1 00:00:00,000 --> 00:00:02,300 If you're not from the majority group, 2 00:00:02,300 --> 00:00:03,940 you don't see anyone else that looks like you, 3 00:00:03,940 --> 00:00:06,880 you think, well, I'm the odd one out, I must be wrong. 4 00:00:06,880 --> 00:00:09,520 But the truth is, as we've talked about, 5 00:00:09,520 --> 00:00:12,320 the diversity is what drives the innovation in science. 6 00:00:12,320 --> 00:00:15,960 And so when you stick it out, you stick to your, 7 00:00:17,120 --> 00:00:21,200 try to bring people along, I guess, to your perspectives. 8 00:00:21,200 --> 00:00:24,060 And in the long run, it's very satisfying. 9 00:00:25,660 --> 00:00:27,960 The human brain is the most complex structure 10 00:00:27,960 --> 00:00:29,120 in the known universe. 11 00:00:29,120 --> 00:00:31,640 And we are in the middle of a scientific revolution 12 00:00:31,640 --> 00:00:33,700 to understand its inner workings. 13 00:00:33,700 --> 00:00:35,140 Join us for a conversation 14 00:00:35,140 --> 00:00:38,440 with world renowned neuroscientists as they visit Rochester. 15 00:00:38,440 --> 00:00:39,820 I am Dr. John Foxe, 16 00:00:39,820 --> 00:00:42,080 Director of the Del Monte Institute for Neuroscience 17 00:00:42,080 --> 00:00:43,840 at the University of Rochester. 18 00:00:43,840 --> 00:00:46,720 And you are listening to Neuroscience Perspectives. 19 00:00:48,800 --> 00:00:51,900 Hello, and welcome to Neuroscience Perspectives. 20 00:00:51,900 --> 00:00:53,840 I'm John Fox, I'm the Director 21 00:00:53,840 --> 00:00:55,800 of the Del Monte Institute for Neuroscience 22 00:00:55,800 --> 00:00:57,760 at the University of Rochester. 23 00:00:57,760 --> 00:01:00,320 And I'm delighted to have with me here today, 24 00:01:00,320 --> 00:01:01,960 Professor Lucina Uddin 25 00:01:01,960 --> 00:01:05,080 from the University of California in Los Angeles. 26 00:01:05,080 --> 00:01:09,240 Lucina is a professor of psychiatry and behavioral sciences. 27 00:01:09,240 --> 00:01:12,480 And she works with functional neuroimaging 28 00:01:12,480 --> 00:01:14,840 to investigate brain connectivity 29 00:01:14,840 --> 00:01:19,840 and circuit activity in children and adolescents 30 00:01:20,080 --> 00:01:21,840 developing the developing brain 31 00:01:21,840 --> 00:01:24,500 with a concentration also on children 32 00:01:24,500 --> 00:01:27,480 with atypical development and intellectual disabilities. 33 00:01:27,480 --> 00:01:30,560 Lucina, it's really wonderful to have you here. 34 00:01:30,560 --> 00:01:32,960 And let's dive right in. 35 00:01:32,960 --> 00:01:36,120 Before we really get stuck into the science, 36 00:01:36,120 --> 00:01:39,880 I always like to begin by just finding out a little bit 37 00:01:39,880 --> 00:01:41,480 about you and your trajectory. 38 00:01:41,480 --> 00:01:44,500 I know you grew up in California, 39 00:01:44,500 --> 00:01:46,040 but you were born elsewhere. 40 00:01:46,040 --> 00:01:47,440 And do you wanna tell us a little bit 41 00:01:47,440 --> 00:01:49,400 about your trajectory? 42 00:01:49,400 --> 00:01:52,000 Well, I guess how far back do you wanna go? 43 00:01:52,000 --> 00:01:54,520 So I was actually born in Bangladesh. 44 00:01:54,520 --> 00:01:56,560 My parents immigrated to the United States 45 00:01:56,560 --> 00:01:58,240 when I was an infant. 46 00:01:58,240 --> 00:02:02,480 And so it's a pretty typical immigrant story from South Asia. 47 00:02:02,480 --> 00:02:05,600 But I did grow up in Southern California 48 00:02:05,600 --> 00:02:09,440 and I was fortunate enough to be able to come back to home 49 00:02:09,440 --> 00:02:10,280 a couple of years ago 50 00:02:10,280 --> 00:02:12,680 when I joined the psychiatry department at UCLA. 51 00:02:12,680 --> 00:02:13,520 Terrific. 52 00:02:13,520 --> 00:02:15,000 And what took you into science? 53 00:02:15,000 --> 00:02:17,960 Was that a passion in school as a youngster 54 00:02:17,960 --> 00:02:19,560 or is it a later developing thing? 55 00:02:19,560 --> 00:02:20,800 The opposite, in fact. 56 00:02:20,800 --> 00:02:22,760 I was not interested in science. 57 00:02:22,760 --> 00:02:26,120 I was interested in literature and language and arts. 58 00:02:26,120 --> 00:02:28,600 My father has a PhD in comparative literature. 59 00:02:28,600 --> 00:02:31,240 So no science in our home. 60 00:02:31,240 --> 00:02:33,680 It was not something I thought I would be doing. 61 00:02:33,680 --> 00:02:36,400 I did, however, become practically minded 62 00:02:36,400 --> 00:02:37,560 towards the college years 63 00:02:37,560 --> 00:02:42,200 and sort of entered a pre-med major, studied neuroscience. 64 00:02:42,200 --> 00:02:45,000 Mainly because at that age, you don't know what to do. 65 00:02:45,000 --> 00:02:48,160 You look at a list of a hundred majors and you pick one 66 00:02:48,160 --> 00:02:50,720 and being a child of immigrants, 67 00:02:50,720 --> 00:02:52,560 it seemed like, yes, I should be a doctor. 68 00:02:52,560 --> 00:02:55,120 It wasn't a whole lot of thought process that went into it. 69 00:02:55,120 --> 00:02:57,160 But towards the end of my undergraduate years 70 00:02:57,160 --> 00:02:58,400 studying neuroscience, 71 00:02:58,400 --> 00:03:02,120 I really began to just become fascinated with the brain. 72 00:03:02,120 --> 00:03:04,920 Of course, what's not to like, right? 73 00:03:04,920 --> 00:03:06,880 It's one of the most interesting organs. 