Imaging the developing and aging brain: Structural and functional connectivity

Lars T. Westlye CSHC / Centre for Advanced Study PSY4303 10.02.2012 [email protected]

Outline

Brain and cognitive plasticity: investigating individual and age differences

Network modeling of the developing and aging brain using resting state fMRI

Structural connectivity: diffusion tensor imaging [DTI]

Example application: DTI and interindividual differences in reaction time variability (if time!)

The plastic brain

Manifestations of plasticity through the brush of an artist At the age of 75, the german painter Anton Raderscheidt suffered from a stroke affecting his right parietal cortex, causing hemispatial neglect.

In the earliest paintings after the incidence he practically ignored one side of his face. He gradually improved over the next few months.

Manifestations of plasticity through the brush of an artist At the age of 75, the german painter Anton Raderscheidt suffered from a stroke affecting his right parietal cortex, causing hemispatial neglect.

In the earliest paintings after the incidence he practically ignored one side of his face. He gradually improved over the next few months. Right parietal stroke

The major lobes of the brain

Hemispatial neglect (right-sided lesion)

Manifestations of plasticity through the brush of an artist At the age of 75, the german painter Anton Raderscheidt suffered from a stroke affecting his right parietal cortex, causing hemispatial neglect.

In the earliest paintings after the incidence he practically ignored one side of his face. He gradually improved over the next few months.

Self portraits of Anton Räderscheidt recovering from hemispatial neglect (1968) http://www.raederscheidt.com

Manifestations of plasticity through the brush of an artist Cognitive and brain plasticity are manifested in many ways. The self portraits painted in the months after the stroke provide a powerful and vivid view of the spontaneous recovery of brain functions, and hints at the remarkable ability of the brain to re-organize and regain function after a serious insult. The inherent potential of functional and structural plasticity of the brain, that is, the dynamic and continuous adaption to contextual demands, is a core property of cognitive and brain function both in health and disease.

The bright and the dark side of plasticity: extensive musical practice

Somatotopic mapping – tactile representations in the brain

Sensory/tactile representations are strictly organised in the somatosensory cortex

Is the somatotopic organization modified by extensive practice?

Somatotopic remapping in violin players (Elbert et al., 1995)

Main findings: Cortical representations of the left fingers are enlarged. No differences were observed for the representations of the right fingers, which control the bow. Degree of cortical remapping correlated with the age at which the person had started practice (note the larger remapping for musicians initiating practice before the age of 12) The size of the representation of different parts of the body in the primary somatosensory cortex changes in accord to current needs and experiences. The potential for plasticity may decrease with age

Focal hand dystonia - somatotopic remapping gone wrong

Sustained muscle contractions causing twisting and repetitive movements or abnormal postures

Loss of control of the 4th and 5th fingers in flexion

Thumb clenched inside the hand

Thumb in extension

Focal hand dystonia - somatotopic remapping gone awry

Elbert et al., 1998

Reduced distance between the representational zones of the digits in somatosensory cortex for the affected hand of dystonic musicians. Cause “fusion” of the digital receptive fields.

The fused zones can be re-differentiated through systematic training which will alleviate/remove the symptoms.

Constraints and facilitators of plasticity – gains and losses

Constraints and facilitators of plasticity – gains and losses The range of cognitive and brain plasticity is constrained and facilitated by a) Environmental influences on level of neurotrophins and vascular supply (influenced by fitness and other life-style factors) b) Genetic influences (brain-derived neurotrophic factor (BDNF), APOE etc) c) Behaviour (“use it or loose it” as opposed to “wear and tear”, enrichment hypothesis)

Since these variables vary between individuals and as a function of age, considerable individual and age differences in the range of plasticity are expected (e.g. manifested as age-differences in behavioral improvements in response to cognitive training)

Identifying, characterizing and limiting the influence of the constraining factors is a major area of research within the neurosciences. This is due to the fact that decreased neuroplasticity is assumed to play a key role in various neurodegenerative and psychiatric disorders, including Alzheimer’s disease.

Identifying and understanding the causes of individual differences is KEY to further our understanding of clinical conditions, including age-relating cognitive decline, dementia (e.g. Alzheimer’s disease), developmental disorders (ADHD etc), and psychiatric conditions (schizophrenia, affective disorders etc)

Does cognitive plasticity change with age? Implications for studies of cognitive aging

Does cognitive plasticity change with age? Not a new question:

[…] plasticity of the cell processes is likely to vary in different life periods: conspicuous in youngsters, it decreases in adults and disappears almost completely in old age (Cajal, 1894).

Ramon y Cajal

The plasticity of the nervous elements […] decreases as the years go by […] and reaches an almost complete annulment in a variable time from one individual to the other (Lugaro, 1898).

