Discussion

• Two interacting subsets of ROIs for solving the VS task at different time-scales. Blue connections are unidirectional and pink connections are bidirectional. ROIs in green (MT+, V3a) are early in the motion processing hierarchy; ROIs in pink (STP, VIP, IPS) are involved in higher level visual processing and ROIs in blue (SPL, FEF, MPFC) are involved in attention and working memory (frontoparietal). • Network of Fast, but imprecise processing of the VS task (0-500ms; Fig 7A). V3a provides computation of motion information; VIP, a large sink, processes egomotion and candidate target-sphere motion. Bidirectional interaction between VIP and SPL provides information for spatial attention shift to locate the candidate target-sphere. Last SPL instructs FEF to shift attention to the candidate target. • Network of Slow, but precise, processing of the VS task (0-1000ms;Fig 7B). Augmenting the computations seen in the network shown in Fig. 7A, area MT+ may implement temporal integration of motion that cannot be done by V3A5, which would increase precision of the motion information sent to VIP for further processing. In support of this, we suggest (Fig 6) that STP uses a template model to compute the perceptual characteristics of the target- sphere in the display and propose candidate target-sphere which sent to MT+ (at 400msec into the motion stimulus). • VIP becomes a larger sink of information, by receiving additional information from MPFC and by sending the information to STP. • IPS computes frames of reference, sends information to STP late in the stimulus. The availability of the template model for target detection and the reference frame for target location, leads to a more efficient and reliable detection of the target object

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Dynamic Granger Causality Network Snapshots

Connections combined across subjects via Fisher’s method (p<0.05), are shown. Unidirectional connections are blue and bidirectional connections are red. Thicker connections correspond to higher significance (p<0.01).

Kunjan D. Rana1, Matti Hamalainen2, Lucia M. Vaina1,2 1Brain & Vision Research Laboratory, Department of Biomedical Engineering, Boston University, Boston, MA, USA

2Massachusetts General Hospital, Harvard Medical School, Departments of Neurology & Radiology, Boston, MA, USA kdrana@bu.edu, msh@nmr.mgh,harvard.edu, vaina@bu.edu

NEW TITLE! Dynamic Granger Causality Applied to Perception of a Complex

Visual Motion Search Task

The stimuli1,2 consisted of: fade-in of nine textured spheres (1.5 degrees in diameter) (1000 ms); a display of the 9 spheres static (1000 ms); 8 of the spheres, randomly selected, simulating forward motion of the observer, and the other sphere (target) moved independently with its own speed and motion (forward or backward) (1000 ms). Then, the spheres are again shown static with numeric labels (1-4) shown on four spheres, one of which is the target which is the target (3000 ms). In a 4AFC subjects indicated via a button press during the label period.

Introduction

Perception and perceptual decisions arise from the spatiotemporal orchestration of activity distributed across brain networks. In an MEG study, we used dynamic Granger Causality and corresponding summary network measures to understand the critical cortical interactions involved in solving a complex visual-motion search task (VS). The VS task involves the detection of a moving object by a forward moving observer. In the first 500 ms of the motion stimulus, a subset of ROIs (V3a, VIP, SPL, FEF) interact to provide a fast, yet imprecise, solution to the VS task. On a longer time scale (1000 ms of the motion stimulus), a larger number of ROIs (MT+, V3a, VIP, STP, IPS, SPL, FEF, MPFC) interact to provide a more precise, but slower, solution to the VS task. This is consistent with the behavioral data obtained in the lab. Stimuli and Task

(3000 ms). Subjects indicated via a button press the number corresponding to the target sphere.

ROI Selection We obtained a set of ROIs based on clusters of activation (z > 3) on the cortical surface (excluding MT+, which was localized using an MT+ Localizer task3). To assess cross-talk between ROIs, we computed a resolution matrix showing percentage signal contribution across columns of each region of interest (ROI) activated during stimulus motion display (z>3 above baseline) (Fig 2). ROIs in black are discarded (>20% signal contribution outside of ROI from a single source or own source contributes <20% signal in measured signal), ROIs in red are joined together (mutual >20% signal contribution from each ROI), and ROIs in green are separable regions (<20% signal contribution from any other source other than ROI).