74 00:03:06,880 --> 00:03:08,160 But realized, of course, 75 00:03:08,160 --> 00:03:11,600 that I was not well-suited to go to medical school 76 00:03:11,600 --> 00:03:12,760 and actually had no interest 77 00:03:12,760 --> 00:03:15,600 in following that particular career path. 78 00:03:15,600 --> 00:03:17,720 Lucky for me, I came to learn 79 00:03:17,720 --> 00:03:20,360 that there's other ways you can engage in science 80 00:03:20,360 --> 00:03:22,560 and one of them is to become a neuroscientist. 81 00:03:22,560 --> 00:03:24,640 So that's what I ended up doing. 82 00:03:24,640 --> 00:03:27,440 Right, well, we won't get into the idea 83 00:03:27,440 --> 00:03:28,920 that the science is practical 84 00:03:28,920 --> 00:03:30,240 and everything else is impractical. 85 00:03:30,240 --> 00:03:31,880 Right, that's not what I meant. 86 00:03:31,880 --> 00:03:34,480 Actually, it's just myself, I studied it. 87 00:03:34,480 --> 00:03:37,600 When I started my studies, it was in literature 88 00:03:37,600 --> 00:03:41,800 and I came to science quite late in life myself. 89 00:03:41,800 --> 00:03:44,160 And I think it makes for an interesting trajectory. 90 00:03:44,160 --> 00:03:48,040 So your parents, your dad was an academic. 91 00:03:48,040 --> 00:03:49,760 So that must have been an influence 92 00:03:49,760 --> 00:03:51,920 on sort of choosing the academic life. 93 00:03:51,920 --> 00:03:52,840 Yeah, I think so. 94 00:03:52,840 --> 00:03:55,720 I mean, just even realizing that one can get a PhD 95 00:03:55,720 --> 00:03:57,120 and specialize on a topic 96 00:03:57,120 --> 00:03:59,040 and study it for the rest of their lives, 97 00:03:59,040 --> 00:04:02,440 I think it's not always understood 98 00:04:02,440 --> 00:04:03,720 that that's a career path. 99 00:04:03,720 --> 00:04:05,920 So I think especially for people 100 00:04:05,920 --> 00:04:07,320 coming from other countries, 101 00:04:08,680 --> 00:04:11,160 they kind of want stability, economic stability. 102 00:04:11,160 --> 00:04:12,600 And that's sort of one path to that 103 00:04:12,600 --> 00:04:17,600 is to go into a field like science, engineering or medicine. 104 00:04:17,640 --> 00:04:19,280 So I think it's really, 105 00:04:19,280 --> 00:04:22,640 I think I appreciate having that kind of background 106 00:04:22,640 --> 00:04:26,160 to sort of draw from in terms of thinking about 107 00:04:26,160 --> 00:04:29,000 when I mentor students from all over the world, 108 00:04:29,000 --> 00:04:30,280 kind of having an understanding 109 00:04:30,280 --> 00:04:32,240 of where they might be coming from might be different 110 00:04:32,240 --> 00:04:36,040 than growing up in a middle-class American family, 111 00:04:36,040 --> 00:04:36,880 for example. 112 00:04:38,520 --> 00:04:39,760 I hadn't intended to ask you this, 113 00:04:39,760 --> 00:04:42,200 but let's stay with this interface 114 00:04:42,200 --> 00:04:44,360 coming from the humanities into science. 115 00:04:44,360 --> 00:04:45,200 Sure. 116 00:04:45,200 --> 00:04:47,320 Do you bring that into your science 117 00:04:47,320 --> 00:04:48,400 or did you leave it behind you? 118 00:04:48,400 --> 00:04:49,240 Oh, I bring it. 119 00:04:49,240 --> 00:04:51,560 I mean, I'm a huge reader. 120 00:04:51,560 --> 00:04:53,760 I love writing and literature to this day. 121 00:04:53,760 --> 00:04:56,280 I read at least one novel a month, 122 00:04:56,280 --> 00:04:57,800 really into fiction and sci-fi. 123 00:04:57,800 --> 00:04:59,320 But I think what people don't realize 124 00:04:59,320 --> 00:05:00,720 is that a career in science 125 00:05:00,720 --> 00:05:03,720 requires so much writing and communication. 126 00:05:03,720 --> 00:05:07,480 And that's something you develop really very much so 127 00:05:07,480 --> 00:05:09,040 in the humanities. 128 00:05:09,040 --> 00:05:11,320 You learn to write, you learn to communicate, 129 00:05:11,320 --> 00:05:15,200 you learn to convey complex ideas to a wide audience 130 00:05:15,200 --> 00:05:17,000 and to really engage and bring people in. 131 00:05:17,000 --> 00:05:18,040 Why are we doing this? 132 00:05:18,040 --> 00:05:19,520 What makes it important? 133 00:05:19,520 --> 00:05:20,960 What's the significance? 134 00:05:20,960 --> 00:05:23,240 How does it help us understand the human condition? 135 00:05:23,240 --> 00:05:25,360 These are what the humanities train you for. 136 00:05:25,360 --> 00:05:27,920 So I think, you know, I never meant to say, 137 00:05:27,920 --> 00:05:28,920 oh, that wasn't practical. 138 00:05:28,920 --> 00:05:31,480 I think, in fact, all of that training, you know, 139 00:05:31,480 --> 00:05:34,080 helped me become the scientist that I am. 140 00:05:34,080 --> 00:05:35,080 I completely agree. 141 00:05:35,080 --> 00:05:38,000 I mean, you could have the greatest finding idea in the world, 142 00:05:38,000 --> 00:05:39,840 but if you can't turn it into words 143 00:05:39,840 --> 00:05:41,680 that you can communicate to people with, 144 00:05:41,680 --> 00:05:42,920 it may as well not exist. 145 00:05:42,920 --> 00:05:43,760 Exactly. 146 00:05:43,760 --> 00:05:45,720 My PhD advisor actually said that 147 00:05:45,720 --> 00:05:47,880 if you did a study and didn't publish it, 148 00:05:47,880 --> 00:05:49,000 it's as if you didn't do it 149 00:05:49,000 --> 00:05:51,400 because no one knows what you did. 150 00:05:51,400 --> 00:05:52,320 Very good. 151 00:05:52,320 --> 00:05:53,160 That's terrific. 