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Overarching Principles and Dimensions of the Functional Organization in the Inferior Parietal Cortex


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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.
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Cerebral Cortex, 2020;00: 1–15
doi: 10.1093/cercor/bhaa133
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:; Matt A. Lambon Ralph.
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
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.
fMRI Task Data
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
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 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
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
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 (
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 (
(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
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
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.
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
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).
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
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
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.)
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
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
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.
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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... Some examples are episodic retrieval or future thinking , narrative speech comprehension (Branzi et al. 2020), and event occurrence frequency (d 'Acremont et al. 2013). The LPC is also sensitive to the temporal structure of events in linguistic, pictorial, numerical, and motoric sequence tasks (Kuperberg et al. 2003;Hoenig and Scheef 2009;Melrose et al. 2008;Tinaz et al. 2006Tinaz et al. , 2008Gheysen et al. 2010;Stevens et al. 2005;Ciaramelli et al. 2008;Bubic et al. 2009;Humphreys et al. 2020a). We would predict that if the LPC operates as a "buffering-system" then the content represented in the system would update periodically, to reflect the changing incoming internal/external information. ...
... These variations are reflected in differences in the emergent cognitive functions. For instance, anterior vLPC (SMG) has been associated with phonological processing and bottom-up attention, whereas the AG forms part of the DMN and is engaged by episodic/autobiographical memory retrieval, narrative comprehension, numerical fact retrieval etc. (Humphreys et al. 2020a(Humphreys et al. , 2022aHumphreys and Lambon Ralph 2015;Branzi et al. 2020;Corbetta and Shulman 2002a;Delazer et al. 2003;Kim 2010;Vilberg and Rugg 2008). ...
... Whilst the evidence discussed above is consistent with these assumptions, a direct test of the model requires (1) within-study cross-domain comparisons, and (2) directly linking functional data with measures of functional and structural connectivity. We addressed this issue in a series of studies that especially focused on the AG and its dorsal boarder with lateral IPS (Humphreys et al. 2020a(Humphreys et al. , 2022b. First, using resting-state ICA, we identified separable AG subregions, consistent with those identified elsewhere (Caspers et al. 2008(Caspers et al. , 2011Uddin et al. 2010;Cloutman et al. 2013;Mars et al. 2011). ...
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Decades of neuropsychological and neuroimaging evidence have implicated the lateral parietal cortex (LPC) in a myriad of cognitive domains, generating numerous influential theoretical models. However, these theories fail to explain why distinct cognitive activities appear to implicate common neural regions. Here we discuss a unifying model in which the angular gyrus forms part of a wider LPC system with a core underlying neurocomputational function; the multi-sensory buffering of spatio-temporally extended representations. We review the principles derived from computational modelling with neuroimaging task data and functional and structural connectivity measures that underpin the unified neurocomputational framework. We propose that although a variety of cognitive activities might draw on shared underlying machinery, variations in task preference across angular gyrus, and wider LPC, arise from graded changes in the underlying structural connectivity of the region to different input/output information sources. More specifically, we propose two primary axes of organisation: a dorsal–ventral axis and an anterior–posterior axis, with variations in task preference arising from underlying connectivity to different core cognitive networks (e.g. the executive, language, visual, or episodic memory networks).
... A related though more specific alternative to the semantic-hub hypothesis is that the AG stores "semantic event" information per se, whereas "object" information is stored elsewhere in the semantic system, such as the anterior temporal lobe (ATL) (6). Alternatively, others have proposed domain general models, for instance the theory that AG is implicated by any "internally-generated" cognition (including semantic memory or episodic memory retrieval, as well as other processes) (7,8), or that it forms a multimodal sequential buffer (9)(10)(11). In addition to the confusing number of potential roles of the AG, the majority of the studies have investigated "input" modalities (e.g. ...
... Nevertheless it does not preclude the possibility that the central function of AG is "semantic event" knowledge since episodic retrieval might necessitate the activation of event semantics. The results are also consistent with the Parietal Unified Connectivity-biased Computation (PUCC) model (9)(10)(11), whereby the emergent expressed function will depend on what sources of information and influences arrive at each subregion, in this case the AG connect with the episodic retrieval network and hence is more engaged by tasks that involve episodic retrieval. Note that PUCC and the "episodic buffer" model are not mutually exclusive, PUCC simply offers a mechanistic explanation as to how an episodic buffer might operate. ...
