The network architecture of cortical processing in visuo-spatial reasoning.
ABSTRACT Reasoning processes have been closely associated with prefrontal cortex (PFC), but specifically emerge from interactions among networks of brain regions. Yet it remains a challenge to integrate these brain-wide interactions in identifying the flow of processing emerging from sensory brain regions to abstract processing regions, particularly within PFC. Functional magnetic resonance imaging data were collected while participants performed a visuo-spatial reasoning task. We found increasing involvement of occipital and parietal regions together with caudal-rostral recruitment of PFC as stimulus dimensions increased. Brain-wide connectivity analysis revealed that interactions between primary visual and parietal regions predominantly influenced activity in frontal lobes. Caudal-to-rostral influences were found within left-PFC. Right-PFC showed evidence of rostral-to-caudal connectivity in addition to relatively independent influences from occipito-parietal cortices. In the context of hierarchical views of PFC organization, our results suggest that a caudal-to-rostral flow of processing may emerge within PFC in reasoning tasks with minimal top-down deductive requirements.
- SourceAvailable from: Xiaotong Wen[Show abstract] [Hide abstract]
ABSTRACT: Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.PLoS ONE 07/2013; 8(7):e67428. · 3.53 Impact Factor
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ABSTRACT: Hepatitis C virus (HCV)-related neurocognitive dysfunction was believed to be a consequence of cirrhosis-associated hepatic encephalopathy. Recently, HCV sequences have been shown in cerebrospinal fluid and brain tissue in chronically infected individuals, suggesting that HCV might cross the blood-brain barrier and could affect cognitive functions. The purpose of the present study was to investigate the possible existence of alterations in the executive functions in chronic HCV-infected elderly patients without cirrhosis. This was a case-control study. Participants were 344 older adults aged 60 years or more. Of these, 172 patients had chronic HCV infection (cases) and 172 were healthy individuals not infected with HCV (controls). Both groups had no liver cirrhosis. All participants were subjected to the following: diagnosis of HCV infection by real-time polymerase chain reaction, non-invasive assessment of liver cirrhosis by (abdominal ultrasound and FibroTest) and evaluation of executive functions using five neuropsychological tests (block design, clock drawing, digit span backward, animal verbal fluency and the Executive Interview 25 [EXIT 25]). Multiple logistic regression analyses showed that chronic HCV infection is a significant independent risk factor of poor performance in the clock drawing test (P = 0.01, OR 6.82), digit span backward test (P = 0.04, OR 3.50), animal verbal fluency test (P = 0.04, OR 5.31) and EXIT 25 test (P = 0.03, OR 15.32). Executive functions are impaired in elderly patients with chronic HCV infection without cirrhosis. Geriatr Gerontol Int 2014; ●●: ●●-●●.Geriatrics & Gerontology International 03/2014; · 1.58 Impact Factor
The Network Architecture of Cortical
Processing in Visuo-spatial Reasoning
Ehsan Shokri-Kojori1, Michael A. Motes1,2, Bart Rypma1,2& Daniel C. Krawczyk1,2
1Centerfor BrainHealth, School ofBehavioral and BrainSciences,TheUniversityof TexasatDallas, Dallas,TX,75235-7205, USA,
2Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, 75390-9070, USA.
interactions among networks of brain regions. Yet it remains a challenge to integrate these brain-wide
regions, particularly within PFC. Functional magnetic resonance imaging data were collected while
participants performed a visuo-spatial reasoning task. We found increasing involvement of occipital and
parietal regions together with caudal-rostral recruitment of PFC as stimulus dimensions increased.