152 00:05:53,160 --> 00:05:57,440 I really, I think that's great advice actually 153 00:05:57,440 --> 00:06:00,800 to graduate students that don't neglect that side of life. 154 00:06:00,800 --> 00:06:02,600 I actually have lived by a maxim myself, 155 00:06:02,600 --> 00:06:04,680 which is if I don't have a place in my life 156 00:06:04,680 --> 00:06:06,680 for a book that's not related to the job, 157 00:06:06,680 --> 00:06:08,440 my life is out of balance. 158 00:06:08,440 --> 00:06:09,280 That's true. 159 00:06:09,280 --> 00:06:10,720 Well, there's two things that I want to talk about. 160 00:06:10,720 --> 00:06:13,960 One, obviously I want to get into the functional imaging 161 00:06:13,960 --> 00:06:16,200 and how you approach your science. 162 00:06:16,200 --> 00:06:17,800 And the other piece, you know, 163 00:06:17,800 --> 00:06:19,480 that our audience should know about is, 164 00:06:19,480 --> 00:06:22,440 you know, you have a very specific devotion 165 00:06:22,440 --> 00:06:26,960 to diversity, equity, and inclusion in the sciences, 166 00:06:26,960 --> 00:06:28,680 and you've put a big chunk of your career 167 00:06:28,680 --> 00:06:30,800 into that and won awards for in that space. 168 00:06:30,800 --> 00:06:32,960 Well, we'll get to talk about that, I think, shortly, 169 00:06:32,960 --> 00:06:35,800 but let's do the science piece first. 170 00:06:35,800 --> 00:06:37,440 You use big magnets. 171 00:06:37,440 --> 00:06:38,280 Yes. 172 00:06:38,280 --> 00:06:40,040 Right, do you want to tell us a little bit about that 173 00:06:40,040 --> 00:06:41,880 and what it means and how you approach it? 174 00:06:41,880 --> 00:06:44,640 Those of us who want to study the human brain, 175 00:06:44,640 --> 00:06:46,240 unfortunately we have few options 176 00:06:46,240 --> 00:06:50,760 because we can't do a lot of that really nice invasive work 177 00:06:50,760 --> 00:06:53,520 that our colleagues do and who work on animal models. 178 00:06:53,520 --> 00:06:55,760 So we can't do quite as much manipulation. 179 00:06:55,760 --> 00:06:59,080 We can't do quite as much of getting into, 180 00:06:59,080 --> 00:07:00,760 you know, cellular mechanisms 181 00:07:00,760 --> 00:07:03,880 because we're confined by what you can do to human subjects. 182 00:07:03,880 --> 00:07:08,680 So I've been working in magnetic resonance imaging 183 00:07:08,680 --> 00:07:11,560 since I was a PhD student, so I guess 20 years now. 184 00:07:11,560 --> 00:07:12,720 And we can, you know, of course, 185 00:07:12,720 --> 00:07:14,200 non-invasively look at the brain 186 00:07:14,200 --> 00:07:16,080 when people are doing certain things. 187 00:07:16,080 --> 00:07:18,880 We can look at their brain when they're doing nothing at all, 188 00:07:18,880 --> 00:07:21,120 which is something that's become very interesting 189 00:07:21,120 --> 00:07:22,280 in the last few years. 190 00:07:22,280 --> 00:07:24,960 So yeah, I've been trying to sort of push the limits 191 00:07:24,960 --> 00:07:28,760 of what we can do with this magnetic resonance imaging 192 00:07:28,760 --> 00:07:32,520 technology because we're learning more and more about sort of, 193 00:07:32,520 --> 00:07:36,000 it's not just, you know, input comes into the brain 194 00:07:36,000 --> 00:07:38,160 and the brain does something and produces some output, 195 00:07:38,160 --> 00:07:40,760 but it turns out there's a lot of spontaneous activity 196 00:07:40,760 --> 00:07:42,800 in the brain and we're only now just beginning to understand 197 00:07:42,800 --> 00:07:45,520 how that constrains function, constrains behavior, 198 00:07:45,520 --> 00:07:47,840 how it shapes kind of what we do. 199 00:07:47,840 --> 00:07:51,120 I think this is just a fascinating area 200 00:07:51,120 --> 00:07:54,040 that my love has been thinking a lot about, 201 00:07:54,040 --> 00:07:56,200 this intrinsic activity, spontaneous activity. 202 00:07:56,200 --> 00:07:57,120 What does it mean? 203 00:07:57,120 --> 00:07:59,560 You know, what's it there for and how can we use it 204 00:07:59,560 --> 00:08:01,520 to better understand brain function? 205 00:08:01,520 --> 00:08:04,160 So this idea that there are brain states, 206 00:08:04,160 --> 00:08:07,040 that the networks are active in a certain way 207 00:08:07,040 --> 00:08:11,200 when things are good and that you can even just image 208 00:08:11,200 --> 00:08:14,240 the brain at rest and understand when things are awry 209 00:08:14,240 --> 00:08:16,880 or off kilter or the networks aren't working well. 210 00:08:16,880 --> 00:08:21,880 And give us something, like give us a nugget of an insight 211 00:08:22,160 --> 00:08:23,720 that we've gained from this. 212 00:08:23,720 --> 00:08:26,240 Yeah, I mean, I think people were surprised to find 213 00:08:26,240 --> 00:08:29,760 how coherent this activity is and how you can find it 214 00:08:29,760 --> 00:08:31,600 in individuals over time. 215 00:08:31,600 --> 00:08:33,360 Like for example, in your brain and my brain, 216 00:08:33,360 --> 00:08:35,440 we can find similar looking networks, 217 00:08:35,440 --> 00:08:37,240 even in this, what we call resting state, 218 00:08:37,240 --> 00:08:39,280 when we're not in, even if we're just lying there, 219 00:08:39,280 --> 00:08:42,080 not doing anything at all, we would still be showing 220 00:08:42,080 --> 00:08:44,560 spontaneous, coherent, low frequency activity 221 00:08:44,560 --> 00:08:47,000 in what you might call a motor network 222 00:08:47,000 --> 00:08:48,880 or a language network or a visual network. 