Ernesto Lugaro

Testing the limits of plasticity Intensive training in the mnemonic technique Method of Loci resulted in improvements in both age groups (16 y: 20-30 y, 19 o: 60-80 y) and strong age differences in learning rate and upper limits of performance. No baseline differences, but training led to a strong group difference in post-training performance. Substantial cognitive plasticity in both age groups but decreased plasticity in the old compared to the young group

Baltes and Kliegl (1992), in Herzog et al., 2010

Age-differences in cognitive functions

Park & Reuter-Lorenz, 2009

Cross-sectional studies show strong age-dependencies except for knowledge representations. Linear and parallel decreases in mental speed, working- and long-term memory from ~20 years of age

BUT: Cross-sectional designs effectively mask considerable individual variability in the trajectories

Buckner, 2004

Methodological issues in cognitive neuroscience (1) We are often interested in phenomena we cannot measure directly (”thinking”, ”cognition”, ”feelings”, ”percepts”), and we need to combine measures from various scientific levels and disciplines (genetics, biomarkers, brain imaging, cognitive tests, surveys, introspection etc) (2) The different scientific levels do not necessarily communicate and translate very well

Example: genetic influences on mood

Methodological issues in cognitive neuroscience (1) We are often interested in phenomena we cannot measure directly (”thinking”, ”cognition”, ”feelings”, ”percepts”), and we need to combine measures from various scientific levels and disciplines (genetics, biomarkers, brain imaging, cognitive tests, surveys, introspection etc) (2) The different scientific levels do not necessarily communicate and translate very well

Example: genetic influences on mood Gene

Brain

How do genes affect brain structure?

Mood

How does brain structure affect mood?

Increasing the knowledge about these associations can help A MAJOR aim within the neurosciences is to develop and validate methods that are sensitive to relevant variability at the different levels, and next relating the different types of data, both in healthy and clinical groups (1) Genetic markers (2) Structural and functional brain imaging (3) Cognitive/emotional measures Gene

Brain

How do genes affect brain structure?

Mood

How does brain structure affect mood?

BUT: Cross-sectional designs effectively mask considerable individual variability in the trajectories

How can we bridge the gaps using advanced brain imaging?

How can we bridge the gaps using advanced brain imaging?

Neuroimaging

Functional

EEG

fMRI

PET

Structural

MEG

sMRI/ DTI

CT

All methods have their strenghts and weaknesses

Magnetic resonance imaging (MRI)

Allows for non-invasive images of the living human brain Safe – can be repeated multiple times A range of different image contrasts can be obtained, all of which provide complementary information about brain structure and function

http://www.howstuffworks.com/mri.htm

Functional MRI (fMRI)

Sensitive to hemodynamic changes in the human brain – a proxy for neuronal activity (the neurovascular coupling is extremely complex and far from understood) Allows for continuous sampling of “brain activity” – one sample approx every 2-3 seconds (slow!)

http://www.howstuffworks.com/fmri.htm

Functional MRI (fMRI)

http://www.howstuffworks.com/fmri.htm

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Measuring brain activity in the ”resting state”

Whilst part of what we perceive comes through our senses from the object before us, another part (and it may be the larger part) always comes out of our own head William James, 1890

Measuring brain activity in the ”resting state”

Girl, 6 years old, data from a 5 min BOLD/fMRI session, one sample every 3rd second (TR=3s)

Instructions: ”keep still with eyes open”

How do we make sense of this?

Girl, 6 years old, data from a 5 min BOLD/fMRI session, one sample every 3rd second (TR=3s)

Instructions: ”keep still with eyes open”

“Resting state” brain activity is highly organised

Prefrontal http://www.youtube.com/watch?v=05j-TvKnFaA&feature=plcp&context=C3532500UDOEgsToPDskLVDAGiRIGmkt2lhoiOnadU

Visual http://www.youtube.com/watch?v=Y9O8eAUS9zw

Independent component analysis (ICA) A computational method for separating a multivariate signal into additive and statistically independent subcomponents Originally proposed to solve blind source separation or so-called cocktailparty problems

Allows “blind separation” of N sound sources summed in recordings at N microphones, without relying on a detailed model of the sound characteristics of each source or the mixing process:

Example: Sound separation

ICA

Courtesy of Arno Delorme, UCSD

How do we make sense of this?

Girl, 6 years old, data from a 5 min session

Instructions: ”keep still with eyes open”

Patterns of brain activation during rest

Functional networks

Hierarchical clustering

The resting brain is highly organized into functional hierarchical networks

How do we make sense of this?

Brain imaging data are high-dimensional and multivariate in nature, i.e. the estimated signal could be regarded as a mixture of many independent sources

The case of EEG

Spatial group ICA on temporally concatenated FMRI data

Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC)

Beckmann et al.

