์€ ์ด์ œ ์–ด๋–ค ํ™œ๋™๋“ค์„ ๋ณด๊ณ 
12:28
and they can start to get clues about activity.
305
748260
2000
์‰ฝ๊ฒŒ ๋‹จ์„œ๋ฅผ ์ฐพ์„์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
12:30
They can start to look at common pathways --
306
750260
2000
๋จผ์ € ๋ณดํ†ต ์“ฐ์ด๋Š” ํ™œ๋™ ๊ฒฝ๋กœ๋ถ€ํ„ฐ ํ™•์ธํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ
12:32
other things that they simply haven't been able to do before.
307
752260
3000
์ „์—๋Š” ๊ทธ์ € ๋ถˆ๊ฐ€๋Šฅ ํ–ˆ๋˜ ๋ฐฉ์‹๋“ค์„ ์ด์ œ๋Š” ์‹œ๋„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
12:36
So I think this audience in particular
308
756260
3000
๊ทธ๋ž˜์„œ ์ €๋Š” ์˜ค๋Š˜ ์ฒญ์ค‘ ์—ฌ๋Ÿฌ๋ถ„์ด ๋”์šฑ
12:39
can understand the importance of individuality.
309
759260
3000
๊ฐ์ž์˜ ์ฐจ์ด์˜ ์ค‘์š”์„ฑ์„ ์ž˜ ์ดํ•ดํ•˜์‹œ๋ฆฌ๋ผ ์ƒ๊ฐ๋ฉ๋‹ˆ๋‹ค.
12:42
And I think every human,
310
762260
2000
๊ทธ๋ฆฌ๊ณ  ๋ชจ๋“  ์ธ๊ฐ„์€
12:44
we all have different genetic backgrounds,
311
764260
4000
๊ฐ๊ฐ ๋‹ค๋ฅธ ์œ ์ „์ ์ธ ๋ฐ”ํƒ•์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ,
12:48
we all have lived separate lives.
312
768260
2000
๊ฐ๊ธฐ ๋‹ค๋ฅธ ์‚ถ์„ ์‚ด์•„์™”์Šต๋‹ˆ๋‹ค.
12:50
But the fact is
313
770260
2000
ํ•˜์ง€๋งŒ ์žฌ๋ฏธ์žˆ๋Š” ์‚ฌ์‹ค์€
12:52
our genomes are greater than 99 percent similar.
314
772260
3000
์šฐ๋ฆฌ์˜ ๊ฒŒ๋†ˆ๋“ค์€ 99ํผ์„ผํŠธ ์ด์ƒ ์ผ์น˜ํ•œ๋‹ค๋Š” ๊ฒƒ ์ด๊ณ 
12:55
We're similar at the genetic level.
315
775260
3000
์šฐ๋ฆฌ๊ฐ€ ์œ ์ „์  ๋ ˆ๋ฒจ์—์„œ๋„ ๋น„์Šทํ•ฉ๋‹ˆ๋‹ค.
12:58
And what we're finding
316
778260
2000
๊ทธ๋ฆฌ๊ณ  ์šฐ๋ฆฌ๊ฐ€ ๋ฐœ๊ฒฌํ•œ ๊ฒƒ์€
13:00
is actually, even at the brain biochemical level,
317
780260
2000
์ƒํ™”ํ•™์ ์ธ ๋‘๋‡Œ ๋ ˆ๋ฒจ์—์„œ๋„
13:02
we are quite similar.
318
782260
2000
์šฐ๋ฆฌ๋Š” ๋งค์šฐ ๋น„์Šทํ•ฉ๋‹ˆ๋‹ค.
13:04
And so this shows it's not 99 percent,
319
784260
2000
99ํผ์„ผํŠธ๋Š” ์•„๋‹ˆ์ง€๋งŒ
13:06
but it's roughly 90 percent correspondence
320
786260
2000
๊ฑฐ์˜ 90ํผ์„ผํŠธ์˜ ์ผ์น˜ํ•จ์„ ๋ณด์ž…๋‹ˆ๋‹ค
13:08
at a reasonable cutoff,
321
788260
3000
๊ทธ๋ž˜์„œ ๋ฏธ์ง€์†์˜ ๋ชจ๋“  ์ž๋ฃŒ๋“ค์€
13:11
so everything in the cloud is roughly correlated.
322
791260
2000
๋Œ€๋ฝ์ ์ธ ๊ด€๋ จ์„ฑ์„ ๋ณด์ž…๋‹ˆ๋‹ค.
13:13
And then we find some outliers,
323
793260
2000
๊ทธ๋ฆฌ๊ณ  ๋ช‡ ๊ฐ€์ง€ ํŠน์ˆ˜ํ•œ ํ‰๊ท ๋ฐ–์˜
13:15
some things that lie beyond the cloud.