223 00:08:48,880 --> 00:08:52,000 Those are regions of the brain that would be cooperating 224 00:08:52,000 --> 00:08:54,320 if we were doing a motor task or a visual task 225 00:08:54,320 --> 00:08:55,280 or a language task. 226 00:08:55,280 --> 00:08:58,640 It turns out they're just kind of spontaneously going, 227 00:08:58,640 --> 00:09:00,880 they're just going in loops all the time. 228 00:09:00,880 --> 00:09:02,280 And you can find them in individuals, 229 00:09:02,280 --> 00:09:04,120 you can find that they change over the lifespan, 230 00:09:04,120 --> 00:09:07,040 you can find some kind of signature alterations 231 00:09:07,040 --> 00:09:08,520 in some developmental conditions. 232 00:09:08,520 --> 00:09:12,400 It's just a whole new way of thinking about brain function 233 00:09:12,400 --> 00:09:15,320 that I think has really produced a lot of insights. 234 00:09:15,320 --> 00:09:18,120 And I suppose a magic power to this particular technique 235 00:09:18,120 --> 00:09:20,640 then is that you don't have, if you have children 236 00:09:20,640 --> 00:09:23,400 and you're very concentrated on development 237 00:09:23,400 --> 00:09:28,080 who can't answer a question or can't perform a complex task, 238 00:09:28,080 --> 00:09:30,640 it doesn't put a huge demand on that. 239 00:09:30,640 --> 00:09:32,720 You can actually look at function 240 00:09:32,720 --> 00:09:35,520 without having people doing somersaults for us. 241 00:09:35,520 --> 00:09:36,360 That's right, yeah. 242 00:09:36,360 --> 00:09:39,040 You can just have someone say, lay still for five minutes. 243 00:09:39,040 --> 00:09:40,640 And if you can get them to do that, 244 00:09:40,640 --> 00:09:42,160 you can actually collect this data, 245 00:09:42,160 --> 00:09:43,680 which tells you so much information 246 00:09:43,680 --> 00:09:44,920 about brain organization. 247 00:09:44,920 --> 00:09:47,760 And gives it a clinical tractability component too. 248 00:09:47,760 --> 00:09:48,600 That's right. 249 00:09:48,600 --> 00:09:50,480 That's really interesting. 250 00:09:50,480 --> 00:09:51,680 I happen to know, of course, 251 00:09:51,680 --> 00:09:55,280 that you are very involved with one of the, 252 00:09:55,280 --> 00:09:58,360 I think maybe the biggest study that NIH has ever, 253 00:09:58,360 --> 00:09:59,720 the National Institute of Health 254 00:09:59,720 --> 00:10:02,840 has ever really undertaken the ABCD study, 255 00:10:02,840 --> 00:10:05,240 Adolescent Brain Cognitive Development Study, 256 00:10:05,240 --> 00:10:06,560 which is a mouthful. 257 00:10:06,560 --> 00:10:07,800 Tell us a little bit about that 258 00:10:07,800 --> 00:10:10,320 and what your hopes and aspirations are for it. 259 00:10:10,320 --> 00:10:13,200 Yeah, and I assume you're also very involved. 260 00:10:13,200 --> 00:10:14,320 Yes, indeed. 261 00:10:14,320 --> 00:10:18,160 But yeah, this study that NIH has been funding now 262 00:10:18,160 --> 00:10:22,400 for close to 10 years has started with nine and 10 year olds 263 00:10:22,400 --> 00:10:23,560 and a huge group of them, 264 00:10:23,560 --> 00:10:25,840 over 10,000 nine and 10 year olds, 265 00:10:25,840 --> 00:10:28,440 who came in and the data were collected 266 00:10:28,440 --> 00:10:30,440 across 21 sites all over the United States. 267 00:10:30,440 --> 00:10:33,200 So the idea was to get people from all over the place 268 00:10:33,200 --> 00:10:36,560 and just follow them up yearly with brain imaging measures 269 00:10:36,560 --> 00:10:39,000 and cognitive measures and behavioral measures 270 00:10:39,000 --> 00:10:42,840 with ultimate goal of trying to predict susceptibility, 271 00:10:42,840 --> 00:10:44,680 risk and resilience to substance abuse 272 00:10:44,680 --> 00:10:49,200 and mental health disorders across this adolescent period. 273 00:10:49,200 --> 00:10:52,240 So it's just enormous, as you can imagine, 274 00:10:52,240 --> 00:10:53,720 both as a data collection effort 275 00:10:53,720 --> 00:10:55,400 and as a data dissemination effort 276 00:10:55,400 --> 00:10:57,200 and as a data analysis effort. 277 00:10:57,200 --> 00:11:01,760 So for years now, NIH has been sort of releasing the data 278 00:11:01,760 --> 00:11:03,800 to the public for researchers to use 279 00:11:03,800 --> 00:11:05,240 to answer all of these questions. 280 00:11:05,240 --> 00:11:06,200 And that is key. 281 00:11:06,200 --> 00:11:08,680 This is a very important piece, right? 282 00:11:08,680 --> 00:11:10,760 The model has changed here. 283 00:11:10,760 --> 00:11:13,040 As scientists, we collected data in our labs, 284 00:11:13,040 --> 00:11:14,480 we kept it, we huddled around it, 285 00:11:14,480 --> 00:11:15,320 we kept it for ourselves. 286 00:11:15,320 --> 00:11:16,160 Horted it. 287 00:11:16,160 --> 00:11:18,240 Horted it, it was precious commodity. 288 00:11:18,240 --> 00:11:19,480 But this is a change in the model. 289 00:11:19,480 --> 00:11:20,920 Do you wanna talk about open science 290 00:11:20,920 --> 00:11:23,440 and what our hopes for that are? 291 00:11:23,440 --> 00:11:24,880 I mean, open science is huge. 292 00:11:24,880 --> 00:11:27,400 The idea that the data that scientists collect 293 00:11:27,400 --> 00:11:29,760 should be made available for other scientists to use 294 00:11:29,760 --> 00:11:34,280 and answer whatever questions that they want to answer. 