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Cerebral Cortex, 2020;00: 1–15
doi: 10.1093/cercor/bhaa133
Original Article
ORIGINAL ARTICLE
Overarching Principles and Dimensions of the
Functional Organization in the Inferior Parietal Cortex
Gina F. Humphreys, Rebecca L. Jackson and Matthew A. Lambon Ralph
MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
Address correspondence to Dr Gina Humphreys. Email: gina.humphreys@mrc-cbu.cam.ac.uk; Matt A. Lambon Ralph.
Email: matt.lambon-ralph@mrc-cbu.cam.ac.uk
Abstract
The parietal cortex (PC) is implicated in a confusing myriad of different cognitive processes/tasks. Consequently,
understanding the nature and organization of the core underlying neurocomputations is challenging. According to the
Parietal Unified Connectivity-biased Computation model, two properties underpin PC function and organization. Firstly, PC
is a multidomain, context-dependent buffer of time- and space-varying input, the function of which, over time, becomes
sensitive to the statistical temporal/spatial structure of events. Secondly, over and above this core buffering computation,
differences in long-range connectivity will generate graded variations in task engagement across subregions. The current
study tested these hypotheses using a group independent component analysis technique with two independent functional
magnetic resonance imaging datasets (task and resting state data). Three functional organizational principles were
revealed: Factor 1, inferior PC was sensitive to the statistical structure of sequences for all stimulus types (pictures,
sentences, numbers); Factor 2, a dorsal–ventral variation in generally task-positive versus task-negative (variable)
engagement; and Factor 3, an anterior–posterior dimension in inferior PC reflecting different engagement in verbal versus
visual tasks, respectively. Together, the data suggest that the core neurocomputation implemented by PC is common across
domains, with graded task engagement across regions reflecting variations in the connectivity of task-specific networks
that interact with PC.
Key words: angular gyrus, numerical processing, parietal, semantic, sequence processing
Introduction
A long history of neuropsychology and functional neuroimaging
has implicated the parietal lobe in a confusing myriad of differ-
ent cognitive processes and tasks. There is currently little clarity
about the underlying core parietal neurocomputations. In a
recent large-scale meta-analysis, we investigated the functional
organization of the inferior parietal cortex (IPC) across multiple
cognitive domains (Humphreys and Lambon Ralph 2015), reveal-
ing dorsal–ventral and anterior–posterior organizational graded
variations in the types of task that engage IPC. Moreover, each
subregion is engaged by multiple diverse tasks indicating that
the region is not tessellated into distinct task-specific modules
but rather the areas support domain-general computations that
are called upon by different activities. Based on these results,
we proposed a unifying model of parietal function, the Parietal
Unified Connectivity-biased Computation (PUCC). Here, we test
some of the central tenants of the model using two independent
functional magnetic resonance imaging (fMRI) datasets as well
as meta-analytic connectivity modeling.
There are three core assumptions of the PUCC model.
The first proposes that the core local computation of the IPC
supports online, multimodal buffering. Any time-extended
behavior, whether verbal or nonverbal relating to internal or
external cognition, requires some kind of internal representa-
tion of “the state of play.”Without a reliable representation of the
current state, it is impossible to check that that the state of the
world has changed in the expected manner following the last
action, to program the next appropriate steps in the sequence
toward the final goal, or to check that the state of the world
has not changed dramatically in the interim such that a whole
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2Cerebral Cortex, 2020, Vol. 00, No. 00
new goal needs to be instituted. Both automatized and exec-
utively guided behaviors require access to an online buffered
representation of the “state of affairs.” A second key notion
relates to the possible broader computational differentiation
across ventral (primarily temporal lobe) and dorsal (parietal)
pathways. Specifically, the ventral processing routes generalize
information across repeated episodes and input modalities,
leading to context-independent representations. For example, in
the case of semantic memory, multiple instances of a particular
exemplar are generalized across time and contexts, thereby
allowing it to be recognized in highly variable situations and
for information to be generalized across instances and contexts
(Lambon Ralph et al. 2010;Buzsaki and Moser 2013;Lambon
Ralph 2014). In contrast, the opposite is true for the parietal
route, which appears to collapse information across items (i.e.,
statistically orthogonal to the ventral pathways) extracting
item-independent time- and space-varying structures (Buck-
ner and Carroll 2007;Kravitz et al. 2011;Ueno et al. 2011;
Bornkessel-Schlesewsky and Schlesewsky 2013). These two
proposed features of the IPC—online buffering and extraction
of item-independent time-/space-related statistics—can arise
from the same computational process. For example, parallel
distributed processing (PDP) models have demonstrated that
through repeated buffering of sequential input, the system
becomes sensitive to the regularities of sequential information
(McClelland et al. 1989;Botvinick and Plaut 2004,2006;Ueno et al.
2011). In the action domain, these statistical structures would
support action schema; in the language domain, it might result
in the knowledge regarding phoneme or word order (depending
on the time resolution over which statistics are computed) as
well as number and spatial codings in other domains. A key
prediction to be tested in this study was that in such models it
is easier to process and buffer sequences that are typical of the
domain in question. Accordingly, we would expect activation in
IPC to be (a) sensitive to sequential violations and (b) to do so
across multiple domains.
There are already hints from past studies that parietal cor-
tex (PC) is sensitive to the temporal structure of events. For
instance, IPC has been shown to respond when a word in a sen-
tence is unexpected (Kuperberg et al. 2003;Hoenig and Scheef
2009), when ordering pictures into the correct sequence (Tinaz
et al. 2006;Melrose et al. 2008;Tinaz et al. 2008), to scrambled
motor sequences compared with learned sequences (Gheysen
et al. 2010), to the oddball task (Stevens et al. 2005;Ciaramelli
et al. 2008), or to violations in an expected visual sequence
(Bubic et al. 2009). Furthermore, the notion that PC buffers
context-dependent information is in accordance with several
more domain-specific theories. For instance, IPC has been pro-
posed as an “episodic buffer” of multimodal episodic infor-
mation (Wagner et al. 2005;Vilberg and Rugg 2008;Shima-
mura 2011), and others suggest that IPC acts as a phonological
buffer/sensorimotor interface for speech (Baddeley 2003;Hickok
and Poeppel 2007;Rauschecker and Scott 2009). While domain-
specific theories have been useful to account for findings from
that domain of interest, they fail to explain how and why dis-
parate cognitive domains coalesce in IPC subregions and thus
what types of domain-general neurocomputations underlie pro-
cessing across tasks (Corbetta and Shulman 2002;Humphreys
and Lambon Ralph 2015,2017).
A third assumption in the PUCC model is that, although there
might be a common overarching parietal neurocomputation,dif-
ferent parietal subregions show variations in processing based
on graded variations in long-range connectivity. Indeed, a large
body of work has shown that the IPC shows a reliable response to
narratives when the content is intact, compared with narrative
stimuli that have been temporally scrambled across multiple
domains of input, for example, language or vision (Hasson et al.