Brain-wide connectivity analysis revealed that interactions between primary visual and parietal regions
Right-PFC showed evidence of rostral-to-caudal connectivity in addition to relatively independent
influences from occipito-parietal cortices. In the context of hierarchical views of PFC organization, our
results suggest that a caudal-to-rostral flow of processing may emerge within PFC in reasoning tasks with
minimal top-down deductive requirements.
rules require bringing the car to a stop. Reasoning involves making connections between sensory information,
integrated features (i.e., perception of sensory information) and rules. Prefrontal cortex (PFC) plays a key role in
mediating these interacting processes through interplay between multiple prefrontal areas and other, more
specialized, cortical regions1. Thus, our approach to characterize PFC activity related to reasoning involved a
brain-wide causal network analysis that combined functional connectivity characteristics with estimates of the
distribution of activity between and within cortical regions. This approach allowed for the detection of the
‘‘processing flow’’ across the brain and, in particular, the direction of influence along the rostral-caudal axis of
PFC in the context of reasoning tasks.
this top-down view, rostral areas are associated with more abstract processes such as maintenance of goals and
rules, predominantly influencing caudal PFC activity that mediates domain-specific motor representations and
sensory feature representations. In reasoning tasks, similar cognitive resources are expected to be engaged when
predicted, wherein domain-specific areas integrate stimulus features and communicate information to rule-
Raven’s Progressive Matrices (RPM) is a commonly used intelligence test that assesses human reasoning4–6. In
RPM, participants are required to select an answer choice that best completes a progression of one or multiple
rules among a series of visual items. Due to the multimodal nature of RPM7and the possibility of confounding
variability in individuals’ approaches to solving reasoning problems, reliable assessment of the contributing
cognitive resources becomes extremely challenging. In response to these concerns, we designed a visuo-spatial
within three panels in each trial. In addition, through feature integration, they had to identify change patterns
across the panels (e.g., identifying a revolving shape). Finally, in the rule verification step, the identified patterns
were compared against a collection of known rules (see Results and Methods for more details). This task enabled
easoning is a cognitive process associated with making logical inferences about experienced phenomena in
the surrounding world. As an everyday driving example, imagine a traffic light turns yellow with some
distance left to the intersection. Integrating this information indicates an upcoming red light, so the traffic
30 August 2011
8 May 2012
18 May 2012
requests for materials
should be addressed to
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
examination of processing sequences involved at the feature level of
visuo-spatial reasoning problems. Similar processing sequences
would be involved in more abstract relational reasoning, wherein
relationships between abstract representations (e.g., relational rules)
emerging from processing of stimulus features are integrated and
common to a range of relational reasoning problems.
rules (i.e., allowable change patterns) to avoid recruitment of highly
top-down and hard-to-administer processes involved in the genera-
tion of logical rules. The sequentialnature of the VSRT enabledus to
observe these reasoning components through the temporal order of
corresponding neural responses. Further, the number of stimulus
varied,allowing usto trackincreasing responses within occipital and
parietal regions (associated with visuo-spatial processing), as well as
domain-specific areas (associated with feature integration) within
hypothesized PFC hierarchies. We applied multivariate Granger
causality (MGC) analysis to address the question of whether the
predominant flow of processing is in a feed-backward (rostral-cau-
dal) or a feed-forward (caudal-rostral) manner while controlling for
top-down task aspects through minimizing rule generation require-
distinguish the cascade of proximal inter-PFC influences, implying
hierarchically processing regions, from alternative connectivity
topologies, such as independent influences from remote parietal
and occipital regions to PFC, as well as loops of bidirectional inter-
actions among active brain areas. Finally, we made group compar-
isons based on speed of processing to investigate connectivity
networks mediating efficient reasoning performance.
In each VSRT trial, participants viewed three simultaneously-pre-
sented frames. The left frame consisted of four shapes occupying
different spatial positions. Each position was associated with one
Figure 1 | The VSRT and the associated brain activity. Participants judged whether shapes changed across the 3 frames according to the following
alteration rules: clockwise revolution for the shape in left upper corner, size increase for the left-side shape, multiplication for the center shape and
clockwise rotation for the surrounding center shape. (a) One relation condition. The ellipsoid should continue revolving clockwise along the corners of
the frame. (b) Two relation condition. The polygon should revolve clockwise and the ellipsoid should continue increasing in size. (c) Three relation
condition. Together with the revolving triangle and multiplying ellipsoids, the surrounding rectangle should continue a clockwise rotation around the
center. (d–f) Activation t-maps for one, two and three relations, respectively (brighter colors represent larger t-values, FDR, P , 0.05). More consistent
recruitment of left caudal PFC is evident when relational complexity increases.