295 00:11:34,280 --> 00:11:35,120 This is huge. 296 00:11:35,120 --> 00:11:37,800 I actually started my lab about 10 years ago 297 00:11:37,800 --> 00:11:39,760 when this really took off. 298 00:11:39,760 --> 00:11:42,760 It used to be sort of unheard of that people would say, 299 00:11:42,760 --> 00:11:45,360 hey, I spent the last five years and $5 million 300 00:11:45,360 --> 00:11:47,200 collecting this data, here, you have it. 301 00:11:47,200 --> 00:11:49,040 But now it's actually mandated 302 00:11:49,040 --> 00:11:50,960 by the National Institute of Health to say, 303 00:11:50,960 --> 00:11:53,360 okay, you've spent all this effort, great. 304 00:11:53,360 --> 00:11:55,840 Now let's see what others can do with it as well. 305 00:11:55,840 --> 00:11:58,440 And it brings the kind of collaboration 306 00:11:58,440 --> 00:12:01,240 that really benefits all of us 307 00:12:01,240 --> 00:12:03,120 because then you get computer scientists, 308 00:12:03,120 --> 00:12:05,520 electrical engineers, physicists sometimes, 309 00:12:05,520 --> 00:12:07,200 people coming with new approaches. 310 00:12:07,200 --> 00:12:09,560 You get people in other fields 311 00:12:11,200 --> 00:12:13,840 just being able to work collaboratively 312 00:12:13,840 --> 00:12:15,760 with the people who have collected the data. 313 00:12:15,760 --> 00:12:18,600 And it just gets extra complicated 314 00:12:18,600 --> 00:12:21,560 and extra nuanced as a result. 315 00:12:21,560 --> 00:12:24,160 But I think this is the future of science. 316 00:12:24,160 --> 00:12:26,000 And I suppose there's two important distinctions. 317 00:12:26,000 --> 00:12:28,000 One is you mentioned $5 million, 318 00:12:28,000 --> 00:12:30,000 which is not atypical. 319 00:12:30,000 --> 00:12:32,440 That $5 million comes from the American taxpayer. 320 00:12:32,440 --> 00:12:33,280 That's right. 321 00:12:33,280 --> 00:12:35,280 And they want value for money, right? 322 00:12:35,280 --> 00:12:39,440 So having those data looked at by one mind 323 00:12:39,440 --> 00:12:42,040 is nothing like having it looked at by 324 00:12:42,040 --> 00:12:44,680 all the brilliant minds that could potentially leverage it. 325 00:12:44,680 --> 00:12:45,520 That's right. 326 00:12:45,520 --> 00:12:47,040 But another piece, and then this goes to 327 00:12:47,040 --> 00:12:48,680 what I wanna talk to you about next, 328 00:12:48,680 --> 00:12:50,360 which is the diversity and inclusion, 329 00:12:50,360 --> 00:12:53,720 is there are many people who don't live 330 00:12:53,720 --> 00:12:55,320 at a major academic center like 331 00:12:55,320 --> 00:12:57,360 the University of California in Los Angeles 332 00:12:57,360 --> 00:12:58,560 or the University of Rochester 333 00:12:58,560 --> 00:13:01,160 where we have phenomenal resources. 334 00:13:01,160 --> 00:13:03,640 But there are great scientists and great minds out there 335 00:13:03,640 --> 00:13:06,160 at smaller places that don't have those resources 336 00:13:06,160 --> 00:13:07,720 that can't get data like these. 337 00:13:07,720 --> 00:13:09,720 So this provides that opportunity for them. 338 00:13:09,720 --> 00:13:10,840 Exactly, right. 339 00:13:10,840 --> 00:13:12,040 Yeah, that's what it did for me 340 00:13:12,040 --> 00:13:14,200 because I started my lab at the University of Miami 341 00:13:14,200 --> 00:13:15,200 where they were just starting 342 00:13:15,200 --> 00:13:16,960 a neuroimaging center at the time. 343 00:13:16,960 --> 00:13:19,160 So while I was waiting for things to pick up, 344 00:13:19,160 --> 00:13:22,280 getting our scanner going, everybody's writing grants, 345 00:13:22,280 --> 00:13:25,000 during that time I was able to analyze a lot of data sets 346 00:13:25,000 --> 00:13:26,280 that have been made available 347 00:13:26,280 --> 00:13:28,040 through these open science efforts. 348 00:13:28,040 --> 00:13:28,880 One of them was called 349 00:13:28,880 --> 00:13:32,240 the Autism Brain Imaging Data Exchange, ABIDE. 350 00:13:32,240 --> 00:13:34,840 And so that's another one where researchers 351 00:13:34,840 --> 00:13:38,400 across the world really were putting their data together 352 00:13:38,400 --> 00:13:39,960 in one place and you could download it, 353 00:13:39,960 --> 00:13:42,640 you could use it to look at brain connectivity 354 00:13:42,640 --> 00:13:43,880 and its development in autism, 355 00:13:43,880 --> 00:13:45,960 which is what I did for many years in the beginning, 356 00:13:45,960 --> 00:13:47,760 while waiting for our own grants to start, 357 00:13:47,760 --> 00:13:49,920 while waiting and doing our own data collection. 358 00:13:49,920 --> 00:13:51,480 So it really has just changed, 359 00:13:51,480 --> 00:13:53,280 I think, the way people do science now. 360 00:13:53,280 --> 00:13:56,040 You just see more and more of these large data, 361 00:13:56,040 --> 00:13:58,120 and not to mention the statistical power, 362 00:13:58,120 --> 00:14:00,880 the increase you get with looking at hundreds of subjects 363 00:14:00,880 --> 00:14:03,120 instead of looking at tens of subjects. 364 00:14:03,120 --> 00:14:04,680 So it's just been a win-win. 365 00:14:04,680 --> 00:14:05,520 It has. 366 00:14:05,520 --> 00:14:08,480 And it's a nice segue into this issue of diversity 367 00:14:08,480 --> 00:14:12,600 because another key component of the ABCD study, right, 368 00:14:12,600 --> 00:14:15,880 in the old days, an investigator would do 369 00:14:15,880 --> 00:14:17,560 a functional imaging study, 370 00:14:17,560 --> 00:14:19,880 at best they'd have 20 or 30 people in there 371 00:14:19,880 --> 00:14:22,760 and they probably all came from the local school, right? 