2008;Lerner et al. 2011). Previous computational models have
demonstrated that, even when the units in the layer of a model
have the same core computation, differences in long-range con-
nectivity generate graded variations in emergent function (Plaut
2002). Such connectivity variations might explain differences
in the locus of activation in task-based studies (Bzdok et al.
2013;Humphreys and Lambon Ralph 2015). For example, tasks
involving tool use have been shown to overlap with numerous
other tasks in dorsal PC (top-down attention, executive seman-
tics, phonology, numerical calculation), yet the center of mass
of this cluster spreads toward motor and somatosensory areas
(Humphreys and Lambon Ralph 2015). Thus, although there may
be a high degree of overlap across tasks,the spread of activation
for each will vary depending on the task-specific networks that
connect to PC.
Such connectivity variations across parietal subdivisions
have been demonstrated using structural and functional
connectivity measures. Angular gyrus (AG),supramarginal gyrus
(SMG), and intraparietal sulcus/superior parietal lobule (IPS/SPL)
have been shown to engage partially distinct neural networks:
The AG forms part of the default mode network (DMN), the
SMG forms part of a cingulo-opercular system, and IPS/SPL is
part of a fronto-parietal control system (Vincent et al. 2008;
Spreng et al. 2010;Uddin et al. 2010;Cloutman et al. 2013;
Power and Petersen 2013). There is some evidence that the
transition between regions in terms of their connectivity profile
is graded, rather than sharp in nature (Daselaar et al. 2013). Such
connectivity-driven variations in function might also explain
differences found between anatomically proximate subregions
(Uddin et al. 2010;Caspers et al. 2011;Cloutman et al. 2013):
Dorsal AG has been found to show positive activation for tasks
involving semantic decisions on words and pictures, whereas
middle AG is deactivated by both tasks, and ventral AG is
activated by pictures but not words (Seghier et al. 2010).
In the current study, three independent datasets and a
combination of methods were used to investigate these three
core assumptions of the PUCC model. The first method used
task-based fMRI. If there is a generalized local buffering
computation, then the IPC should activate more for sequential
violations. To test this, sequences of items were presented
with either a regular structure or one where the structure
was violated. Also, the model assumes that, over and above
the generalized buffering mechanism, graded task differences
will follow from the known variations in connectivity. To test
this hypothesis, different types of sequences were presented:
comprising words, pictures, or numbers. To test our predictions
in more detail, the data were analyzed using a group spatial
independent component (ICA). ICA has the advantage of
being a data-driven method which can separate signal from
noise components associated with movement or physiological
fluctuations. As a result, ICA has been shown to possess
increased sensitivity compared with standard generalized
linear model (GLM) techniques (McKeown et al. 2003). An
additional advantage is that ICA can distinguish between
distinct components with partial spatial overlap based on
variations in time courses (Leech et al. 2011). This point is
significant because if subdivisions are graded, we expect some
degree of spatial overlap across subregions. Therefore, task
ICA was used to investigate the functional networks involved
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Functional organization of inferior parietal cortex Humphreys et al. 3
in processing sequence violations across domains. After
establishing the presence of distinct functional PC networks
using the task data, an independent resting state dataset was
used to independently verify the results.
Methods
fMRI Task Data
Participants
Twenty participants took part in the study (average age=24.4,
standard deviation [SD] = 4.79; # females = 16). All participants
were native English speakers with no history of neurological or
psychiatric disorders and normal or corrected-to-normal vision.
Task Design
The participants completed three experimental tasks (sentence
task, picture task, and number task) in separate scan sessions,
the order of which was counterbalanced across subjects. In each
task, on a given trial, a sequence of items (words, pictures, or
numbers) was visually presented one item at a time with either
a familiar structure (normal sequences) or a violated structure
inwhichthefinalitemfromeachsequencewastakenfroma
different item. The participants’ task was to determine if the
sequence was coherent. The sequences were selected from the
most accurate subset from a pilot experiment. Since this is the
first study of its kind to examine sequence violations across
the language, pictorial, and number domain using a shared
paradigm, we sought to maximize any potential effect by using
a highly unexpected sequence ending and an explicit task in
which participants were instructed to focus on the coherence
of the sequences. An example trial from each task is shown in
Figure 1. The items were counterbalanced such that the same
participant did not see both the normal or violated versions of
the same item.
Sentence task: to ensure a high degree of statistical regularity,
sentences were selected in which the final word in the sentence
had a high cloze probability and was thus highly predictable (e.g.,
“He loosened the tie around his neck”). The stimuli were a subset
of the high cloze probability items included in Block and Baldwin
(2010) (average cloze probability = 0.94, SD = 0.01). The sentence
length varied from 6 to 10 words (average length =8.4 words,
SD = 1.0).
Picture task:a series of four color pictures depicted the occur-
rence of real-life, everyday events with a clear causal structure,
that is, the events could not plausibly occur in a different order
(e.g., a banana being peeled,a house being built, etc.). The images
consisted of stills taken from freely available short online video
clips downloaded from youtube.com. In each case, the event
of interest was the central focus of the videos, and there was
minimal distracting background information.
Number task:a series of four numbers involving low-digit
multiplication (e.g., 2 4 6 8) or addition (e.g., 1 2 3 4). Low-digit
multiplication and addition have been shown to be automated
skills, the solutions to which can be easily retrieved from mem-
ory (Simon et al. 2002;Dehaene et al. 2003). Note that the high
accuracy scores for the task (see Results section) confirm that
the sequences were easily recognizable.
Task Procedures
There were 42 items per condition presented using an event-
related design with the most efficient ordering of events deter-
mined using Optseq (http://www.freesurfer. net/optseq). Null
time was intermixed between trials and varied between 2 and
18 s (average= 4.59 s, SD = 3.06) during which a fixation cross
was presented. For the picture and number task, each of the
four items in the sequence was presented for 900 ms (total
length = 3.6 s). The word sequences in the sentence task con-
tained between 6 and 10 words presented at a rate of one word
every 360 ms such that the maximum trial duration matched the
picture and number task. Every item was followed by a “?” for
1.4 s at which point the participants provided a YES/NO button
response.
Task Acquisition Parameters
Images were acquired using a 3 T Philips Achieva scanner using a
dual gradient-echo sequence, which is known to have improved
signal relative to conventional techniques, especially in areas
associated with signal loss (Halai et al. 2014). Thirty-one axial
slices were collected using a TR = 2.8 seconds, TE = 12 and 35 ms,
flip angle = 95◦,80×79 matrix, with resolution 3 ×3 mm, slice
thickness 4 mm. Across all tasks, 918 volumes were acquired in
total, collected in 6 runs of 428.4 s each. B0 images were also
acquired to correct for image distortion.