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
predefined alteration rule (Fig. 1). Proceeding from left to right, one,
two or three of the shapes were altered so as to vary the relational
complexity (Fig. 1a-c). A shape always began in one location and
either remained the same or began to change in the second frame.
Only if it had started to change in the second frame did it finish
changing in the third frame. If all shapes completed their sequence
of changes in the rightmost frame, the trial was considered to be
‘‘True’’ (half of the trials). Participants were instructed to respond
the shapes that had altered in position from the leftmost to the mid-
dle frame, returned to its original position in the rightmost frame.
Participants were instructed to respond ‘‘False’’ to such trials by
making a left key press. Prior to the functional magnetic resonance
imaging (fMRI) session, participants were given instructions and
examples showing possible types of allowable shape alterations.
Average task accuracy varied from 98% in the simplest condition
to 95% and 92% at the higher complexity levels. Reaction time (RT)
2500 msand2744 ms,respectively,asthetaskcomplexityincreased.
A positive linear trend was observed in RT as relational complexity
increased from level one to level three (F1,575 15.18, P 5 0.0003).
Our hypothesis was that the VSRT evoked cognitive processes sim-
ilar to conventional reasoning paradigms (such as the RPM), but
with a sequential task structure and without the open-ended deduct-
ive requirements of these paradigms. We compared VSRT and RPM
performance (i.e., accuracy and RT) on an additional thirty-two
participants outside the scanner (see Supplementary Methods).
Significant correlations were observed between performance in each
of the VSRT conditions and the RPM. These results supported our
hypothesis that the VSRT conditions involved processes similar to
the performance correlations remained relatively unchanged, sug-
gesting that similar cognitive processes are recruited across VSRT
conditions. Though task conditions appear to tax similar cognitive
processes, they had different durations of engagement indexed by
changes in the mean RT.
to sensory processing, feature integration, and rule verification com-
ponents. Functional regions of interest (ROIs) were extracted by
investigating regions that exhibited a linear increase in activity as a
function of task difficulty (i.e., contrasting activation maps of level-
three complexity to level-one complexity). These ROIs included
bilateral areas from inferior frontal, middle frontal, and posterior
orbital gyri, as well as cingulate and inferior frontal sulci within
PFC (Fig. 2a). ROIs within the caudate nucleus and the thalamus
were also detected. Large clusters of activation within the occipital
and the parietal lobes constituted a major portion of active voxels in
the posterior areas of the brain. Throughout different VSRT condi-
tions, only the quantity of manipulated stimulus features (i.e., the
was held constant. Thus, we predicted that the linear increase in RT
ment of sensory processing and feature integration components
rather than the rule verification processes. Thereby, ‘‘demand-
sensitivity’’ of regions to modulation of these task components
(i.e., sensory processing and feature integration) was determined
by correlating RT with ROI t-values (indexing regressor reliability)
calculated for different task conditions. ROIs with significant group-
level correlations are shown in Fig. 2a. These regions are mostly left-
sided within PFC (i.e. IFGpo and MFG). Cingulate sulcus, parietal,
and occipital ROIs exhibited bilateral demand-sensitivity. These
ing that demand-sensitive regions within left caudal-PFC mediate
domain-specific processes such as feature integration. Moreover,
consistent with specialization of occipital and parietal cortices,
modulation of visuo-spatial sensory processing.
categorically different sequence of cognitive processes. This hypo-
between the performance measures across task conditions and the
RPM (see Supplementary Methods). Thus, the group-level connec-
tivity analysis was performed on the fMRI time-series representing
nectivity in either direction (solid blue lines) were observed between
bilateral MOG, bilateral IpS, and right IFGpo ROIs. These regions
form a heavily interconnected right prefrontal-parietal-occipital
(rPPO) loop previously associated with visuo-spatial processing8,9.