372 00:14:22,760 --> 00:14:25,960 Or some organization, somebody's book club, 373 00:14:25,960 --> 00:14:29,760 but ABCD changes that, right? 374 00:14:29,760 --> 00:14:30,920 Do you wanna speak to that? 375 00:14:30,920 --> 00:14:33,360 Well, it tries very hard to match 376 00:14:33,360 --> 00:14:36,000 the racial and ethnic diversity 377 00:14:36,000 --> 00:14:37,280 that we have in the United States. 378 00:14:37,280 --> 00:14:40,600 So it tries very hard to sort of get the percentages right. 379 00:14:40,600 --> 00:14:42,560 And that brings with it extra challenges 380 00:14:42,560 --> 00:14:45,680 because trying to get into, 381 00:14:45,680 --> 00:14:48,480 get participants who maybe don't have as many resources 382 00:14:48,480 --> 00:14:50,880 to say, hey, I'm gonna give you five hours out of my day 383 00:14:50,880 --> 00:14:53,120 to do a study, it's a different set of challenges 384 00:14:53,120 --> 00:14:56,760 than saying getting a college student to come in to do a study. 385 00:14:56,760 --> 00:15:01,760 So this project is funny because I'm a newcomer to ABCD myself. 386 00:15:02,480 --> 00:15:04,960 So I joined UCLA two years ago 387 00:15:04,960 --> 00:15:08,040 and sort of was immediately sucked into this world 388 00:15:08,040 --> 00:15:10,200 of this big grant. 389 00:15:10,200 --> 00:15:11,800 And then just a year ago, 390 00:15:11,800 --> 00:15:13,920 I was appointed as the Associate Director 391 00:15:13,920 --> 00:15:16,160 for Justice, Equity, Diversity, and Inclusivity 392 00:15:16,160 --> 00:15:19,880 for all of ABCD, which means now I'm talking with everybody, 393 00:15:19,880 --> 00:15:21,360 all the 21 sites looking at 394 00:15:21,360 --> 00:15:23,200 what their local recruitment efforts are, 395 00:15:23,200 --> 00:15:25,840 what are the difficulties they're having with retention, 396 00:15:25,840 --> 00:15:29,080 especially from participants in minoritized populations. 397 00:15:29,080 --> 00:15:32,720 All of, lots of issues related to justice, equity, 398 00:15:32,720 --> 00:15:34,560 diversity, and inclusivity in all of the study 399 00:15:34,560 --> 00:15:37,880 have now sort of fallen under my umbrella. 400 00:15:37,880 --> 00:15:41,000 And now I have a team of individuals who I work with 401 00:15:42,000 --> 00:15:44,200 to sort of spread the Jedi efforts 402 00:15:44,200 --> 00:15:45,920 all throughout the consortium. 403 00:15:45,920 --> 00:15:49,040 So that has been quite an interesting challenge. 404 00:15:49,040 --> 00:15:51,680 It's a tour de force and a real challenge 405 00:15:51,680 --> 00:15:54,200 because I suppose, you know, 406 00:15:54,200 --> 00:15:58,200 having been early enough into, in the ABCD, 407 00:15:58,200 --> 00:16:00,600 you know, people started out with the best intentions. 408 00:16:00,600 --> 00:16:01,440 Of course. 409 00:16:01,440 --> 00:16:05,120 But of course, I don't think we had a real appreciation 410 00:16:05,120 --> 00:16:07,280 for the true barriers that are right there 411 00:16:07,280 --> 00:16:09,920 for people who come from hardscrabble backgrounds, 412 00:16:09,920 --> 00:16:12,040 who have, don't have the economic means 413 00:16:12,040 --> 00:16:14,600 for them to stay involved in the study, 414 00:16:14,600 --> 00:16:17,200 the mobility in those communities, 415 00:16:17,200 --> 00:16:19,080 the lack of a bus fare. 416 00:16:19,080 --> 00:16:22,400 This comes down to such basic simple things 417 00:16:22,400 --> 00:16:24,640 that militate against participation 418 00:16:24,640 --> 00:16:27,640 and how intentional we need to be as a community. 419 00:16:27,640 --> 00:16:28,480 Yeah, yeah. 420 00:16:28,480 --> 00:16:30,800 It's, for me, it's been a whirlwind tour 421 00:16:30,800 --> 00:16:34,480 of just learning while trying to do justice to this role, 422 00:16:34,480 --> 00:16:38,200 which I think was very forward thinking 423 00:16:38,200 --> 00:16:39,200 for the directors to say, 424 00:16:39,200 --> 00:16:42,040 hey, we actually need an entire person devoted to thinking 425 00:16:42,040 --> 00:16:43,880 about these issues, an entire team 426 00:16:43,880 --> 00:16:45,360 that's now funded to do that. 427 00:16:45,360 --> 00:16:47,320 So, you know, we've talked a lot 428 00:16:47,320 --> 00:16:51,600 throughout our symposium today about how the funding, 429 00:16:51,600 --> 00:16:53,600 you know, bodies like National Institute of Health 430 00:16:53,600 --> 00:16:56,400 can help us think this way to be more inclusive 431 00:16:56,400 --> 00:16:57,240 in our science. 432 00:16:57,240 --> 00:16:59,760 And we just had wonderful presentations already about this. 433 00:16:59,760 --> 00:17:02,000 But part of it is just as simple as money. 