Task Data Analysis
Preprocessing
The dual-echo images were first B0 corrected and then averaged.
Data were analyzed using SPM8. After motion correction images
were coregistered to the participants T1. Spatial normaliza-
tion into MNI space was computed using DARTEL (Ashburner
2007), and the functional images were resampled to a 3 ×3×
3 mm voxel size and smoothed with an 8-mm full-width at half
maximum (FWHM) Gaussian kernel.
General Linear Modeling
The data were filtered using a high-pass filter with a cutoff
of 190 s and then analyzed using a GLM. At the individual
subject level, each condition for each task was modeled
with a separate regressor (normal, violated) with time and
dispersion derivatives added, and events were convolved
with the canonical hemodynamic response function. Each
sequence was modeled as a single event. Motion parameters
were entered into the model as covariates of no interest. To
investigate the effect of violation, the contrast of violation
sequences >normal sequences was computed in a whole-
brain analysis (uncorrected, P<0.001), with a significant cluster
extent estimated using AlphaSim with α<0.05 and a brain mask
applied (https://afni.nimh.nih.gov/pub/dist/doc/program_help/
AlphaSim.html). More targeted analyses were also conducted
using the parameter estimates. Both GLM and ICA methods
have advantages and disadvantages, and thus we performed
both here. While GLM is a highly informative fMRI analytic
approach, ICA has been shown to reveal a wider task-related
network compared with GLM analyses (Robinson et al. 2013), as
well as the potential to show distinct yet spatially overlapping
functional networks (Xu et al. 2013,2016). On the other hand,
GLM has other advantages including the ability to explore BOLD
time courses across longer trials than those used in the current
study (e.g., van der Linden et al. 2017).
Task Group Spatial ICA
The preprocessed fMRI data were analyzed in a group spatial
ICA using the GIFT toolbox (http://mialab.mrn.org/software/gift)
(Calhoun et al. 2001) to decompose the data into its components,
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4Cerebral Cortex, 2020, Vol. 00, No. 00
Figure 1. Top panel: An example from one trial for each of the tasks. Bottom panel: The task-general and task-specific ICA components (cluster corrected, P<0.05).
separately for each task. GIFT was used to concatenate the sub-
jects’ data and reduce the aggregated dataset to the estimated
number of dimensions using PCA, followed by an ICA analysis
using the infomax algorithm (Bell and Sejnowski 1995). There
were found to be nine non-noise components for the number
task, 11 for the picture task, and 13 for the sentence task. One-
sample t-tests were used to identify areas that significantly
contributed to each component (cluster corrected, P<0.05). The
thresholded t-maps were then inspected, and verbal labels were
assigned to each network, where possible labels were used
which were consistent with those used frequently elsewhere
in the literature (e.g., DMN, motor network, visual network,
language network, saliency network) (Power et al. 2011;Lee et al.
2012;Yeo et al. 2013).
Certain components were found to be common to all
tasks (see Supplementary Material, Fig. S1), we shall therefore
refer to these as task-general networks. We defined task-
general networks based on the degree of spatial overlap across
components (all comparisons had a Dice coefficient >0.7, which
is considered a high degree of spatial overlap). These closely
resemble those that are commonly labeled as a DMN component
and a fronto-parietal executive control component (described in
detail in Results section). An additional network resembling
that commonly referred to as the saliency network was also
present which included the temporo-parietal junction (TPJ);
however this component was found to be insensitive to any
task manipulation and was therefore not included in further
analyses (no task modulated TPJ activation relative to rest
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Functional organization of inferior parietal cortex Humphreys et al. 5
[all ts<1.3, ps>0.2], nor was there any modulation based on
violation [all ts<1.4, ps>0.2]). We have now measured this was
similarly the case for the executive network components (Dice
coefficient >0.7 in all pair-wise comparisons).
In addition to the task-general components, we also identi-
fied two task-specif ic left parietal components (i.e., components
which were not common to each task); a network that we
have labeled as the “language component” from the sentence
task and a “visual-parietal component” from the picture task
(described in detail in Results section).
In order to interrogate the cognitive signature of each compo-
nent, 12-mm spheres were defined around the peak coordinates
from all components of interest, and these were used as regions
of interest (ROIs) to test for significant effects of conditions.
Finally, we also examined how parietal networks might interact
with one another or with other networks in the brain (e.g.,
visual or auditory) by performing a crosscorrelation analysis
of the average time-series for these components (parietal or
nonparietal).
Resting State Data
Participants, Procedures, and Acquisition Parameters
Seventy-eight participants completed the resting state scan
(average age = 25.23, SD = 5.55; # females = 57). During the scan,
the participants were instructed to keep their eyes open and
look at the fixation cross. The data acquisition parameters for
the resting state scan were identical to the experimental task.
The scan consisted of a single 364-s scan session of 130 volumes.
Data Analysis
Preprocessing: Preprocessing was performed using SPM8 and
the Data Processing Assistant for Resting State fMRI (DPARSF
Advanced Edition, V2.3) toolbox (Chao-Gan and Yu-Feng 2010).
Compared with the task data, additional preprocessing steps
were carried out on the resting state data to minimize the
influence of d istance-dependent increases in correlations due to
motion, which are considered problematic in resting state data.
Thus, several procedures were adopted: censoring, global signal
regression, 24 motion parameter regression, and scrubbing of
high motion time points. These methods have been shown to
greatly reduce the effects of motion (Weissenbacher et al. 2009;
Van Dijk et al. 2012;Yan et al. 2013;Power et al. 2014).
The images were first slice-time corrected, realigned, and
coregistered to the subjects T1 using SPM. Censoring was applied
using a threshold of greater than 3 mm of translation or 1 degree
of rotation. This resulted in the exclusion of six participants
from further analysis. Using DPARSF, images were normalized
using DARTEL, smoothed with a 8-mm FWHM Gaussian kernel,
and filtered at 0.01–0.08 Hz (Satterthwaite et al. 2013). Nuisance
covariates were regressed out. These included covariates for 24
motion parameters, white matter, CSF, and global tissue signal
and also the performance of linear detrending. The 24 motion
parameters were calculated from the six original motion param-
eters using Volterra expansion (Friston et al. 1996) and have
been shown to improve motion correction comparedwith the six
parameters alone (Yan et al. 2013;Power et al. 2014). Additional
covariates were included for outlier time points with a with a z-
score >2.5 from the mean global power or >1-mm translation as
identified using the ARtifact detection Tools software package
(ART; www.nitrc.org/projects/artifact_detect).