Dynamic Granger Causality and Network Sources and Sinks

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Figure 5

References

1. Vaina, L.M., et al., Long-Range Coupling of Prefrontal Cortex and Visual (MT) or Polysensory (STP) Cortical Areas in Motion Perception. BIOMAG2010, IFBME Proceedings Series, Springer Verlag IFBME., 2010. 2. Calabro, F.J., S. Soto-Faraco, and L.M. Vaina, Acoustic facilitation of object movement detection during self-motion. Proceedings of the Royal Society of London B., 2011. 3. Rana, K.D. and Vaina, L.M., Functional roles of 10 Hz alpha-band power modulating engagement and disengagement of cortical networks in a complex visual motion task. PLOS ONE (Accepted). 4. Lin, F.H., et al., Dynamic Granger-Geweke causality modeling with application to interictal spike propagation. Hum Brain Mapp, 2009. 30(6): p. 1877-86. 5. L.M. Vaina, N.M. Gryzwacz, P. Saiviroonporn, M. LeMay, D.C. Bienfang, A. Cowey. Can spatial and temporal motion integration compensate for deficits in local motion mechanisms?. Neuropsychologia, 41 (2003), pp. 1817–1836

Supported by: NIH-T90DA032484 (KDR), NIH-RO1NS064100 (LMV), NIH-P41RR14075 (MSH)

Figure 1:

Figure 6

Figure 7

Figure 2: are discarded (>20% signal contribution outside of ROI from a single source or own source contr ibutes <20% signal in measured signal), ROIs in red are joined together (mutual >20% signal contribution from each ROI), and ROIs in green are separable regions (<20% signal contribution from any other source other than ROI).

Final ROIs set after r e m o v a l o f h i g h cross-talk regions or p o o r l y l o c a l i z e d sources, and joining into a single ROI mutually high cross-talk regions.

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Legend: Cinf – Inferior Central Sulcus, Csup – Superior Central Sulcus, DIPSM – Dorsal intraparietal sulcus middle, IPSsup – superior Intraparietal Sulcus, IPS – Intraparietal sulcus, MPFC – middle Prefrontal Cortex, MT+ - human middle temporal area, PostCinf – Inferior Postcentral sulcus, SPL – Superior Parietal Lobule, FEF – Frontal Eye Field area, STP – Superior Temporal Polysensory area, STSm – Middle Superior Temporal Sulcus, VIP – Ventral Intraparietal Sulcus, V3a – area V3a.

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Behavioral Performance on the VS task

• 8 RH healthy subjects (age 18-23) performed the task in the lab prior to t h e M E G s c a n n i n g session • x-axis represents target-object velocity (-8 cm/s to 8 c m / s a t 2 c m / s increments, not including 0 c m / s . r e l a t i v e t o observer motion); y-axis observer motion); y-axis represents performance level (% correct)

• stimulus motion duration was varied (200, 300, 500, 1000 ms) • arrow represents velocity at which spheres would appear stationary (-3 cm/s)

Figure 4:

We measured functional information transfer from one region to another during the stimulus motion in the VS task through Dynamic Granger Causality (DGC). DGC is a time varying form of Granger Causality (GC), computed over a sliding window across time4.

We computed sources, network information flowing out of an ROI, and sinks, network information flowing into an ROI, using DGC. The radius of the circle is proportional to the absolute net information flow through an ROI. Red: if net flow is inward (sink). Blue: if net flow is outward (source) (Fig 5).

• First 100ms: Sources - V3A, MT+, STP, IPS. Sinks - VIP, SPL, FEF. • MT+ is the largest source of information and VIP is the largest sink of information. V3a and MT+ send motion information to the network while VIP extracts the sphere properties (speed and direction) • By 500 ms: Sources - V3a, IPS. Sinks - MT+, VIP, FEF • V3a plays a large role in sending out information up to this time. IPS may be broadcasting frame of reference. FEF is a sink of information which may perform the attention shift. • By 1000 ms: Sources - MT+, V3a, SPL, IPS. Sinks - VIP, FEF, MPFC • MT+ becomes a source again between 500-1000 ms, suggesting a late role in computation of motion information. We suggest that SPL is broadcasting spatial attention shift information, VIP computes information about egomotion and sphere motion, and MPFC stores in working memory past and current target object-location information.

• Early in the stimulus motion (0 - 500 msec): - V3a connects unidirectionally to VIP, supporting its role in supplying visual motion information. - VIP and SPL communicate bidirectionally. - SPL unidirectionally connects to FEF. VIP is critical to extracting the target sphere while SPL is involved in shifting spatial attention. Together, they choose the candidate target sphere and then SPL instructs FEF where to shift attention. • Late in the stimulus motion (500-1000msec): - VIP gains connections from MT+ between 600-700 ms and connects unidirectionally to STP. - IPS sends information to STP late in the stimulus (by 900 ms in the left hemisphere and by 1000 ms in the right hemisphere). - Frontoparietal regions (SPL, FEF, and MPFC) connect transiently and sporadically to various regions throughout the stimulus. STP aggregates information about target-sphere features (speed and direction values) obtained from VIP and defines the perceptual characteristics of target- sphere in the display, to qualify as target-sphere. We suggest that this is accomplished by a template matching algorithm. It also interfaces with IPS to inform the observer about the location of the target sphere.

BIOMAG  2014  

Halifax