434 00:17:02,000 --> 00:17:04,880 Have you provided, you know, 435 00:17:04,880 --> 00:17:07,840 means for transportation or meals during this, you know, 436 00:17:07,840 --> 00:17:10,280 things that make it easier for people to go to child care, 437 00:17:10,280 --> 00:17:13,560 even if they have other children who need, you know, care, 438 00:17:13,560 --> 00:17:16,200 and they want to bring in their adolescent to the study, 439 00:17:16,200 --> 00:17:18,080 maybe we have to provide means for that as well. 440 00:17:18,080 --> 00:17:20,880 So, I mean, just thinking these issues through 441 00:17:20,880 --> 00:17:21,800 is a big step. 442 00:17:21,800 --> 00:17:25,120 Right, and taking off hours of a work day for, you know, 443 00:17:25,120 --> 00:17:28,200 when you're a professional who can get time off, 444 00:17:28,200 --> 00:17:30,400 but if you're a shift worker, you know, it's- 445 00:17:30,400 --> 00:17:31,560 It's hard. 446 00:17:31,560 --> 00:17:33,920 You know, we were talking about best intentions, 447 00:17:33,920 --> 00:17:34,880 but at the end of the day, 448 00:17:34,880 --> 00:17:38,080 you have to actually deploy resources 449 00:17:38,080 --> 00:17:40,560 and human power and knowledge. 450 00:17:40,560 --> 00:17:41,880 And I think, you know, you're at the point, 451 00:17:41,880 --> 00:17:42,960 the end of the stick on this. 452 00:17:42,960 --> 00:17:45,680 And I think as an ABCD investigator, 453 00:17:45,680 --> 00:17:49,120 we really appreciate what you and the team are doing. 454 00:17:49,120 --> 00:17:50,800 No, it's a tall order, 455 00:17:50,800 --> 00:17:53,120 and that's why we really need buy-in from everybody. 456 00:17:53,120 --> 00:17:54,120 And that's part of what it is, 457 00:17:54,120 --> 00:17:56,760 is that everyone at ABCD and studies like this, 458 00:17:56,760 --> 00:18:00,960 we've all realized we don't live in a academic ivory 459 00:18:00,960 --> 00:18:02,760 tower bubble, or maybe we do, 460 00:18:02,760 --> 00:18:05,760 but if we want to really make the kind of impact 461 00:18:05,760 --> 00:18:07,160 that we are hoping to make, 462 00:18:07,160 --> 00:18:09,760 we just have to take all of these factors 463 00:18:09,760 --> 00:18:11,120 into consideration. 464 00:18:11,120 --> 00:18:13,440 And I mean, just coming back to where we started, 465 00:18:13,440 --> 00:18:15,320 does your Bangladeshi background, 466 00:18:15,320 --> 00:18:16,480 your immigrant background, 467 00:18:16,480 --> 00:18:19,800 do you feel like that's helped you in this role? 468 00:18:19,800 --> 00:18:20,640 I mean, for sure. 469 00:18:20,640 --> 00:18:22,640 I think part of what we're combating now 470 00:18:22,640 --> 00:18:24,560 is also this idea of medical mistrust, 471 00:18:24,560 --> 00:18:26,520 or just mistrust in science. 472 00:18:26,520 --> 00:18:28,680 And there's a lot of reasons why some communities 473 00:18:28,680 --> 00:18:30,600 would not want to come in and participate 474 00:18:30,600 --> 00:18:31,880 in a research study. 475 00:18:31,880 --> 00:18:34,160 And it helps me to understand some of that. 476 00:18:34,160 --> 00:18:36,520 I can tell you, like, as an immigrant, 477 00:18:36,520 --> 00:18:38,280 we only became citizens, you know, 478 00:18:38,280 --> 00:18:40,240 through the naturalization process. 479 00:18:40,240 --> 00:18:42,880 And you may not want to, you know, 480 00:18:42,880 --> 00:18:46,320 to reveal to an institution what your immigration status is 481 00:18:46,320 --> 00:18:48,320 in the United States, contentious issues. 482 00:18:48,320 --> 00:18:50,400 And there's a lot of things going on right now 483 00:18:50,400 --> 00:18:52,800 with sexual and gender minorities in some states 484 00:18:52,800 --> 00:18:54,640 in the United States where it's becoming more 485 00:18:54,640 --> 00:18:57,520 and more dangerous for those individuals to exist. 486 00:18:57,520 --> 00:18:59,600 And so, I mean, our study, 487 00:18:59,600 --> 00:19:01,960 we might ask these questions that some people 488 00:19:01,960 --> 00:19:03,320 are even afraid to answer. 489 00:19:03,320 --> 00:19:05,320 So, you know, just being sensitive to, you know, 490 00:19:05,320 --> 00:19:07,920 like, the things we're asking, we're doing this 491 00:19:07,920 --> 00:19:10,040 because we want to help these communities, 492 00:19:10,040 --> 00:19:12,320 but at the same time, we have to build trust 493 00:19:12,320 --> 00:19:14,560 with the communities so that they know, 494 00:19:14,560 --> 00:19:17,160 hey, we're doing this for you, not, you know, 495 00:19:17,160 --> 00:19:18,800 it's for some nefarious purpose. 496 00:19:18,800 --> 00:19:20,200 Right, right, right. 497 00:19:20,200 --> 00:19:22,200 And I would say, there's a saying, right, 498 00:19:22,200 --> 00:19:25,680 you know, the strength of science is in its diverse minds 499 00:19:25,680 --> 00:19:28,080 and bringing people from different backgrounds together. 500 00:19:28,080 --> 00:19:30,760 And I personally believe that's one of the great achievements 501 00:19:30,760 --> 00:19:34,000 of the American science engine is that it took minds 502 00:19:34,000 --> 00:19:37,120 and brilliance from wherever it could get it. 503 00:19:37,120 --> 00:19:40,640 And I rue the day that they would decide not to do that 504 00:19:40,640 --> 00:19:43,920 because that is what's driven innovation in this country. 505 00:19:43,920 --> 00:19:45,880 Now, you know, I really appreciate that. 506 00:19:45,880 --> 00:19:48,720 I appreciate you wading into 507 00:19:48,720 --> 00:19:50,880 what is difficult political territory. 508 00:19:50,880 --> 00:19:52,800 It's not easy to talk about. 509 00:19:52,800 --> 00:19:55,880 I always like to close with this question, you know, 510 00:19:55,880 --> 00:19:57,800 for youngsters up and coming, you know, 511 00:19:57,800 --> 00:19:59,960 you've traveled a certain path, you know, 512 00:19:59,960 --> 00:20:03,440 from an immigrant family of the humanities and so on. 513 00:20:03,440 --> 00:20:05,640 When you turn around and you talk to your graduate students 514 00:20:05,640 --> 00:20:08,880 or even if you're out in the school system, 515 00:20:08,880 --> 00:20:10,480 what do you say to youngsters? 