324
795260
3000
์ž๋ฃŒ๋“ค์„ ์ฐพ๊ธฐ๋„ ํ•˜์ฃ .
13:18
And those genes are interesting,
325
798260
2000
์ด๋Ÿฐ ์œ ์ „์ž๋“ค์€ ๋งค์šฐ ํฅ๋ฏธ๋กญ์Šต๋‹ˆ๋‹ค๋งŒ,
13:20
but they're very subtle.
326
800260
2000
๋™์‹œ์— ๋งค์šฐ ๋ฏผ๊ฐํ•ฉ๋‹ˆ๋‹ค.
13:22
So I think it's an important message
327
802260
3000
๊ทธ๋ž˜์„œ ์˜ค๋Š˜ ์—ฌ๋Ÿฌ๋ถ„์—๊ฒŒ ๋ณด๋‚ด๋Š” ์ค‘์š”ํ•œ ๋ฉ”์‹œ์ง€๋Š”
13:25
to take home today
328
805260
2000
์šฐ๋ฆฌ๋Š” ์„œ๋กœ์˜ ๋‹ค๋ฅธ์ ๋“ค์— ๋Œ€ํ•ด
13:27
that even though we celebrate all of our differences,
329
807260
3000
์ฆ๊ฑฐ์›Œ ํ•˜๊ธฐ๋„ ํ•˜์ง€๋งŒ
13:30
we are quite similar
330
810260
2000
๋‘๋‡Œ ๋ ˆ๋ฒจ์—์„œ๋„ ์šฐ๋ฆฌ๋Š” ๋ชจ๋‘
13:32
even at the brain level.
331
812260
2000
๋น„์Šทํ•œ ์กด์žฌ๋ผ๋Š” ๊ฒƒ ์ž…๋‹ˆ๋‹ค.
13:34
Now what do those differences look like?
332
814260
2000
์ด๋Ÿฐ ์ฐจ์ด๋ฅผ ๋ˆˆ์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ์„๊นŒ์š”?
13:36
This is an example of a study that we did
333
816260
2000
๋ณด์‹œ๋Š” ๊ฒƒ์€ ๊ทธ ์ฐจ์ด์ ๋“ค์„ ์ •ํ™•ํžˆ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด
13:38
to follow up and see what exactly those differences were --
334
818260
2000
์ €ํฌ๋“ค์ด ๋งˆ์นœ ์—ฐ๊ตฌ์˜ ์˜ˆ์ด๋ฉฐ
13:40
and they're quite subtle.
335
820260
2000
๊ทธ ์ฐจ์ด์ ๋“ค์€ ๋งค์šฐ ๋ฏธ๋ฌ˜ํ•ฉ๋‹ˆ๋‹ค.
13:42
These are things where genes are turned on in an individual cell type.
336
822260
4000
์ด ๋ถ€๋ถ„๋“ค์€ ํ•œ๊ฐ€์ง€ ์„ธํฌ ์œ ํ˜• ์†์˜ ์œ ์ „์ž๋“ค์ด ๋ฐ˜์‘ํ•˜๊ณ  ์žˆ๋Š” ๊ณณ ์ž…๋‹ˆ๋‹ค.
13:46
These are two genes that we found as good examples.
337
826260
3000
์ €ํฌ๊ฐ€ ์ฐพ์€ ์ด ๋‘๊ฐœ์˜ ์œ ์ „์ž๋“ค์ด ์ข‹์€ ์˜ˆ ์ž…๋‹ˆ๋‹ค.
13:49
One is called RELN -- it's involved in early developmental cues.
338
829260
3000
์ฒซ๋ฒˆ์งธ๋Š” RELN ์ด๋ผ๊ณ  ๋ถˆ๋ฆฌ๋ฉฐ ์ดˆ๊ธฐ ์ง„ํ–‰๋‹จ๊ณ„ ์•”์‹œ์— ๊ด€๊ณ„ ๋˜์–ด์žˆ์Šต๋‹ˆ๋‹ค.
13:52
DISC1 is a gene
339
832260
2000
DISC1 ๋Š” ํ•œ ์œ ์ „์ž๋กœ์„œ
13:54
that's deleted in schizophrenia.
340
834260
2000
์ •์‹ ๋ถ„์—ด์ฆ์ƒ ์—์„œ๋Š” ๋ˆ„๋ฝ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค.