Resting state ICA: The goal of the resting state ICA analysis
was to use an independent dataset to verify the AG functional
subdivisions identified by the task ICA. The ICA was carried out
on the preprocessed resting state data using the same method
as the task data. This analysis identified five AG components,
the significance of which was tested using one-sample t-tests
(cluster corrected, P<0.05). These five AG subdivisions were
then used as ROIs for the task data to test for effects of violation
and task. The spatial similarity of the parietal ROIs defined
using the task ICA versus the resting state ICA and observed
Dice coefficients varying from 0.2 to 0.6 (dorsal PGa = 0.4, mid-
PGp = 0.4, ventral PGa = 0.6, and ventral PGp = 0.2). This is a good
level of overlap when considering that the components were
identified using different fMRI techniques (resting state vs. task
fMRI) and using different subjects.
Results
Behavioral Results
Task performance was highly accurate across all experimental
tasks (sentence task = 97%, SD = 3.3; picture task = 93%, SD = 5.8;
number task = 91%, SD = 8.1). Nevertheless,there were some task
differences: The sentence task was found to be significantly
more accurate than the number task (t(19) = 3.17, P= 0.005,
d= 0.71) and marginally more accurate than the picture task
(t(19) = 2.52, P= 0.02, d= 0.71), which does not survive Bonferroni
correction. The picture task was marginally more accurate than
the number task (t(19) = 2.17, P= 0.04, d= 0.49), which does not
survive Bonferroni correction.
In terms of reaction time, a 3 ×2 within-subjects ANOVA
found a significant effect of task (F(38) = 18.13, P= 0.001,
ηp2= 0.49), violation (F(38) = 7.71, P= 0.01, ηp2= 0.29) and a
significant task ×violation interaction (F(38) = 22.09, P= 0.001,
ηp2= 0.54). Paired t-tests showed that responses to the sentence
task were slower compared with the picture task (t(19) = 6.35,
P= 0.001, d>1.75) and the number task (t(19) = 5.35, P= 0.001,
d= 1.36) which did not differ (t(19) = 1.06, P= 0.30). The interaction
can be explained by an effect of violation in the picture task
(t(19) = 5.75, P= 0.001, d= 1.28), but no significant difference for
the sentence task or number task (all ts <2ps>0.05).
GLM Analysis
Compared with the normal sequences, the violation sequences
elicited greater activation within IPC for all tasks (see Supple-
mentary Material, Fig. S2 and Tabl e S 1), with overlap in medial
posterior AG (PGp). There was some overlap in the violation
>normal contrast in the DLPFC, although this cluster did not
survive the cluster correction for the number task. No parietal
voxels showed the opposite pattern (normal >violation), even
at a very lenient threshold (all ts <1). Some task differences
were found to be significant. The violation effect was found to
be significantly larger for the sentence task compared with the
other two tasks combined (Sentences >Pictures +Numbers) in
the anterior AG (PGa), left lateral frontal areas (inferior frontal
gyrus [IFG] and precentral gyrus), and right superior temporal
gyrus. The left posterior middle temporal gyrus was also more
strongly recruited for the sentence task; however, this did not
survive the cluster correction. The violation effect for the picture
task was found to be greater than the sentence and number
tasks combined (Pictures >Sentences +Numbers) in a network
of bilateral visual areas (fusiform gyrus and visual cortex). There
were no regions more responsive to the number task compared
with the other two tasks. Therefore, these analyses support
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6Cerebral Cortex, 2020, Vol. 00, No. 00
the hypothesis that IPC, especially AG, is sensitive to sequence
violations but also suggests that there may be task differences
in the full network recruited.
Task ICA Analysis
Task-General Networks
Certain parietal components were found to be largely over-
lapping across tasks (task-general networks). These resemble
a DMN component (bilateral posterior AG, precuneus [PCC],
medial frontal, mid-middle temporal gyrus [MTG]), a fronto-
parietal executive control component (referred to as the execu-
tive network from here on, including left lateral frontal, AG/IPS,
pMTG, and posterior superior frontal gyrus [SFG]). The DMN and
executive control network overlapped in AG; however the peak
for the executive control network was found to be more dorsal,
approaching IPS. Both networks also overlapped in IFG and SFG,
although the peak activation was more dorsal for the executive
control network (Fig. 1).
There were two parietal components that were task-specific
in nature (see Fig. 1). First, there was a visual–parietal network
that involved visual cortex, SPL, and PGp, which was present in
the picture task alone. Second,there was a component identif ied
from the sentence task data that clearly resembled what is often
referred to as the language network (left IFG, MTG, anterior AG)
(Vigneau et al. 2006).
Sensitivity of task-general and task-specific networks to violation. In
order to examine the cognitive signatures of each identified
component, spheres were defined around the peak coordinates
from the task-general (DMN, executive network, and saliency
network) and task-specific networks (language, visual–parietal,
and IPS number), and these were used as ROIs to test for effects
of violation.
Task-General Parietal ROIs
The AG ROI for the executive network was more dorsal (in PGa,
here on referred to as dorsal PGa) compared with the DMN
ROI, which was in central AG (in PGp) (here on referred to
as mid-PGp). Both ROIs showed an overall effect of violation,
which did not interact with task: Within the DMN, mid-PGp
showed a significant effect of violation (F(1,19) = 5.73, P= 0.03,
ηp2= 0.23) but no effect of task (F(2,38) = 1.75, P= 0.19) and no
task x violation interaction (F(2,38) = 0.01, P= 0.99). Within the
executive network, the dorsal PGa ROI showed a significant
effect of violation (F(1,19) = 18.63, P= 0.001, ηp2= 0.55), a marginal
effect of task (F(2,38) = 3.01, P= 0.06, ηp2= 0.14), but no task x
violation interaction (F(2,38) = 1.96, P= 0.16). The effect of task
reflected moderately stronger activity for the picture compared
with number tasks; however, this did not survive a Bonferroni
correction (t(19) = 2.23, P= 0.04).
Despite the AG components of the executive network and
DMN showing similar task-general sensitivity to sequence viola-
tions, the two subregions exhibited opposing directions of acti-
vation relative to fixation; activation for the executive network
was significantly greater than zero for each condition (one-
sample t-test, all ts >3.49, ps <0.002, ds >0.78), whereas the
DMN elicits significant negative activation for each condition
(one-sample t-test, all ts >−3.68, ps <0.002, ds >1.01, although
the sentence violation condition only trended after Bonferroni
correction was applied (t(19) = −2.8, P= 0.01, d= 0.6). This sug-
gests that while both areas show a similar effect of violation,
the underpinning function of each subdivision is likely to dif-
fer, perhaps reflecting the fact that the dorsal PGa is part of
the task-positive executive network, whereas the mid-PGp is
part of the DMN which often shows a task-negative response.
These results from all regions are presented in Figure 2 (and see
Supplementary Material, Fig. S3 for an alternative view of the
ROIs).
Task-General Nonparietal ROIs
Given that the AG component of the executive and DMN were
both sensitive to sequence violations, further analyses were
conducted on the nonparietal components of the networks so
as to determine whether the effect was AG-specific or general
to the whole of the network (see Fig. 3).
