Thalamocortical Connectivity in Autism S

- An fcMRI and DTI Tractography

Study

Thalamocortical Connectivity

In Autism Spectrum Disorder

Aarti Nair

1,2

, Dinesh K. Shukla

1

, Jeff M.Treiber

1

,

Brandon Keehn

1

, Ralph-Axel Müller

1,2,3

1Brain Development Imaging Lab, Dept. of Psychology, SDSU 2_{Joint Doctoral Program in Clinical Psychology, SDSU/UCSD}

Background



Thalamus



Important subcortical relay structure

sensory, motor, and attentional nuclei



Thalamocortical connectivity laid out during

neuronal migration along radial glial cells

(Rakic et al. 2004)



Functional differentiation and development of

columnar architecture in cerebral cortex

(O’Leary & Nakagawa 2002)



Highly specific patterns of thalamocortical

Thalamus in ASD



Thalamic abnormalities in ASD



Reductions in volume

(Tsatsanis et al. 2003)

, neuronal

integrity

(Friedman et al 2003)

, and glucose metabolism

(Haznedar et al. 2006)



Abnormal serotonin synthesis along

cerebello-thalamo-cortical pathway

(Chugani et al. 1997)



Minicolumnar abnormalities

(Casanova et al. 2006)

–



Early-onset anomalies of thalamocortical



Assess functional connectivity and white matter

integrity between five different cortical regions of

interest (ROIs) and thalamus in adolescents with

ASD compared to TD adolescents



Cortical ROIs based on Zhang et al.(2008)



prefrontal, motor, somatosensory, temporal, and

parietal-occipital cortex

(Zhang et al. 2008)



Participants aged 12-17 years



No significant age, IQ, sex, handedness

differences



ASD diagnosed using DSM-IV, ADOS and ADI-R

TD

(n=22)

2 left-handed; 3 female

ASD

(n=18)

2 left-handed; 2 female

Mean

Range

SD

Mean

Range

SD

p

Age

14.5 12.1–

16.8

1.5 14.3 12.1-17.1 1.6 .51

IQ

109 88-126 10.7 114 87-141 14.1 .30

Data acquisition



GE 3T aMR750 scanner with 8-channel head coil



Anatomical



FSPGR T1-weighted sequence (180 slices; 1mm3 resolution)



BOLD (fcMRI)



6-minute resting-state functional EPI (180 whole-brain

volumes,TR: 2000ms; TE: 30ms; 3.4mm slice thickness;

in-plane resolution 3.4mm2)



DTI



Single-shot diffusion-weighted EPI (TR=5000 ms, TE=99.4ms,

2mm slice thickness)



Two degrees of diffusion weighting (b = 0 and 1000 s/mm2_{; 61}

Methods



ROIs b

ased on Brodmann areas identified using

Talairach-Tournoux Stereotaxic Atlas (AFNI;

TT-Daemon)



Boundaries taken from previous studies of

thalamocortical connectivity

(Zhang et al., 2008; Fair et al., 2010, Zhang et al., 2010)

Methods: fcMRI



FcMRI analysis using Analysis of Functional

NeuroImages

(AFNI; Cox, 1996)



Physiological regression (heart beat, respiration)



Field map correction



Motion correction and co-registration to anatomical image



Normalization to Talairach space



Spatial smoothing (4mm Gaussian kernal)

Methods: fcMRI



Time series extraction from cortical ROIs



Six rigid-body motion parameters and

physiological events modeled as nuisance

variables and removed with regression

Methods: DTI



Probabilistic Tractography (FMRIB Diffusion

Toolbox)



Bayesian estimation of diffusion parameters

using Monte Carlo sampling



Repetitive sampling from distributions on

voxel-wise principal diffusion directions, each time

computing a streamline through local samples



Posterior distribution on the streamline location

or the connectivity distribution generated



DTI indices:



fractional anisotropy (FA)



mean diffusivity (MD)

Z h a n g e t a l. (2 0 0 8 ) : A d u lt s

Prefrontal Parietal Motor Somatosenso

ry Temporal 4.87 0.78 p <.05 t F a ir e t a l. ( 2 0 1 0 ): A d o le s c e n ts

Results: FcMRI (TD group)



Partial correlation maps between cortical ROIs and thalamus for

p <.05

t

Prefrontal Parietal Motor Somatosenso

ry Temporal

4.87

0.78 t

T

D

4.87

0.78

A

S

D

T

D

v

s

A

S

D

3.64

-3.64 t

p <.05

t

Prefrontal Parietal Motor Somatosens

ory

Temporal

4.87

0.78

T

D

4.87

0.78

A

S

D

t

T

D

v

s

A

S

D 3.64

-3.64 t

p <.05

Prefrontal Parietal Motor Somatosens

ory

Temporal

4.87

0.78

T

D _t

4.87

0.78

A

S

D

t

T

D

v

s

A

S

D

3.64

-3.64 t

Summary of findings



FCMRI results similar to findings from prior studies in

adults and adolescents

(Zhang et al. 2008, Fair et al. 2010, Zhang et al. 2010)



ASD group showed overall reduced thalamocortical

connectivity, with some exceptions:



Temporo-thalamic connectivity atypically increased,

especially for ipsilateral right-hemisphere connections



Cortical connectivity with some thalamic sites that do not

connect in the typical brain (e.g., prefrontal connections with lateral-posterior thalamic sites)



Patterns of group differences mostly similar for both

hemispheres (except R>L effects for

temporo-thalamic connectivity in ASD)



DTI probabilistic tractography results:



Compromised thalamocortical connectivity (increased MD and

Discussion



Findings suggest overall impaired and less distinctly

segregated thalamocortical connectivity in ASD



Temporo-thalamic connections atypically enhanced

(especially in RH)



Temporal lobe structural and functional abnormalities in ASD

(e.g., Zilbovicius et al., 2000; Boddaert et al. 2004; Bigler et al., 2007; Redcay, 2008)



Superior temporal gyrus/sulcus specifically implicated in

auditory processing, imitation, biological motion perception, language, and social cognition



Typical maturational changes



Reduction in thalamo-temporal connectivity with age,

along with increase in thalamo-frontal connectivity

(Fair et al., 2010)



Thalamo-temporal overconnectivity in ASD = immature

Discussion



Findings of less distinctively segregated

thalamocortical functional connectivity may

reflect early migrational disturbances, affecting:



Formation of cortical minicolumns

(Casanova et al., 2007; 2009)



Differentiation of multipotential cortex into

functionally differentiated regions

(O’Leary & Nakagawa 2002)



For example: Functional differentiation of posterior superior

temporal sulcus reduced in ASD (Shih et al. Biol Psychiatry, in

Acknowledgments



Participants and their families



Alan Lincoln, Ph.D (recruitment and diagnostic

work)



Patti Shih, Chris Keown, Ryan Cardinale (BDIL)



Mike Datko and Jaime Pineda, Ph.D