516 00:20:10,480 --> 00:20:12,000 What do you say about, you know, 517 00:20:12,000 --> 00:20:14,440 do you have some nuggets tips for life? 518 00:20:14,440 --> 00:20:15,680 For life? Oh, wow. 519 00:20:15,680 --> 00:20:17,480 Or the academy? 520 00:20:17,480 --> 00:20:21,680 It's interesting because I was not by any means 521 00:20:21,680 --> 00:20:24,080 a traditional success story. 522 00:20:24,080 --> 00:20:27,560 I think it took a long time for me to get off the ground. 523 00:20:27,560 --> 00:20:28,720 I mean, in terms of, you know, 524 00:20:28,720 --> 00:20:30,840 I was a postdoc for something like seven years, 525 00:20:30,840 --> 00:20:32,920 which may be more typical nowadays, 526 00:20:32,920 --> 00:20:37,400 but just a larger commentary on academia in general. 527 00:20:37,400 --> 00:20:39,800 But, you know, I didn't think that I would succeed 528 00:20:39,800 --> 00:20:40,680 in this career path. 529 00:20:40,680 --> 00:20:42,960 There was no indication I would for many, many years. 530 00:20:42,960 --> 00:20:44,280 I struggled. 531 00:20:44,280 --> 00:20:46,360 I didn't get my first faculty position 532 00:20:46,360 --> 00:20:49,000 until, you know, about 10 years ago. 533 00:20:49,000 --> 00:20:50,840 It's a lot of investment that you put in, 534 00:20:50,840 --> 00:20:52,640 not sort of knowing what will come out the other end, 535 00:20:52,640 --> 00:20:56,120 but, you know, and people will give you all kinds of advice, 536 00:20:56,120 --> 00:20:57,960 but it's kind of the survivorship bias. 537 00:20:57,960 --> 00:20:59,400 Like this worked for me. 538 00:20:59,400 --> 00:21:02,280 That doesn't necessarily mean it'll work for anybody else. 539 00:21:02,280 --> 00:21:07,280 But one thing that I found that I couldn't move away from 540 00:21:07,400 --> 00:21:10,760 was this kind of like, I have a unique perspective 541 00:21:10,760 --> 00:21:12,880 and I'm just gonna keep doing the thing 542 00:21:12,880 --> 00:21:14,560 that I think needs to be done, 543 00:21:14,560 --> 00:21:16,440 whether it's looking at spontaneous brain activity 544 00:21:16,440 --> 00:21:18,160 when nobody else cared about that, 545 00:21:18,160 --> 00:21:19,480 or, you know, whether it's, you know, 546 00:21:19,480 --> 00:21:21,600 looking at certain populations using, you know, 547 00:21:21,600 --> 00:21:22,680 certain approaches. 548 00:21:22,680 --> 00:21:24,720 I have a unique perspective and I'm gonna be here 549 00:21:24,720 --> 00:21:27,320 and I'm gonna bring that perspective to academia, 550 00:21:27,320 --> 00:21:29,000 whether they want it or not. 551 00:21:29,000 --> 00:21:32,240 It turns out eventually they do want it because, you know, 552 00:21:32,240 --> 00:21:36,960 sometimes if you're not from the majority group, 553 00:21:36,960 --> 00:21:38,600 you don't see anyone else that looks like you, 554 00:21:38,600 --> 00:21:40,400 you think, well, I'm the odd one out. 555 00:21:40,400 --> 00:21:41,560 I must be wrong. 556 00:21:41,560 --> 00:21:44,000 But the truth is, you know, that as we've talked about, 557 00:21:44,000 --> 00:21:45,640 you know, the diversity is what like drives 558 00:21:45,640 --> 00:21:47,000 the innovation in science. 559 00:21:47,000 --> 00:21:49,400 And so when you, you know, you stick it out, 560 00:21:49,400 --> 00:21:53,920 you stick to your, you know, try to bring people along, 561 00:21:53,920 --> 00:21:55,920 I guess, to your perspectives. 562 00:21:55,920 --> 00:21:59,160 And in the long run, it's very satisfying, 563 00:21:59,160 --> 00:22:01,360 but it is a uphill battle. 564 00:22:01,360 --> 00:22:03,280 And the only thing that sort of keeps you going 565 00:22:03,280 --> 00:22:05,920 is the mentors and allies and friends and collaborators 566 00:22:05,920 --> 00:22:08,440 that you meet along the way who support you in that path. 567 00:22:08,440 --> 00:22:11,600 So there's the, both sort of don't lose your hope, 568 00:22:11,600 --> 00:22:13,400 even though it sometimes looks bleak, 569 00:22:13,400 --> 00:22:15,560 but also like rely on those friends and connections 570 00:22:15,560 --> 00:22:19,800 and collaborations because that's what makes it all possible 571 00:22:19,800 --> 00:22:20,640 and worthwhile. 572 00:22:20,640 --> 00:22:21,720 That's fantastic. 573 00:22:21,720 --> 00:22:23,960 You know, we hear this again and again, 574 00:22:23,960 --> 00:22:26,120 when I ask this question, mentors, 575 00:22:26,120 --> 00:22:27,960 the people that give you a leg up in life. 576 00:22:27,960 --> 00:22:30,520 And what I hear is perseverance, 577 00:22:30,520 --> 00:22:32,880 single-mindedness and hard work. 578 00:22:32,880 --> 00:22:34,440 Well, the mentors and it's true, 579 00:22:34,440 --> 00:22:37,720 you can't do it without mentors, colleagues, friends. 580 00:22:37,720 --> 00:22:39,720 Lucina, thank you very much for taking time out. 581 00:22:39,720 --> 00:22:41,560 It's really been a pleasure to chat with you 582 00:22:41,560 --> 00:22:43,440 and to introduce you to our audience. 583 00:22:43,440 --> 00:22:44,280 Thank you. 584 00:22:44,280 --> 00:22:45,120 It's been a pleasure. 585 00:22:45,120 --> 00:22:45,960 Thank you so much. 586 00:22:45,960 --> 00:22:46,800 Awesome. 587 00:22:46,800 --> 00:23:04,360 it's amazing.