Florenz Poster V1 0

A dynamic causal modeling study of attention shifting between smooth pursuit and saccadic targets Ferenc Acs & Mark W. Greenlee, University of Regensburg

Overview: The purpose of this project is to explore the possibilities of closing the gap between fMRI data measurement and the construction of artificial neural networks, especially mean field networks (Brunel, 2003). These models simulate the behaviour of massive biological cell assemblies. The system dynamics of interconnected massive biological cell assemblies in human brains became measurable with functional Magnetic Resonance Imaging and the data analysis method of Dynamic Causal Modelling (Friston, 2003 & Fig. 4). DCM requires assumptions about the connectivity between brain regions, for this purpose, Krauzlis’ (2005) model of attentional eye movement processing was applied for the human brain (Fig. 1). Here are the results of a DCM group study, exploring the dynamics of certain brain regions during different attention conditions to moving stimuli.

Figure 1: The connectivity between brain regions relevant to attentional eye movement processing. This is the schematic illustration of a macaque monkey brain, because effective connectivity data for humans are still rare. (From Krauzlis, 2005)

Experimental Design: The experiment was conducted with a 3T Siemens Allegra MRT scanner (TR=2s, 32 slices, 3x3x3 mm Voxel Size). A total of 1156 functional scans were acquired, total running time was 38 minutes. The subjects saw two moving dots and a fixation cross in his visual field. The upper dot made smooth sinusoidal movements, the lower dot was the target for triple step saccades in both directions. (Fig. 2,I). A simple block design was used (Tab. 1), the Blocks differed only in the instruction for the subjects. Either the subjects had to follow one of the dots (A,B) or the subjects had to fixate the yellow cross and shift his attention towards the upper (C) or the lower (D) dot. The rest condition (R) was used to acquire a baseline, only the fixation cross was visible and the subjects had to fixate it. Occasionally, with a probability of 0.05% per frame, both dots changed their color, this was an reaction time task to test the concentration of the subjects (Fig. 2,II). Pursuit

Saccades

A

B

Attention Pursuit C

Pursuit Saccades Att. Purs. Att. Sacc. Relax

0.45

Photic Stimulation -0.98

0.06

-0.49 -----

0.78

FEF

Pursuit Saccades Att. Purs. Att. Sacc. Relax

0.16 -0.23 -0.14 -0.23 0.32

Pursuit Saccades Att. Purs. Att. Sacc. Relax

-0.04 --0.25 --0.08

Pursuit Saccades Att. Purs. Att. Sacc. Relax

Pursuit Saccades Att. Purs. Att. Sacc. Relax

LIP 0.41 -----

Pursuit Saccades Att. Purs. Att. Sacc. Relax

------

Pursuit Saccades Att. Purs. Att. Sacc. Relax

------

0.15

--

0.27

Pursuit Saccades Att. Purs. Att. Sacc. Relax

------

Pursuit Saccades Att. Purs. Att. Sacc. Relax

------

0.29

SEF

0.20 -----

Pursuit Saccades Att. Purs. Att. Sacc. Relax

Figure 2: I) The visual stimuli used in the experiment. Sinusoidal moving dots with an amplitude of 9° in the visual field and a frequency of 0.77 Hz. II) Shows the change of the stimuli for the reaction time task. This stimulus appeared with a probability of 0.05% per frame (70Hz) and lasted 200ms. The subjects had to press a button with the index finger upon detection

Results: Comparing six DCM models after parameter estimation, the model shown on the left provided most information (Fig. 3). The model comparison used pairwise testing of the models, the measures were the Bayesian Information Criterion and Akaikes Information Criterion (Penny, 2004). Nearly every connection specified in the model passed the significance threshold (>90%, Bayesian posterior probablility). The general excitatory effect of V1 to MT/MST is weakened by the conditions B,C & D and enhanced by conditions A & R. The MT/MST - LIP interaction is enhanced by pursuit stimuli (A). Attentional shifting effects (C,D) could be reproduced for the V1 -> MT/MST -> FEF pathway. However a modified model (Fig. 3, orange line) leaving LIP away and modelling a direct connection between MT/MST and SEF showed clear inhibitory effects for the attention to pursuit condition (C). Conditions A & R enhanced the connectivity.

-0.20 -0.15 --

--

0.73

MT MST

V1 Pursuit Saccades Att. Purs. Att. Sacc. Relax

-0.91 0.18 -0.15 --

0.18

II) Reaction Time Task

Table 1: The fMRI Block Design. The duration of each block was 30s, 15 volume scans were acquired per block. Each block was preceded by a display of the visual instructions for 6 seconds. The order of the blocks was permutated by the method of the latin square.