Within the DMN, no other region was found to be sensitive
to violation. There were no significant effects for ventral IFG
or PCC (all Fs <2.58, ps >0.13). Mid-MTG and medial frontal
showed a significant effect of task but no effect of violation and
no interaction (mid-MTG, task F(2,38) = 18.95, P= 0.001, ηp2= 0.55,
condition F(1,19) = 0.30, P= 0.59, interaction F(2,38) = 0.47, P= 0.63;
Medial frontal task, F(2,38) = 5.35, P= 0.009, ηp2= 0.22, condition
F(1,19) = 0.08, P= 0.78, interaction F(2,38) = 0.85, P= 0.44). For mid-
MTG, sentences elicited greater activity compared with pictures
(t(19) = 5.45, P= 0.001, d= 1.22) and numbers (t(19) = 4.28, P= 0.001,
d= 0.96), which did not differ (t(19) = 1.58, P= 0.13). Within the
medial frontal ROI, numbers elicited greater activity compared
with pictures (t(19) = 3.52, P= 0.002, d= 1.01). Therefore, the AG is
the only DMN area to respond to sequence violations.
Unlike the DMN, all regions of the executive network showed
task-general sensitivity to violation, with stronger activation for
the violation condition compared with the normal sequences.
Within the dorsal IFG, there was a significant effect of task
(F(2,38) = 6.27, P= 0.004, ηp2= 0.25), violation (F(1,19) = 14.60,
P= 0.001, ηp2= 0.44), but no significant task ×violation inter-
action (F(2,38) = 0.20, P= 0.82). The task effect ref lects greater
activity for sentences compared with numbers (t(19) = 3.10,
P= 0.006, d= 0.69) and pictures (t(19) = 2.46, P= 0.02, d= 0.55).
Similarly, within posterior MTG there was a significant effect
of task (F(2,38) = 18.11, P= 0.001) and violation (F(1,19) = 13.55,
P= 0.002) and no significant task ×violation interaction
(F(2,38) = 2.54, P= 0.09). The task effect reflected reduced for
numbers compared with pictures (t(19) = 5.74, P= 0.001, d= 1.28)
and sentences (t(19) = 4.56, P= 0.001, d= 1.01).
Task-Specific Parietal ROIs
Language network: The anterior ventral AG (here on referred to
as ventral PGa) showed a significant effect of task (F(2,38) = 26.92,
P= 0.001, ηp2= 0.59), violation (F(1,19) = 10.75, P= 0.004, ηp2= 0.39),
and a significant task ×condition interaction (F(2,38) = 5.93,
P= 0.006, ηp2= 0.24). The task effect ref lects stronger activation
for the sentence task compared with the picture task (t(19) = 6.15,
P= 0.001, d= 1.37) and the number task (t(19) = 7.32, P= 0.001,
d= 1.64). The interaction can be explained by a stronger effect
of violation in the sentence task compared with the picture task
(t(19) = 3.12, P= 0.006, d= 0.70) and the number task (t(19) = 2.92,
P= 0.009, d= 0.65). One-sample t-tests were used to examine
whether the activation differed significantly from zero and if so
in which direction. This showed significantly positive activation
for the sentence conditions only (ts >4.72, ps <0.001, d= 1.01),
with the picture and number conditions showing no difference
from zero (ts <0.92, ps >0.37). Therefore, while this area is sensi-
tive to violation overall, the effect is larger in the sentence task,
which is also the only task to positively activate this region. This
difference is likely explained by the fact that this region forms
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Functional organization of inferior parietal cortex Humphreys et al. 7
Figure 2. Percent signal change for the violation and normal sequences for each task within the task ICA ROIs and resting state ICA ROIs. The results show the same
pattern for both methods.
part of the language network and hence responds strongly to
linguistic stimuli. These results are presented in Figure 2 (see
Supplementary Material, Fig. S3 for an alternative anatomical
view of the ROIs).
Visual–parietal network: The posterior ventral AG (here on
referred to as ventral PGp) was specifically sensitive to the
picture task. The results showed a significant effect of task
(F(2,38) = 50.86, P= 0.001, ηp2= 0.73), violation (F(1,19) = 23.69,
P= 0.001, ηp2= 0.56), but no task ×violation interaction (F(2,38) =
1.16, P= 0.33). The picture task elicited stronger activation than
the number task (t(19) = 10.02, P= 0.001, d= 2.24) and the sentence
task (t(19) = 7.62, P= 0.001, d= 1.70), which differed marginally in
favor of the sentence task after applying Bonferroni correction
(t(19) = 2.20, P= 0.04, d= 0.02). Examinations of the direction
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8Cerebral Cortex, 2020, Vol. 00, No. 00
Figure 3. Percent signal change for the violation and normal sequences for each task within the executive network and default mode network. The regions to show a
significant effect of violation are highlighted in red.
of activation revealed significantly positive activation for
the picture task only (ts >6.35, ps <0.001, d= 1.42), with no
modulation of the sentence and number tasks (ts <2, ps >0.05).
Therefore, while this area is sensitive to violation overall, it
shows a task-specific response to picture task likely due to the
fact that this region is part of a visual processing network. These
results are presented in Figure 2 (see Supplementary Material,
Fig. S3 for an alternative anatomical view of the ROIs).
Crosscorrelations
We examined how parietal networks might functionally relate
to one another and also to the nonparietal neural networks by
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Functional organization of inferior parietal cortex Humphreys et al. 9
Figure 4. The crosscorrelation with the average time course of the executive
network.
measuring the crosscorrelations between each network’s time-
series (Bonferroni corrected). Besides the networks mentioned
above,we additionally included in this analysis the task-general
auditory (bilateral auditory cortex) and visual networks (bilateral
visual cortex) in order to have a common comparison across
tasks and test whether each task differentially engaged each
modality (e.g., picture tasks might correlate more strongly with
visual components). Interestingly, like in the analyses described
above, there was a dissociation in responses for the picture task
compared with the sentence task (Fig. 4). These showed a strong
anticorrelation between the executive network (and related net-
works) and the DMN for both tasks, thus suggesting that activa-
tion of the executive network may relate to suppression in the
DMN.
For the sentence tasks, the executive network showed a sig-
nificant positive correlation with the language network and also
the auditory network (even though the sentences were visually
presented) (r= 0.52, r= 0.20, respectively, ps <0.01), but a negative
correlation with visual networks and DMN (r=−0.22, r=−0.30,
respectively, ps <0.001). Whereas, in the picture task, the execu-
tive network instead correlated positively with visual networks
(rs = 0.54, ps <0.001) and negatively with auditory networks and
DMN (r=−0.34, r=−0.42, respectively, ps <0.001). This suggests
that while the executive network is task-general, there is a
shift in the networks that interact with it based on the varying
demands of each task. Furthermore, when a network is not
required for that particular task, it becomes anticorrelated with
the executive network. (Note,we dropped the number task from
this analysis because a “number network” is not well established
in the literature. Without a well-established network to identify,
it is not clear how to select a “number ICA component” for use
in the crosscorrelation analysis. In contrast, visual and auditory
components are well established and could be easily identified.)