ined. Caudal PFC activation (i.e., IFGpo and CS) in both hemi-
spheres was influenced by occipital and parietal areas. Rostral PFC
within the right hemisphere (i.e., IFS, IFGpt, and POG) showed
evidence of similar occipital and parietal influences with minimal
indication of local interconnectivity. In contrast, left rostral PFC
ive caudal-to-rostral connections. Activity in left IFGpo predicted
the activity in left IFGpt and activity in left CS predicted the
activity in left IFS. Both left IFGpo and left IFGpt were established
as demand-sensitive regions. To further examine the directionality
of the identified group-level connections, the causal influences in
both directions were contrasted within each ROI pair with signifi-
cant connectivity, and the dominant direction of influence is
shown in Supplementary Fig. S1. Almost all of the group-level
connections, particularly along the rostral-caudal axis of PFC,
survived the additional contrast analysis. Furthermore, the distinc-
tiveness of the connectivity patterns between hemispheres was
assessed through a contrast analysis between significant group-
level connections and their homologous connections in the
contralateral hemisphere (Supplementary Fig. S2). These results
support a similar hemispheric distribution of connections, how-
ever, with the expectation of some connections found in left PFC
(e.g., left IFGpo to left IFGpt).
the terminal rostral PFC areas may be associated with more abstract
processing components, such as rule verification. Additionally, our
behavioral data support a rule processing role for the rostral PFC,
where activity in these regions was generally insensitive to the task-
demand (as indexed by RT), given that the complexity of the rules
was not manipulated across task conditions. The results suggest that
the caudal-rostral organization may represent a series of hierarch-
ically functioning regions, but only within the left hemisphere. By
comparison, corresponding right PFC activity appears to represent
relatively independent functional processes associated with visuo-
spatial reasoning. The interhemispheric functional connectivity
was mediated through bilateral Tp, CN and CS. The coordinates of
MFG ROIs are within the range of frontal eye field (FEF) coordi-
nates10(Supplementary Table S1). These MFG ROIs were mainly
influenced by visuo-spatial representations within rPPO loop and
activity in right IFGpo, together mediating the most demanding
motor output (i.e., eye movements), but not from more anterior
median split) were examined in order to dissociate connections con-
left PFC and rPPO loop became significant (Fig. 2b). For slow per-
formers, few influences within right PFC were detected, suggesting
relatively less consistent recruitment of cortical networks across
these participants (Fig. 2c). Furthermore, these results were comple-
mented by directly contrasting connectivity measures across per-
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
formance groups. Accordingly, a collection of connections within
the rPPO loop was found to be significantly stronger within the
network of causal connections, but only when fast performers were
compared to the slow performers (see Supplementary Fig. S1).
Overall, these results suggest that efficient utilization of network
components mediating sensory processing, feature integration, and
rule verification (i.e., the rPPO loop and left PFC hierarchy) contrib-
ute to the speed of task performance.
Reasoning processes are jointly influenced by interactions between
higher cognitive capabilities and the quality of incoming representa-
Figure 2 | Topographical representations of causal connectivitymaps. ROIs are positioned based on their order alongthe coronal axis. (a)Group-level
0.05). Yellow circles represent demand-sensitive ROIs where their activation significantly increased along with RT (FDR, P , 0.05). Green circles
represent non-significant correlations with RT. The radius of the circles is proportional to the mean t-value of selected voxels within each ROI across all
VSRT conditions. Directional influences are represented by red lines with thickness proportional to significance (FDR, P , 0.05). Blue lines represent
significant bidirectional connections (FDR, P , 0.05). (b) Connectivity map of fast performers. Group-level connections were explored to detect those
that are part of fast performers’ connectivity network (P , 0.01). (c) Connectivity map of slow performers (P , 0.01).
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
tions11. The extent to which higher cognition influences task per-
formance has been termed the top-down aspect of a cognitive pro-
cess. In contrast, when performance is affected by the quality of
stimulus representations, the process is said to be mainly bottom-
up. Functional mapping of these processes, central to PFC networks,
has remained a difficult challenge from both neural and cognitive
perspectives. From a neural perspective, massive anatomical inter-
cortical areas has been observed12. Predominantly feed-forward pro-
cessing from posterior regions are shown to mediate bottom-up
information processing, while feed-backward connections, particu-
larly from the PFC to posterior areas, mediate top-down proces-
sing13. Further, PFC neurons tend to show highly flexible response
properties14,15. From a cognitive perspective, top-down and bottom-
up task components can be expected to differentially affect the flow
of underlying neural processing, particularly within PFC. These
competing influences can affect detection of the sequence of reason-
ing processes in performance conditions where both bottom-up and
top-down components are emphasized. In order to overcome these
confounding factors in observing the order of processing, we struc-
tured reasoning conditions where top-down task requirements were
minimized (i.e., eliminating rule generation processes).