Attention Relax Saccades D R

Pursuit Saccades Att. Purs. Att. Sacc. Relax

I) Visual Stimuli

-0.18 ----

Pursuit Saccades Att. Purs. Att. Sacc. Relax

0.38

0.22 --0.47 -0.31

0.41

Figure 3: The resulting averaged DCM model. Five regions of interest have twoway connections, indicated by the black arrows connecting the circles. The numbers represent the connection strength between regions, the unit of measurement is Hz. The larger the number, the faster a modulational effect between the regions will propagate, indicating a stronger connection. The large numbers are the intrinsic connections strengths. They are modulated by the different conditions affecting the internal processing state of the system (boxes with green arrows). However the box ‘Photic Stimulation’ describes the external stimuli entering the visual system through retinal activation, these external stimuli are pertubing the system and are necessary to determine the modulational effects of internal processing states. (Subjects: SA,SB,SC,SD) The orange line, connecting MT/MST and SEF directly, lacks of physiological evidence. But the attentional shifting effect visible here, suggest that either there is a direct connection or one important area in the pathway between these regions is missing. (Subjects: SC,SD)

Discussion: The effects of the attention conditions (C,D) to the V1->MT/MST->LIP connections could be shown. However the assumed MT/MST <-> SEF connection seems to be relevant for paying attention to pursuit stimuli (C). Saccadic stimuli seem to affect the connections MT/MST -> FEF and the connections LIP -> FEF & LIP -> SEF, the activation effect might be caused by the programming of eye movement vectors. Finally attentional shifting effects could be demonstrated. Effects for attention to pursuit stimuli (MT/MST -> SEF), evident in a four region model may be due to the fact that one important area for attentional modulation is be missing.

Table 2: The MNI coordinates of the regions of interest analysis for each subject (SA, SB, SC & SD)

V1: data and model predictions

FEF

SEF

hemodynamic responses 0.2

-0.2

LIP

1 0 15

10

5

MT/MST Coordinates: x: -50 y: -72 z: 4 (MNI Space)

0

t-Values

-1

500

1000 1500 time (seconds}

0 -0.02

0

-0.5

-0.05

0

0

V1

1000

V1 LIP FEF SEF MTMST

1200

1300

1400

1500

1600

1700

1800

1900

2000

MTMST: data and model predictions 2 0 -2 1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

LIP: data and model predictions

10 time {seconds} neuronal responses to Relax 0.1

0

2

0

-2

0

SEF FEF LIP

-0.1

10 time {seconds} hemodynamic responses 0.5

V1 FEF SEF LIP MTMST

0

0

10 time {seconds} neuronal responses to Att_Stim 0.1

MTMST

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

2000

2 0 -2 1000

V1

1100

FEF: data and model predictions

-0.5

80 voxels in VOI at [-50 -72 4] Variance: 72.73%

-2

0

0

SEF FEF V1 LIP

MTMST

2000

V1 LIPFEFSEF MTMST

10 time {seconds} neuronal responses to Saccades 0.05

10 time {seconds} hemodynamic responses 0.5

MT MST

-2

FEFSEF V1 LIP

0

1

-1

MTMST

Fixation Pursuit Saccadic

Tracking Pursuit Saccadic

0.02

10 time {seconds} hemodynamic responses 0.5

1st eigenvariate: MT/MST

2

0

MTMST

0

2

neuronal responses to Pursuit 0.04

0

V1

0

LIPFEFSEF

1100

1200

V1 LIPFEFSEF MTMST

1300

1400

1500

1600

1700

1800

1900

2000

1800

1900

2000

SEF: data and model predictions 2

-0.5

0

-0.1

10 time {seconds}

0

10 time {seconds}

0 -2 1000

a)

b)

c)

d)

Figure 4: Schematical steps for a DCM fMRI single subject analysis: a) After a conventional GLM analysis a region of interest has to be marked, in this picture it is the MT/MST area in a human brain. b) The volume time course if this region is extracted. It consists of the 1’st eigenvariate of all BOLD signals of the Voxels within this region. Steps a) and b) have to be repeated for every Region of Interest. c) A DCM model has to be specified. The connectivity in the model should be evident from theoretical assumptions and knowledge about the effective connectivity between these Regions. d) The parameter estimation, estimates the neural activity for each region from the BOLD response. During several iterative computing steps the model is fitted on the data until: e) The model data (red) approximate the measured BOLD time course (blue) according to a gradient descent algorithm using a Laplace approximation.

1100

1200

1300

1400

1500 1600 time {seconds}

1700

e)

Literature: Brunel, N. (2003), ‘Dynamics and Plasticity of Stimulus-selective Persistent Activity in Cortical Network Models’, Cerebral Cortex, 13(11), 1151-1161 Friston, K.J., Harrison, L. & Penny, W. (2003), 'Dynamic causal modelling', NeuroImage, 19 (4), 1273- 1302 Krauzlis, R.J. (2005), ‘The control of voluntary eye movements: New perspectives.’ The Neuroscientist, 11: 124-137 Penny, W.D., Stephan, K.E., Mechelli, A. & Friston, K.J. (2004), 'Comparing dynamic causal models', NeuroImage, 22, 1157- 1172