13:56
These aren't schizophrenic individuals,
341
836260
2000
๋ณด์‹œ๋Š” ๊ฒƒ์€ ์ •์‹ ๋ถ„์—ด์ฆ์„ ๊ฒช๋Š” ์‚ฌ๋žŒ๋“ค์ด ์•„๋‹ˆ์ง€๋งŒ,
13:58
but they do show some population variation.
342
838260
3000
์ง‘๋‹จ์‚ฌ์ด ํŽธ์ฐจ๊ฐ€ ์กด์žฌํ•˜๊ธฐ๋„ ํ•ฉ๋‹ˆ๋‹ค.
14:01
And so what you're looking at here
343
841260
2000
๊ทธ๋ž˜์„œ ์ง€๊ธˆ ๋ณด์‹œ๋Š” ๊ฒƒ์€
14:03
in donor one and donor four,
344
843260
2000
์ œ๊ณต์ž 1๋ฒˆ๊ณผ 4๋ฒˆ์˜ ๊ฒƒ์œผ๋กœ
14:05
which are the exceptions to the other two,
345
845260
2000
๋‹ค๋ฅธ ๋‘ ์ œ๊ณต์ž๋“ค๊ณผ๋Š” ์˜ˆ์™ธ์ ์œผ๋กœ
14:07
that genes are being turned on
346
847260
2000
๋งค์šฐ ํŠน์ •ํ•œ ์กฐ์ง์˜ ๋ถ€๋ถ„์ง‘ํ•ฉ ๋‚ด์—์„œ
14:09
in a very specific subset of cells.
347
849260
2000
์œ ์ „์ž๋“ค์ด ๋ฐ˜์‘ํ•˜๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
14:11
It's this dark purple precipitate within the cell
348
851260
3000
์ง€๊ธˆ ๋ณด์ด๋Š” ์กฐ์ง๋‚ด์˜ ์–ด๋‘์šด ๋ณด๋ž๋น› ์นจ์ „๋ฌผ์ด
14:14
that's telling us a gene is turned on there.
349
854260
3000
์œ ์ „์ž๊ฐ€ ๊ทธ ๊ณณ์—์„œ ๋ฐ˜์‘ํ•˜๊ณ  ์žˆ๋‹ค๊ณ  ๋งํ•ด ์ค๋‹ˆ๋‹ค.
14:17
Whether or not that's due
350
857260
2000
์ด๊ฒƒ์ด ๊ทธ ๊ฐœ์ธ์˜ ์œ ์ „์ ์ธ
14:19
to an individual's genetic background or their experiences,
351
859260
2000
๋ฐฐ๊ฒฝ์ด๋‚˜ ๊ฒฝํ—˜์— ๋”ฐ๋ฅธ ๊ฒƒ์ธ์ง€ ์•„๋‹Œ์ง€๋Š”,
14:21
we don't know.
352
861260
2000
์ €ํฌ๋„ ์•Œ ์ˆ˜ ์—†์Šต๋‹ˆ๋‹ค.
14:23
Those kinds of studies require much larger populations.
353
863260
3000
๊ทธ๋Ÿฐ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด์„œ๋Š” ํ›จ์”ฌ ๋งŽ์€ ์ƒ˜ํ”Œ๋“ค์ด ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค.
14:28
So I'm going to leave you with a final note
354
868260
2000
๋งˆ์ง€๋ง‰์œผ๋กœ ๋‘๋‡Œ์˜ ๋ณต์žก์„ฑ์— ๋Œ€ํ•ด์„œ์™€
14:30
about the complexity of the brain
355
870260
3000
์–ผ๋งˆ๋‚˜ ์•„์ง๋„ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•œ์ง€์— ๋Œ€ํ•ด์„œ
14:33
and how much more we have to go.
356
873260
2000
๋ง์”€๋“œ๋ฆฌ๋ฉฐ ๋ง์„ ๋งˆ์น˜๊ณ  ์‹ถ์Šต๋‹ˆ๋‹ค.
14:35
I think these resources are incredibly valuable.
357
875260
2000
์ „ ์ด ์ž๋ฃŒ๋“ค์ด ์—„์ฒญ๋‚œ ๊ฐ€์น˜๋ฅผ ์ง€๋‹Œ๋‹ค๊ณ  ์ƒ๊ฐํ•ฉ๋‹ˆ๋‹ค.
14:37
They give researchers a handle
358
877260
2000
์—ฐ๊ตฌ์›๋“ค์—๊ฒŒ ์–ด๋””๋กœ ๋‚˜์•„๊ฐ€์•ผ ํ• ์ง€๋ฅผ
14:39
on where to go.
359
879260
2000
์•Œ๋ ค์ฃผ๋Š” ๋ฐฉํ–ฅํƒ€๊ฐ€ ๋˜์–ด ์ค๋‹ˆ๋‹ค.