Generally, relational integration tasks require processing and
integration of multiple relations and rules embedded in the stimu-
relations that can be explored and processed independently before
problems, through systematically manipulating the number of inde-
pendent relations, in an attempt to discern the neural bases of rela-
tion processing. In contrast to the RPM, the simplified and well-
practiced task structure ensured a controlled and directed recruit-
ment of the cognitive resources involved in sensory processing, fea-
ture integration, and rule verification.
Our brain-wide Granger causal connectivity approach adds to the
picture provided by univariate activation maps to explore the
sequence of processes involved in reasoning. Cortical interactions
underlying the distribution of activities were incorporated as an
essential element in characterizing PFC functions16. These cortical
interactions can be utilized to track processing sequences emerging
from more specialized regions (such as occipital and parietal cor-
outgoing influences within PFC regions.
We investigated the responsiveness of functional ROIs to the
increased visuo-spatial processing demand in order to determine
which brain regions are sensitive to subsequent increase in sensory
processing and feature integration. Consistent with the prior literat-
ure investigating the neural basis of visuo-spatial reasoning8,9,17, we
found that when participants spent more time extracting and integ-
rating visuo-spatial features (i.e., in more complex VSRT condi-
tions), bilateral areas from occipital and parietal regions, along
with left caudal PFC became significantly more responsive.
However, considering our specific task design, rostral PFC activity
did not show evidence of correlation with RT, suggesting relatively
ponents. The connectivity network of fast performers revealed two
main networks contributing to efficient visuo-spatial reasoning: a
right-sided visuo-spatial processing loop between prefrontal-
parietal-occipital ROIs and left PFC caudal-to-rostral influences as-
actions within PFC appeared to establish a hierarchical processing
cascade, wherein domain-specific left caudal PFC areas (associated
within left rostral PFC. However, some ROIs found within the PFC
are somewhat ventral to regions previously established along the
hierarchical PFC organization2,21. While our results still support
the same organization pattern, we attribute these differences to the
nonidentical processing modules engaged in our novel task com-
left-sided PFC network is consistent with recent findings indicating
the dominant role of left rostrolateral PFC in relational integ-
ration18,19. The interactions from the posterior rPPO loop influenced
mostof theactivitywithin rightfrontallobe,whichmayberelated to
functions such as performance monitoring mechanisms associated
These results may initially appear to be incompatible with cognit-
ive control views, which emphasize greater rostral PFC influence on
caudal regions in tasks that require top-down control, such as super-
vising steps involved in driving to the airport from one’s home2,21.
However, in practiced reasoning conditions with less rule ambiguity
(i.e., reduced top-down demand), we have found evidence for both
caudal-to-rostral and rostral-to-caudal processing within the PFC.
Thepresence of caudal-to-rostral influences maybeattributed tothe
fact that rule abstraction and planning of future actions were not
strongly emphasized, which would likely stimulate more dominant
rostral-to-caudal influences. Our results appear to indicate that in
these reasoning conditions, there is a discernable temporal ordering
of processing between posterior visuo-spatial representation and
more anterior regions involved in feature integration and later rule
verification identified in left PFC.
In summary, we have provided a framework within which the
tions have been incorporated in characterizing regions mediating
visuo-spatial reasoning. The present study shows the effectiveness
of connectivity approaches to determine whether neural networks
mediate task sub-processes through cascades of processing modules
regions (e.g., the rPPO loop), or independent influences from other
the functional role and representations within ROIs may not neces-
sarily dictate the directionality of cortical influences. Depending on
the nature of the task, similar regions might exhibit different
strengths or directions of influence. Notably, a recent study on
rule-learning processes has demonstrated that the bottom-up flow
of information from caudal-to-rostral PFC during rule encoding
stages can be reversed, if the presented rules were altered from novel
to practiced sets22. However, the modulation of the respective pro-
cessing flow during the response stage (i.e., rule execution), specif-
ically in reasoning paradigms with different levels of rule ambiguity
remains to be fully explored.