The IC maps reflect intrinsic patterns of functional organization across subjects and correspond with known neuroanatomical and functional brain ”networks”

Veer et al., 2010

How do we get from the group level to the subject level?

Dual regression allows for estimations of subect-specific spatial maps and corresponding time courses Spatiotemporal regression in two steps:

A) Use the group-level spatial maps as spatial regressors to estimate the temporal dynamics (time courses) associated with each gICA map

B) Use time courses (after optional normalization to unit variance) spatial regressors to find subject-specific maps associated with the group-level maps.

Spatiotemporal regression:

Yielding n × d time courses, where n = number of subjects and d = model order (number of ICs). The covariance of the time courses reflect the large-scale functional connectivity of the brain, and can be submitted to various connectivity analysis - and subsequent analysis with relevant demographic, cognitive and genetic data

Does the organization of the resting state networks change in aging?

Application: Modelling the effects of age across the adult lifespan

N

Mean

Min

Max

SD

222

50.2

21.1

81.2

16.8

Imaging data: 1.5 T Siemens Avanto, 10 min resting state fMRI (200 TRs) Conventional preprosessing including motion correction, filtering etc Group ICA (94 subjects to avoid bias due to) using melodic (d=80) and dual regression in order to estimate subject-specific time courses of each IC. Removal of 43 ICs reflecting motion artefacts, pulsation etc yielded 36 RSNs

Resting state networks

Hierarchical clustering of the connectivity matrix across subjects

Hierarchical clustering of the connectivity matrix across subjects

DMN

Motor

Visual

Hierarchical clustering recovers large-scale brain networks

Visual

Motor

The connectivity matrix across subjects

Full correlations

Partial correlations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage)

The connectivity matrix (across subjects)

Partial correlations (ICOV, lambda=10) (see Smith et al., 2010, NeuroImage)

Full correlations

Direct links

Direct + indirect links

The connectivity matrix (across subjects)

Network modelling using full correlation (strongest edges shown only)

Modelling effects of age

Direct links

Direct + indirect links

Modelling effects of age

Subject-specific connectivity matrices (n = 222)

Modelling effects of age (edges showing abs(tage>7))

Network modelling

Dominantly positive correlations between edge strengths and age: dedifferentiation of functional networks in the aging brain

Network modelling

Interim conclusion: Increased correlations between functional brain networks with increasing age from 21 to 81 years of age. Is functional dedifferentiation, i.e. less specialized networks?

What about the maturing brain during childhood/adolescence?

Application: Modelling the effects of age in childhood and adolescence

N

Mean

Min

Max

SD

210

13.2

4.4

21.9

5.0

Imaging data: 1.5 T Siemens Avanto, 5 min resting state fMRI (100 TRs) Conventional preprosessing including motion correction, filtering etc Group ICA using temporal concatenation in melodic (d=60) and dual regression in order to estimate subject-specific time courses of each IC. Removal of 26 ICs reflecting motion artefacts, pulsation etc yielded 34 resting state networks (RSNs)

Hierarchical clustering across subjects

Modelling effects of age

Subject-specific connectivity matrices (n = 210)

Modelling effects of age

Blue entries denote negative correlations with age

Modelling effects of age (Bonferroni corrected)

Network modelling

Putamen Part of the basal ganglia. Main functions are to regulate movements and influence various types of learning (wiki). Putamen differentiates from other brain networks during childhood and adolescence. A developmental marker?

Putamen: Part of the basal ganglia. Main functions are to regulate movements and influence various types of learning (wiki). Putamen dedifferentiates from other brain networks during aging. A marker of cognitive aging?

Some take home messages: (1) The resting brain is strictly organised into dynamic functional networks (2) The temporal correlations (the strength of the connections) between networks show strong age-related differences throughout the human lifespan (8-81 years) (3) Brain maturation during childhood and adolescence is characterized by decreased correlations, reflecting shaping and differentiation/increased specialization of the networks (4) Aging-related changes characterized by increased correlations between networks, reflecting dedifferentiation of specialized networks, e.g. ”functional blurring”

Questions for future research: (1) Can we use this method to detect and monitor plastic changes in the brain in response to learning, disease, or treatment? (2) Can we use this method to predict who will develop disease at a later stage? (e.g. Alzheimer’s disease) (see Westlye et al., 2011, Journal of Neuroscience [APOE/RSFC]) (3) How are these measures of functional connectivity related to other measures, e.g. genetics, cognitive functions, or other imaging measures?

Can network modeling of fMRI data be used for mind reading?

Parcellation of the brain into 90 regions of interest

Shirer W R et al. Cereb. Cortex 2011

Subject-driven episodic memory recall drives changes in whole-brain functional connectivity.

Shirer W R et al. Cereb. Cortex 2011