Summary
The task ICA results suggest that IPC as a whole is sensitive
to sequence violations. However, IPC subregions appear to be
organized along dorsal–ventral and anterior–posterior dimen-
sions. Specifically, dorsal PGa and mid-PGp showed task-general
responses, yet the time-series of the two networks were anticor-
related and the subregions had opposing activation directions
relative to rest: Dorsal PGa was positively activated by all tasks,
whereas ventral mid-PGp was deactivated.
Whereas these areas showed a task-general response
to violation, anterior and posterior portions of ventral IPC
showed task-specific responses. Specifically, ventral PGa was
only positively activated by sentence tasks, whereas ventral
PGp responded positively to picture tasks alone. This pattern
mirrors the variations in the networks that correlate with each
subregion. Specifically, ventral PGa is part of the language
network and hence is positively activated for sentence tasks,
whereas ventral PGp is part of a visual network and hence
responds to picture tasks. There was also a dynamic, task-
specific switching between the executive network and the
other networks; for the sentence, task the executive network
correlated with language and auditory networks and was
anticorrelated with visual areas, but for the picture tasks the
opposite pattern was found.
Resting State ICA
The resting state ICA analysis was used to verify the presence of
the functional subdivisions using an independent dataset and in
the absence of a task. This ICA revealed five components which
involved IPC. Components 1 and 2 engaged partially overlapping
regions of dorsal PGa. Component 1 included a similar network
as the executive component from the task ICA analysis (lateral
frontal, dorsal PGa, and pMTG), while component 2 was more
restricted in size but still recruited lateral frontal and dorsal
PGa. Component 3 engaged mid-PGp region and was similar
to the DMN identified in the task ICA analysis. Component 4
engaged ventral PGa and resembled the language network from
the previous analysis. Finally, component 5 engaged ventral
PGp and included a network of superior parietal and higher-
level visual areas which included some of the same regions as
the visual/SPL network from the task ICA analyses. Thus, there
appeared to be strong correspondence between the networks
identified in the task-based and resting state ICA analyses (see
Fig. 5).
The five rs-fMRI–derived IPC subdivisions were used as ROIs
for the task data to examine whether they showed a similar
pattern of sensitivity to violation across tasks as those regions
defined by the task ICA (see Fig. 2; Supplementary Material,
Fig. S3). Responses within components 1 and 2 were found to
be similar to the dorsal PGa region from executive network in
the task ICA data. Both components 1 and 2 showed a signif-
icant effect of violation (component 1, F(1,19) = 13.13, P= 0.002,
ηp2>0.41; component 2, F(1,19) = 15.13, P= 0.001, ηp2>0.44) but
no effect of task (component 1, F(2,38) = 1.57, P= 0.22; component
2, F(2,38) = 1.36, P= 0.87) and no task ×condition interaction
(component 1, F(2,38) = 1.98, P= 0.15; component 2, F(2,38) = 0.94,
P= 0.40). Activation was also found to be significantly positive
compared with zero across all conditions (all ts >3.81, ps <0.002,
ds >1.38).
Responses within component 3 resembled those of mid-
PGp DMN in the task data. There was a significant effect of
violation (F(1,19) = 10.10, P= 0.005, ηp2>0.35) but no effect of
task (F(2,38) = 2.76, P= 0.08) and no task ×condition interaction
(F(2,38) = 0.142, P= 0.87). Responses tended to be negative relative
to zero (all ts >2.53, ps <0.01, ds >0.57) except for the picture
violation and sentence violation conditions which did not differ
from zero (t<1.81, P>0.09).
The response within components 4 and 5 resembled the task-
specific responses found for the language network and visual–
parietal network from the task data, respectively.Specif ically, for
component 4 there was a significant effect of task (F(2,38) = 22.54,
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10 Cerebral Cortex, 2020, Vol. 00, No. 00
Figure 5. The correspondence between the task ICA and resting state ICA analyses.
P= 0.001, ηp2>0.67), violation (F(1,19) = 7.67, P= 0.01, ηp2>0.29),
and a significant task ×violation interaction (F(2,38) = 4.86,
P= 0.01, ηp2>0.34). Paired t-tests showed greater activation for
the sentence task compared with the number task (t(19) = 4.97,
P= 0.001, d>1.11) and picture task (t(19) = 5.83, P= 0.001, d>1.30)
and greater activation for the number task compared with
the picture task (t(19) = 2.62, P= 0.02, d>0.58). Also, the effect
of violation was significantly larger in the sentence task
compared with the picture task (t(19) = 2.97, P= 0.008, d>0.66)
or the number task (t(19) = 2.44, P= 0.02, d>0.55), although this
was trending using a Bonferroni correction. Compared with
zero, responses were significantly positive or trending for the
sentence conditions (all ts >2.34, ps <0.03, ds >0.55), did not
differ from zero for the number conditions (all ts <0.34, P>0.74),
and were negative for the picture conditions (all ts>2.52,
P<0.02, ds >0.57).
For component 5, there was a significant effect of violation
(F(1,19) = 19.83, P= 0.001, ηp2>0.51) and task (F(2,38) = 57.46,
P= 0.001, ηp2>0.82) but no task ×condition interaction
(F(2,38) = 0.60, P= 0.55). Paired t-tests showed that the picture
task elicited significantly greater activation compared with the
sentence task (t(19) = 8.09, P= 0.001, d>1.81) and the number task
(t(19) = 8.95, P= 0.001, d>2.00), which did not differ (t(19) = 0.93,
P= 0.37). Against zero, activation was positive for the picture
condition (all ts >4.70, ps <0.001, ds >1.10), did not differ from
zero for the sentence conditions (all ts <2.1, P>0.05), and was
negative or trending for the number condition (all ts>1.87,
P<0.08, ds >0.42).
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Functional organization of inferior parietal cortex Humphreys et al. 11
Discussion
The multimethod approach used in this study revealed several
key findings with regard the function of the IPC. Aligning with
the predictions of the PUCC model (see Introduction section), the
highly convergent results can be summarized in terms of three
contributing factors.
Factor 1: sensitivity to violation—multiple parts of IPC and
the whole executive network are sensitive to sequence violation
across domains; they respond more strongly to sequences in
which the statistical regularity is violated compared with regular
sequences. Despite this general property, the IPC has graded
functional subdivisions as observed in the task ICA and repli-
cated using the independently defined ROIs from the resting
state ICA. Together these ROI analyses plot out two primary axes
of IPC organization (described next).