While, due to methodological limitations (e.g., insufficient con-
tinuous time points per block), our current design does not allow for
research could complement our results by manipulating complexity
of task sub-processes or bottom-up and top-down characteristics
and examining subsequent modulations among cortical influences
and estimates of activity. These observations would provide further
insight into how cognitive demand leads to feed-forward and feed-
back flows of cortical processing in reasoning. The temporal and
spatial resolution of fMRI limit detection of causal influences upon
cortical activity (e.g., the outgoing connections from rostral PFC
areas are not determined). Nonetheless, our results substantiate the
application of methodologies required in studying brain-wide net-
works underlying the distribution of neural activity, mediating com-
plex reasoning processes.
Participants. Twenty healthy, right-handed, native English-speaking volunteers
participated in the study. They ranged in age from 18 to 45 (11 females, mean age
27.5) and had normal or corrected vision. The experimental protocol received
approval from the Institutional Review Boards of The University of Texas at Dallas
and UT Southwestern Medical Center at Dallas. All participants provided informed
consent to participate in accordance with the 1964 Declaration of Helsinki.
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
problem types (1, 2, or 3 relations) consisting of 8 trials. The assignment of different
was counterbalanced throughout VSRT trials. One of the four shapes always
frames within each trial were shown simultaneously to minimize the working
memory load of different relational complexity levels. An introductory slide was
shown for 6 seconds before the beginning of each block to inform the participants
displayed for 5 seconds, with a 1-second fixation point between trials. Participants
speed were emphasized. Blocks were positioned in a pseudo-random order to reduce
collinearity between task regressors.
Functional MRI data acquisition and processing. Functional images were acquired
using a 3T Philips MRI scanner with a gradient echo-planar sequence sensitive to
BOLD contrast (TR 5 2000 ms, TE 5 30 ms, flip angle 5 70u). Each volume
consisted of 36 tilted axial slices (4 mm thick, no gap) that provided nearly whole
brain coverage. Participants viewed the projected stimulus through a mirror above
the receiving coil. Anatomical T1-weighted images were acquired in the following
setup: TE 5 3.76 ms, slice thickness 5 1 mm, no gap, flip angle 5 12u. Head motion
was limited using foam head padding. Data processing was performed using
Statistical Parametric Mapping Software (SPM5; Statistical Parametric Mapping;
Wellcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk/spm) run in
MATLAB 7.4 (http://www.mathworks.com). Slice-timing correction was performed
using sinc-interpolation to adjust for different acquisition time of slices. EPI images
were realigned to the first volume of acquisition to correct for subject movement
effects. Anatomical images were coregistered to the realigned EPI images.
Normalization was performed by initially segmenting the coregistered anatomical
brain into gray matter, white matter, and CSF. Then, nonlinear warping was applied
to register individual brains into the MNI space23. The transformation derived from
spatial normalization of the anatomical image was applied to the functional volumes
to bring them into standardized MNI space.
Voxel-level data analysis was performed to assess activation patterns within the
onset and duration of trials within the task blocks. Boxcar functions were convolved
with the canonical HRF function to generate response estimates of each task con-
dition. The resulting estimates, together with motion correction parameters, were
used as regressors in the General Linear Model (GLM) analysis24. Brain areas sig-
nificant at the group-level were detected by contrasting the parameter estimates of
three to one relational complexity (i.e., the voxel-wise linear trend analysis of rela-
tional complexity, Supplementary Fig. S3). Based on this contrast map, twenty
functional ROIs, at least 20 mm apart, were identified (Fig. 2a, Supplementary Table
S3). To compensate for anatomical variability across participants and to ensure that
only task-relevant voxels were considered in the analyses, we searched for most
significant subject-level voxels within all ROIs. Accordingly, cubic search spaces
(20320320 mm3) were positioned on the peaks of group-level activations for all
extracted ROIs with no overlapping spaces. For each participant, a cluster of top 20
significant voxels within each cubic ROI was selected for further data processing.