14:41
But we only looked at a handful of individuals at this point.
360
881260
3000
ํ•˜์ง€๋งŒ ์ง€๊ธˆ๊นŒ์ง€ ์šฐ๋ฆฌ๋Š” ๋งค์šฐ ์†Œ์ˆ˜์˜ ์‚ฌ๋žŒ๋“ค์˜ ์ž๋ฃŒ๋งŒ ํ™•์ธํ•ด ๋ณด์•˜์Šต๋‹ˆ๋‹ค.
14:44
We're certainly going to be looking at more.
361
884260
2000
๋ฌผ๋ก  ์•ž์œผ๋กœ๋Š” ํ›จ์”ฌ ๋” ์‚ฌ๋žŒ๋“ค์˜ ์ž๋ฃŒ๋ฅผ ๋ณผ ๊ฒƒ์ž…๋‹ˆ๋‹ค.
14:46
I'll just close by saying
362
886260
2000
๋งˆ์ง€๋ง‰์œผ๋กœ ์ œ๊ฐ€ ํ•˜๊ณ  ์‹ถ์€ ๋ง์€
14:48
that the tools are there,
363
888260
2000
ํ•„์š”ํ•œ ๋„๊ตฌ๋Š” ์ค€๋น„๋˜์—ˆ์œผ๋ฉฐ
14:50
and this is truly an unexplored, undiscovered continent.
364
890260
4000
์ด๊ฒƒ์€ ์•„๋ฌด๋„ ํƒํ—˜ํ•˜์ง€ ์•Š์€ ๋ฏธ์ง€์˜ ๋ถ„์•ผ๋ผ๋Š” ๊ฒƒ ์ž…๋‹ˆ๋‹ค.
14:54
This is the new frontier, if you will.
365
894260
4000
๋‹ค์‹œ๋งํ•ด ์ƒˆ๋กœ์šด ๊ตญ๊ฒฝ์ž…๋‹ˆ๋‹ค.
14:58
And so for those who are undaunted,
366
898260
2000
๋‘๋‡Œ์˜ ์ƒˆ๋กœ์šด ๋ฐœ๊ฒฌ์„ ๋‘๋ ค์›Œ ํ•˜์ง€ ์•Š์œผ๋ฉฐ
15:00
but humbled by the complexity of the brain,
367
900260
2000
๊ทธ ์‹ ๋น„์˜ ์†Œ์ค‘ํ•จ์„ ์•„๋Š” ์ž๋“ค์—๊ฒŒ๋Š”
15:02
the future awaits.
368
902260
2000
๋ฐ์€ ๋ฏธ๋ž˜๊ฐ€ ๊ธฐ๋‹ค๋ฆฌ๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค.
15:04
Thanks.
369
904260
2000
๊ฐ์‚ฌํ•ฉ๋‹ˆ๋‹ค.
15:06
(Applause)
370
906260
9000
(๋ฐ•์ˆ˜)

Original video on YouTube.com
์ด ์›น์‚ฌ์ดํŠธ ์ •๋ณด

์ด ์‚ฌ์ดํŠธ๋Š” ์˜์–ด ํ•™์Šต์— ์œ ์šฉํ•œ YouTube ๋™์˜์ƒ์„ ์†Œ๊ฐœํ•ฉ๋‹ˆ๋‹ค. ์ „ ์„ธ๊ณ„ ์ตœ๊ณ ์˜ ์„ ์ƒ๋‹˜๋“ค์ด ๊ฐ€๋ฅด์น˜๋Š” ์˜์–ด ์ˆ˜์—…์„ ๋ณด๊ฒŒ ๋  ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ฐ ๋™์˜์ƒ ํŽ˜์ด์ง€์— ํ‘œ์‹œ๋˜๋Š” ์˜์–ด ์ž๋ง‰์„ ๋”๋ธ” ํด๋ฆญํ•˜๋ฉด ๊ทธ๊ณณ์—์„œ ๋™์˜์ƒ์ด ์žฌ์ƒ๋ฉ๋‹ˆ๋‹ค. ๋น„๋””์˜ค ์žฌ์ƒ์— ๋งž์ถฐ ์ž๋ง‰์ด ์Šคํฌ๋กค๋ฉ๋‹ˆ๋‹ค. ์˜๊ฒฌ์ด๋‚˜ ์š”์ฒญ์ด ์žˆ๋Š” ๊ฒฝ์šฐ ์ด ๋ฌธ์˜ ์–‘์‹์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ฌธ์˜ํ•˜์‹ญ์‹œ์˜ค.

https://forms.gle/WvT1wiN1qDtmnspy7