Factor 2: A dorsal–ventral difference was established with
more dorsal areas of AG that approach the IPS (dorsal PGa),
forming part of the executive network and responding with
positive activation to sequences in a domain-general fashion. In
contrast, more ventralareas (mid-PGp) form part of the DMN and
are deactivated by all tasks (though mid-PGp was the only part
of the DMN that was sensitive to sequential violation). More-
over, the executive network and DMN showed anticorrelated
time-series. Together these results suggest that activation of
the top-down executive network relate to suppression of the
DMN.
Factor 3: the final factor to influence the results was an
anterior–posterior dimension of organization within ventral
IPC. Ventral PGa formed a part of the language network and
hence responded specifically to linguistic material (sentences),
whereas ventral PGp was part of the visual/SPL network and
hence only responded positively to pictorial material. Also the
language and visual–parietal networks selectively correlated
with the executive network only when their preferred task was
performed. This suggests task-dependent dynamic f lexibility
in the regions in their interaction with the core, multidemand
executive network.
The results show that IPC, together with the executive net-
work, responded to task-general sequence violations. The PUCC
model proposes that the IPC may form a neuroanatomically
graded multimodal buffer, thereby supporting a dynamic repre-
sentation of the changing internal and external “state of affairs.”
As a by-product of repeated events, this system will become
sensitive to the temporal and spatial regularities (Plaut 2003).
Accordingly, sequence violations are more effortful to process
and thus elicit greater activation. The current data areconsistent
with existing studies finding that IPC responds to the regulari-
ties of meaningful (words/picture sequences) and meaningless
events (motor/visual sequences) (Kuperberg et al. 2003;Tinaz
et al. 2006;Melrose et al. 2008;Tinaz et al. 2008;Bubic et al. 2009;
Hoenig and Scheef 2009;Gheysen et al. 2010). Indeed, there is a
growing body of evidence that IPC forms part of a context-related
processing network. For instance, it responds more strongly to
images with strong rather than weak contextual associations
(Bar et al. 2008), or when subjects remember contextual asso-
ciates of an item (Fornito et al. 2012), and is sensitive to event
occurrence frequency (d’Acremont et al. 2013). Future studies
will be able to explore how these increased IPL activations relate
to the underlying processes/computations (e.g., prolonged pro-
cessing, transient reorienting of attention, transient/sustained
control mechanism, etc.) and their exact timings. Such inves-
tigations may require formal computational models of these
processes and descriptions of the resultant temporally varying
neural signatures.
IPC responses were found to be task-general with some vari-
ations around the anterior and posterior edges. This supports
the notion that there is a core underlying IPC neurocomputation
which is common across tasks (Walsh 2003;Cabeza et al. 2012;
Humphreys and Lambon Ralph 2015) and argues against a highly
“fractionated” or modular pattern of organization (Nelson et al.
2012). Indeed, the current data appear inconsistent with any
domain-specific theories of IPC function which, for example,
suggest specialization for semantic memory (Geschwind 1972;
Binder et al. 2009), episodic memory (Wagner et al. 2005;Vil-
berg and Rugg 2008;Shimamura 2011), or numerical process-
ing (Arsalidou and Taylor 2011). We consider brief ly how each
domain-specific theory might address the results of this and
other studies. In doing so, we note that these authors might
not have intended their theory to provide explanations for data
from other cognitive domains, as each theory typically focusses
on the primary domain of interest. Most IPC semantic mod-
els would predict stronger activation for words and pictures
compared with numbers and (presumably) positive activation
over and above “rest.” The current data clearly do not support
this prediction. With regard to episodic-related proposals, there
is some convincing evidence that the mid-PGp region is often
positively engaged during episodic fMRI tasks (Humphreys and
Lambon Ralph 2015) and also shows structural and functional
connectivity with other parts of the episodic network including
precuneus and hippocampus (Uddin et al. 2010). The experi-
mental manipulation used in the current experiment places
limited demands on episodic memory retrieval. Since the task
does not require episodic retrieval, this could perhaps explain
the consistent deactivation of PGp across domains since the
region is not required for this cognitive activity. Nevertheless,
any proposal suggesting that the IPC only supports episodic
functions could not account for the overall pattern of activation
found in the current study, for instance,the anterior–posterior or
the dorsal–ventral gradient. Finally, with regard to attention the-
ories of IPC function, direct comparisons between the attention-
reorienting literature and those that relate to the DMN and the
current paradigm have shown that attentional reorientation is
associated with responses within the TPJ (and saliency network),
which is anterior to the AG and does not overlap with the
current areas of interest (Humphreys and Lambon Ralph 2015).
Indeed, the TPJ (and wider saliency network) was found to be
entirely insensitive to any manipulation in the current study.
Nevertheless, while the posterior IPL is not typically implicated
in attention-reorienting functions, one cannot entirely exclude
the possibility that the key differences between the current
and previous studies might have caused a posterior shift of
reorienting-related activity. If this hypothesisis correct, then one
must consider what function “triggers” the reorienting mecha-
nism. Indeed, in the current context, the reorienting mechanism
must be triggered from a signal derived during sequential pro-
cessing, which would necessitate a form of temporal buffering,
such as that proposed here. Overall, together with previous
crossdomain explorations of IPC function (Cabeza et al. 2012;
Humphreys et al. 2015;Humphreys and Lambon Ralph 2015,
2017), the current data are more consistent with the notion of a
domain-general process but with graded differences in function
based on variations in connectivity to different AG subregions.
The ventral PC is involved in bottom-up/stimulus-driven and
automatic task components (Cabeza et al. 2012;Humphreys and
Lambon Ralph 2015). For instance, AG shows stronger activation
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12 Cerebral Cortex, 2020, Vol. 00, No. 00
for faster reaction times (Hahn et al. 2007) and is sensitive to a
range of tasks with more automated tasks compared with exec-
utively demanding tasks, for example, numerical fact retrieval
versus numerical calculation, or making semantic decisions on
concrete versus abstract words (Humphreys and Lambon Ralph
2015). In contrast, the executive network including dorsal IPC
subregions (including dorsal AG and IPS) is known to be involved
in top-down processing, responding more strongly to difficult
decisions or task demands across diverse domains and task
types (Fedorenko et al. 2013;Noonan et al. 2013;Humphreys
and Lambon Ralph 2017). The relationship between the bottom-
up and top-down networks is unclear, but it is possible that
when currently buffered information cannot be automatically
processed by the ventral IPC subregions and their connected
networks (as in the case of sequence violations), then this trig-
gers the involvement of top-down executive processing systems
(see Humphreys and Lambon Ralph 2015 for further discussion).
Indeed, this is akin to the notion of a “circuit breaker” proposed
by Corbetta and Shulman (2002) in which the stimulus-driven
network acts as an alerting system for top-down processing.
Two anatomical gradients of organization were identified
within IPC: dorsal–ventral and anterior–posterior. The fact that
dorsal (IPS/SPL) and ventral parietal (AG/SMG) areas are func-
tionally dissociable has been recognized by several models of
parietal function (Corbetta and Shulman 2002;Cabeza et al.