Correlation between brain activations and reaction times. In order to examine the
effect of varying visuo-spatial task-demand on activity within ROIs, correlation
coefficients between subject RT (i.e., mean RT per relational complexity level) and
ROI activity (i.e., the mean t-value per relational complexity level) were obtained.
Group-level analysis was performed by applying t-tests on the z-transformed
correlation coefficients (Supplementary Fig. S4). ROIs with significant correlations
are shown by yellow colored circles in Fig. 2a.
Multivariate Granger causality analysis. Multivariate Granger causality (MGC)
analysis is a useful technique in detection of causal relations between multiple time-
varying processes. Giventhatcognitive functions are mediated byneural interactions
within and between active cortical and sub-cortical regions, MGC analysis can be
applied in exploring these brain-wide causal interactions25,26. This approach, when
applied to a network of ROIs, enables observation of unique and directional causal
influences based on the temporal precedence of activity. In contrast to correlational
similar activity, that are considered to be connected in the correlational approaches.
Moreover, unlike other causality analysis techniques, such as structural equation
modeling27and dynamic causal modeling28, MGC is exploratory in nature, in that it
identifying predictability properties of time-series representing regional
hemodynamic responses throughout a cognitive task. Specifically, if inclusion of past
time-series from a particular originating region uniquely improves the prediction of
present activity within a target region (i.e., reducing the prediction error), then the
originating region is inferred to have a Granger causal influence on the target
region29,30. In this study, causal connectivity was analyzed to determine regional
associations by specifying areas that influence each other more than other brain
regions (see Supplementary Methods). The MGC method was applied to the
preprocessed fMRI time-series averaged over subject-specific voxels within the
functional ROIs. The preprocessing steps before applying the connectivity analysis
included motion correction, slice-timing correction, and spatial normalization. After
detrending the voxel time-series in each run25, the MGC analysis was performed on
the runs separately, and the resulting Fisher’s z-values were averaged across the task
runs to generate an estimate of causal correlations for each direction for a given ROI
pair when other possiblecausal effects were partialled out(Fig. 3a&b). The MGC was
adapted for group-level comparisons to investigate consistent cortical networks
across participants mediating visuo-spatial reasoning (Fig. 3c).
signal from prefrontal cortex in executive control of memory retrieval. Nature
401, 699–703 (1999).
Figure 3 | Group-level investigation of causal influence of ROIion ROIj(N 5 20). (a) Schematic of the multivariate Granger causality (MGC) at the
influence of ROIion ROIj. (c) Significant causal influences were then determined using a two tailed t-test comparing the mean of Fisher’s z-transformed
correlations against zero (FDR, P , 0.05).
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411
2. Badre, D. & D’Esposito, M. Is the rostro-caudal axis of the frontal lobe
hierarchical? Nat. Rev. Neurosci. 10, 659–669 (2009).
executive function. Trends Cogn. Sci. 11, 229–235 (2007).
4. Raven, J. C., Court, J. H. & Raven, J. Manual for Raven’s Progressive Matrices and
Vocabulary Scales (Lewis, London, 1988).
the Raven’s Progressive Matrices Test. Cogn. Psychol. 33, 43–63 (1997).
6. Perfetti, B., Saggino, A., Ferretti, A., Caulo, M., Romani, GL. & Onofrj, M.
Differential patterns of cortical activation as a function of fluid reasoning
complexity. Hum. Brain. Mapp. 30, 497–510 (2009).
7. Snow, R. & Lohman, D. Toward a theory of cognitive aptitude for learning from
instruction. J. Ed. Psych. 76, 347–376 (1984).
8. Christoff, K. et al. Rostrolateral prefrontal cortex involvement in relational
integration during reasoning. Neuroimage 14, 1136–1149 (2001).
9. Prabhakaran, V., Narayanan, K., Zhao, Z. & Gabrieli, J. Integration of diverse
information in working memory within the frontal lobe. Nat. Neurosci. 3, 85–90
10. Paus, T. Location and function of the human frontal eye-field: A selective review.
Neuropsychologia 4, 475–483 (1996).
