ADOLESCENT BRAIN DEVELOPMENT Kate Mills, Jenn Pfeifer, and Nick - - PowerPoint PPT Presentation

▶
adolescent brain development
SMART_READER_LITE
LIVE PREVIEW

ADOLESCENT BRAIN DEVELOPMENT Kate Mills, Jenn Pfeifer, and Nick - - PowerPoint PPT Presentation

Sep 29, 2022 133 likes •447 views

ADOLESCENT BRAIN DEVELOPMENT Kate Mills, Jenn Pfeifer, and Nick Allen AND MENTAL HEALTH Department of Psychology University of Oregon Outline 1. Structural brain development and individual differences 2. Task-based fMRI and puberty-related

slide-1
SLIDE 1

Kate Mills, Jenn Pfeifer, and Nick Allen Department of Psychology University of Oregon

ADOLESCENT BRAIN DEVELOPMENT AND MENTAL HEALTH

slide-2
SLIDE 2
  • 1. Structural brain development and individual differences
  • 2. Task-based fMRI and puberty-related theories of mental health
  • 3. Relating brain development patterns to mental health outcomes

Outline

slide-3
SLIDE 3

Body Development

  • Characterizing typical growth
  • Identifying atypical growth
  • Example: Failure to Thrive
slide-4
SLIDE 4

Brain Development

  • Characterizing typical growth
  • Identifying atypical growth
  • Example: Schizophrenia

Tamnes et al., 2017 Data from Four Labs Collaboration

slide-5
SLIDE 5

Establishing replicable patterns of typical brain development

Samples

slide-6
SLIDE 6

Methods

  • Mixed-effects models in R
  • Best fitting model selected by AIC
  • Code available on Open Science Framework

http://surfer.nmr.mgh.harvard.edu/

Establishing replicable patterns of typical brain development

slide-7
SLIDE 7

Cortical Grey Matter Volume

Cortical Grey Matter

Mills et al., 2016; Tamnes et al., 2017

391 participants 852 scans 51% female

slide-8
SLIDE 8

Cerebral White Matter Volume

Mills et al., 2016; Tamnes et al., 2017

Cerebral White Matter 391 participants 852 scans 51% female

Reynolds et al., 2019

slide-9
SLIDE 9

Statistical analysis: Raw vs. corrected measures

Mills et al., 2016

Prefrontal Cortex 391 participants 852 scans 51% female

slide-10
SLIDE 10

Statistical analysis: Raw vs. corrected measures

Mills et al., 2016

Prefrontal Cortex 391 participants 852 scans 51% female

  • Controlling for whole brain volume reduces

magnitude of cortical volumetric development

slide-11
SLIDE 11

Regional differences in cortical development

Tamnes et al., 2017

Cortical Grey Matter 388 participants 854 scans 51% female

slide-12
SLIDE 12

Grey matter volume is the product of cortical thickness and surface area

Winkler et al., 2010

slide-13
SLIDE 13

Tamnes et al., 2017

Cortical Thickness vs. Surface Area

  • There is less inter-individual variability in cortical thickness than in

surface area

slide-14
SLIDE 14

Cortical Thickness vs. Surface Area

  • Cortical thinning is the dominant contributor to cortical volume

reductions during adolescence

Tamnes et al., 2017

slide-15
SLIDE 15

Cortical thickness decreases across adolescence

Tamnes et al., 2017 Data from Four Labs Collaboration

slide-16
SLIDE 16

Inter-individual variability in cortical thickness

Tamnes et al., 2017 Data from Four Labs Collaboration

slide-17
SLIDE 17

Drawing inferences about brain development from cross-sectional data

Paulus et al., 2019 60 Minutes, December 2018

slide-18
SLIDE 18

Aubert-Broche et al., 2013 van Soelen et al., 2013 Lenroot et al., 2007

Total Cerebral Volume

Variability between individuals > Variability within individuals

slide-19
SLIDE 19

Braintime (Leiden) Neurocognitive Development (Oslo) Pittsburgh Child Psychiatry Branch (NIMH)

Individual Variability in Cortical Grey Matter

Mills et al., 2016 Data from Four Labs Collaboration

slide-20
SLIDE 20

Cortical thickness correlates with subsequent change

Data from Four Labs Collaboration

slide-21
SLIDE 21

LunaCog data on Data Dryad Montez, Calabro, & Luna 2017

Cortical thickness correlates with subsequent change: Replication with LunaCog data

slide-22
SLIDE 22

Cortical thickness correlates with subsequent change

Data from Four Labs Collaboration

slide-23
SLIDE 23

Cortical thickness correlates with subsequent change

Data from Four Labs Collaboration

slide-24
SLIDE 24

Inter-individual variability in cortical thickness development

Tamnes et al., 2017 Data from Four Labs Collaboration

slide-25
SLIDE 25

Data from Four Labs Collaboration

Inter-individual variability in cortical thickness development

slide-26
SLIDE 26

Inter-individual variability in cortical thickness development

Data from Four Labs Collaboration

Drawing inferences about an individual’s brain maturity requires longitudinal data!

slide-27
SLIDE 27

Longitudinal brain development fMRI

  • Inter-individual variability can be greater in fMRI than sMRI
  • Variability in overall size (intercept)
  • Variability in direction and magnitude of change (slope)

Resting-state functional connectivity

Braams et al., 2015 van Duijvenvoorde et al., 2019 Mills et al., in prep

slide-28
SLIDE 28
  • Modular segregation in structural and functional connectivity

General principles about connectivity

Bassett, Xia, and Satterthwaite, 2018 Functional Structural

slide-29
SLIDE 29

Keep in Mind

  • Maybe longitudinal data are not needed if baseline data

provide the relevant information (and change does not)

  • We can only know if we test – so please do!
slide-30
SLIDE 30

PITTSBURGH

Megan Herting Christian Tamnes Rosa Meuwese Anne-Lise Goddings Eveline Crone Berna Güroğlu Sarah-Jayne Blakemore Armin Raznahan Ron Dahl Elizabeth Sowell

NIH OSLO LEIDEN

Four labs replicable brain development collaboration

Thank you!

+ Bea Luna & lab for sharing LunaCog dataset