2008;Humphreys and Lambon Ralph 2015). Indeed, dorsal and
ventral PC connect with distinct cortical areas: Central AG forms
part of the DMN, whereas IPS/SPL is part of a fronto-parietal
control system (Vincent et al. 2008;Spreng et al. 2010;Uddin
et al. 2010;Cloutman et al. 2013;Power and Petersen 2013).
fMRI studies have also shown that dorsal IPC is associated
with task-positive activation, whereas ventral IPC is typically
associated with task-negative activation (Fox et al. 2005). The
current findings demonstrate that, rather than a sharp disso-
ciation between dorsal (IPS/SPL) and ventral (AG/SMG) areas,
there is a graded shift in activation even within the AG: Regions
toward IPS become positively activated and relate more strongly
to the executive network compared with the DMN. The current
results are consistent with a similar graded shift from negative
to positive activation in AG observed for semantic tasks (Seghier
et al. 2010), though the current study shows that this pattern is
not specific to semantic tasks but rather a task-general feature.
The results also showed that the executive network and DMN
had anticorrelated time-series. Likewise, resting state studies
have frequently shown that these networks are anticorrelated
(Fox et al. 2005;Hampson et al. 2010); nevertheless there is
evidence to show this dynamic interplay during task perfor-
mance (Sestieri et al. 2010;Spreng et al. 2010). Future studies
are needed to answer the subsequent questions that arise from
this repeated observation (see also Humphreys and Lambon
Ralph 2015). First, why are any brain regions deactivated at
all? Two important possibilities include the observation that
rest is not a neutral condition but rather allows in-scanner
spontaneous cognition and internal processes and thus “deac-
tivation” might reflect the fact that some active fMRI tasks do
not share these cognitive processes (Buckner and Carroll 2007;
Raichle and Snyder 2007;Binder et al. 2009;Andrews-Hanna
2012). Another possibility relates to the fact that regions tuned to
task-irrelevant functions might be deactivated to save metabolic
energy (Attwell and Laughlin 2001;Humphreys et al. 2015). This
second possibility is consistent with the results found here for
the anterior–posterior changes in function across the ventral IPC
(and other findings, see Humphreys and Lambon Ralph 2017).
Ventral PGa is tuned more toward language, while ventral PGp
for visual tasks. When the active task matches their function,
then these regions exhibit positive activation, whereas during
other types of tasks, they actually deactivate.
A second puzzle is why the executive and DMN are often
(though not always) anticorrelated, with the degree of DMN
deactivation and executive network activation both correlated
with task/item difficulty, regardless of task (Fedorenko et al.
2013;Humphreys and Lambon Ralph 2017). The PUCC model
suggests that the two networks are often counterpointed
because ventral IPC buffering for automatic activities, by
definition, does not require working memory or “problem-
solving” mechanisms, whereas when an ongoing task becomes
problematic, the executive network is engaged and ongoing
automatic buffering may be counterproductive for problem-
solving and thus the buffering is temporarily suspended or
suppressed. These notions are similar to previous suggestions
for a “safety break” mechanism formed through the dynamic
interplay between IPS and IPC and triggered when an unex-
pected event or stimulus is encountered in the ventral network
(Corbetta and Shulman 2002).
The third question relates to what types of task generate
task-positive activation in ventral IPC regions and by extension
the DMN. These regions are most often associated with task-
related deactivation, and thus, understanding the conditions
under which task-positive responses are observed might provide
critical clues about these regions’ core function. This study and
related investigations (e.g., Humphreys and Lambon Ralph 2017)
provide the first evidence for modality-related variations of pro-
cessing within AG, which is frequently considered as a modality-
general processing area (Binder and Desai 2011) and align with
recent proposals that the DMN, more generally, is a multifaceted
entity which fractionates depending on the nature of the task
that is compared with rest (Buckner et al. 2008;Humphreys et al.
2015;Axelrod et al. 2017). The current study observed this type of
fractionation along the ventral IPC region (see also Humphreys
et al. 2017): Ventral PGa exhibited deactivation in all conditions
except for the language sequences when it was positively acti-
vated; ventral PGp showed exactly the reverse pattern. Such
results run counter to any single cause or domain-general rea-
son for deactivation but are consistent with notions that areas
unnecessary for the current task are deactivated, perhaps to
minimize cognitive interference and/or to save metabolic energy
(Attwell and Laughlin 2001;Humphreys et al. 2015;Humphreys
and Lambon Ralph 2015). The mid-AG remains something of a
mystery in that it deactivated across all conditions (albeit being
sensitive to sequence violations like the entire IPC region) and is
one of the areas consistently associated with the DMN (Buckner
et al. 2008). Future crossdomain comparative fMRI studies are
required to establish which subtypes of task generate positive
activations in the mid-AG and whether these tasks are selective
to this IPC subregion, as ventral PGa and PGp appear to be
for language and visual tasks, respectively. Possibilities include
mind-wandering or other forms of internally directed cognition
(Andrews-Hanna 2012), vivid episodic/autobiographical recall
(Wagner et al. 2005;Vilberg and Rugg 2008), or future thinking
(Buckner and Carroll 2007).
The final question to be considered here pertains to what
drives these graded anterior–posterior and superior–ventral
graded functional variations across the IPC region? The PUCC
model, like other proposals (Cabeza et al. 2012), assumes that,
while the IPC might have a core basic neurocomputation (e.g.,
buffering of current information), subregions come to exhibit
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Functional organization of inferior parietal cortex Humphreys et al. 13
gradedly different responses depending on their pattern of
long-range connectivity. This computational principle has
been demonstrated previously for PDP models of semantic
representation (Plaut 2002). In terms of the anterior–posterior
AG gradient, ventral PGa responded positively to the sentence
task presumably due to input from the verbally related posterior
temporal (STS/MTG) areas, whereas ventral PGp exhibited
activation for the picture task, perhaps reflecting greater
connectivity to visually related occipital/occipitoparietal regions
(Ruschel et al. 2014). In a similar vein, the strong dorsal–ventral
IPC variation is likely to reflect differential connectivity, with
stronger connections from dorsal AG/IPS regions to DLPFC, thus
forming the foundation for the multidemand, executive network
(Uddin et al. 2010;Yeo et al. 2011).
To conclude, the IPC exhibits crossdomain sensitivity to
sequence violation, consistent with a multimodal buffering
computation. This generalized function is conditioned across
dorsal–ventral and anterior–posterior dimensions in keeping
with variations in long-range connectivity.
Supplementary Material
Supplementary material can be found at Cerebral Cortex online.
Notes
This research was supported by an MRC Programme grant to
M.A.L.R. (MR/R023883/1), a British Academy fellowship to R.L.J
(pf170068), and MRC intramural funding (MC_UU_00005/18).
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