11.Norman,D.&Bobrow, D.Ondata-limitedand resource-limitedprocesses. Cogn.
Psychol. 7, 44–64 (1975).
12. Petrides, M. & Pandya, D. Dorsolateral prefrontal cortex: comparative
cytoarchitectonic analysis in the human and the macaque brain and
corticocortical connection patterns. Eur. J. Neurosci. 11, 1011–1036 (1999).
13. Miller, B. & D’Esposito, M. Searching for ‘‘the top’’ in top-down control. Neuron
48, 535–538 (2005).
14. Wallis, J., Anderson, K. & Miller, E. Single neurons in prefrontal cortex encode
abstract rules. Nature 21, 953–956 (2001).
15. Miller, E., Freedman, D. & Wallis, J. The Prefrontal Cortex: Categories, Concepts
and Cognition. Philos. Trans. R. Soc. Lond. B Biol. Sci. 357, 1123–1136 (2002).
16. Fuster, J. The cognit: a network model of cortical representation. Int. J.
Psychophysiol 60, 125–132 (2006).
17. Kroger, J., Sabb, F., Fales, C., Bookheimer, S., Cohen, M. & Holyoak, K.
Recruitment of anterior dorsolateral prefrontal cortex in human reasoning: a
parametric study of relational complexity. Cereb. Cortex 12, 477–485 (2002).
cortex meets a stringent test of the relational integration hypothesis. Neuroimage
46, 338–342 (2009).
19. Hampshire, A., Thompson, R., Duncan, J. & Owen, A. Lateral prefrontal cortex
subregions make dissociable contributions during fluid reasoning. Cereb. Cortex
21, 1–10 (2010).
20. Petrides, M. Impairments on nonspatial self-ordered and externally ordered
working memory tasks after lesions of the mid-dorsal part of the lateral frontal
cortex in the monkey. J. Neurosci. 15, 359–375 (1995).
21. Koechlin, E., Ody, C. & Kouneiher, F. The architecture of cognitive control in the
human prefrontal cortex. Science 14, 1181–1185 (2003).
22. Cole, M., Bagic, A., Kass, R. & Schneider, W. Prefrontal dynamics underlying
rapid instructed task learning reverse with practice. J. Neurosci. 30, 14245–14254
23.Ashburner,J.&Friston, K.Nonlinear spatialnormalizationusingbasisfunctions.
Hum. Brain Mapp. 7, 254–266 (1999).
24. Friston, K., Holmes, A., Worsley, K., Poline, J. P., Frith, C. & Frackowiak, R.
Statistical parametric maps in functional imaging: a general linear approach.
Hum. Brain Mapp. 2, 189–210 (1995).
Methods 186, 262–273 (2009).
26. Roebroeck, A., Formisano, E. & Goebel, R. Mapping directed influence over the
brain using Granger causality and fMRI. Neuroimage 25, 230–242 (2005).
New York, 2000).
28.Friston,K., Harrison, L.& Penny, W. Dynamic causal modelling. Neuroimage 19,
29. Granger, C. Investigating causal relations by econometric models and cross-
spectral methods. Econometrica 37, 424–438 (1969).
30. Geweke, J. Measures of conditional linear-dependence and feedback between
time-series. J. Am. Stat. Assoc. 79, 907–915 (1984).
The authors thank A. O’Toole and I. J. Bennett for valuable comments. We also thank M.
McClelland and C. Donovan for assistance in data collection.
E. S.-K. analyzed the data and wrote the manuscript. M. A. M. and B. R. designed the
experiment and edited the manuscript. D. C. K. designed the experiment and wrote the
Supplementary information accompanies this paper at http://www.nature.com/
Competing financial interests: The authors declare no competing financial interests.
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How to cite this article: Shokri-Kojori, E., Motes, M.A., Rypma, B. & Krawczyk, D.C. The
Network Architecture of Cortical Processing in Visuo-spatial Reasoning. Sci. Rep. 2, 411;
SCIENTIFIC REPORTS | 2 : 411 | DOI: 10.1038/srep00411