... The current data are consistent with the PUCC model, which proposes that the LPC acts as online buffer of information, in this case relating to autobiographical episodes. According to PUCC, variations in activation across LPC arise from variations in long-range connectivity (9)(10)(11)(12). This tenet has been formally demonstrated by computational models, whereby the resultant "expressed behaviour" of a group of processing units depends not only on their local computation, but also on the long-range connectivity (more recently referred to as 'connectivity-constrained cognition -C 3 ': (51)(52)(53)). ...
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A long history of neuropsychology and neuroimaging has implicated the angular gyrus (AG) with a myriad of cognitive functions. We investigated the functional engagement of AG by three forms of memory: 1) episodic/autobiographical memory 2) object semantic-memory, and 3) event-semantic processing using the previously under-examined naturalistic task of propositional speech production. We conducted an ALE meta-analysis of imaging studies of propositional speech, followed by an fMRI study in which we conducted direct cross-domain comparisons. The results from the meta-analysis suggest that the AG is only engaged as part of the propositional speech network when the task carries an autobiographical component. This conclusion was strongly supported by the results from the fMRI study. The key fMRI findings were: 1) AG was positively engaged during autobiographical memory retrieval. 2) The AG was strongly deactivated for definitions of object semantics and non-propositional speech. 3) The level of AG activation increased with the degree to which the event descriptions relied on input from the episodic memory system. 4) Critically, the AG showed a very different pattern to that of known semantic representation regions (the anterior temporal lobe; ATL)-whilst AG activation increased with the autobiographical nature of the task, the ATL was equally responsive to all conditions. These results provide clear evidence that the AG is not acting as a semantic hub. In contrast, the AG activation profile directly mirrored that found in the wider autobiographical retrieval network. We propose that information stored elsewhere in the episodic system is temporally buffered online in the AG during autobiographical retrieval/memory construction.
... Moreover, neuroimaging studies have also found that Chinese children with reading difficulty in English showed decreased brain activity in the LAG and increased gray matter volume in the LSMG compared to typically developing readers ( Li et al., 2018 ;You et al., 2011 ), which did not overlap with the anterior part of the LIPL on which our study focused. This finding could be associated with functional specialization within different inferior parietal lobes ( Bzdok et al., 2016 ;Humphreys et al., 2020 ;Numssen et al., 2021 ). For example, Numssen et al. (2021) addressed this issue through a functional parcellation analysis and utilized tasks associated with attention, semantics, and social cognition. ...
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Additional neural substance for reading in a second language has been reported by prior studies. However, to date, there has been little investigation into whether and how the brain's adaptation to a second language is induced by specific linguistic tasks or is a general effect during reading in a new language. To address this issue, our study investigated Chinese children learning English as a second language by combining cross-sectional and longitudinal Functional Magnetic Resonance Imaging (fMRI) studies. We compared brain activation across four reading tasks, orthographic tasks and phonological tasks in Chinese (the first language, L1) and English (the second language, L2). By comparing the activation pattern across languages, we observed greater activation in the left inferior parietal lobule (LIPL) in English compared to Chinese, suggesting a functional preference of the LIPL to L2. In addition, greater correlation between LIPL-related FC and L2 was mainly observed in the phonological task, indicating that LIPL could be associated with phonological processing. Moreover, a proportion of the children were enrolled in an 8-week phonological-based reading-training program. We observed significant functional plasticity of the LIPL elicited by this training program only in the English phonological task and not in the orthographic task, further substantiating that the additional requirements of the LIPL in L2 are mainly associated with phonological processing. The findings provide new insights into understanding the functional contribution of the LIPL to reading in a second language.
... For the episodic memory network, a left hippocampal ROI (MNI: -28 -14 -15) was defined based on previous literature of initial hippocampal activation during word learning.29,30 In addition, an ROI in the left inferior parietal lobe was included (IPL; MNI: -47 -64 34) due to consistent activation of this region in episodic processing.70,71 Pearson's r correlations were used to explore associations between ROI BOLD activity and behavioural performance measures (accuracy and RT). ...
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Speech and language therapy can be an effective tool in improving language in post-stroke aphasia. Despite an increasing literature on the efficacy of language therapies, there is a dearth of evidence about the neurocognitive mechanisms that underpin language re-learning, including the mechanisms implicated in neurotypical learning. Neurotypical word acquisition fits within the idea of Complementary Learning Systems, whereby an episodic hippocampal system supports initial rapid and sparse learning, whilst longer-term consolidation and extraction of statistical regularities across items is underpinned by neocortical systems. Therapy may drive these neurotypical learning mechanisms, and efficacy outcome may depend on whether there is available spared tissue across these dual systems to support learning. Here, for the first time, we utilised a reverse translation approach to explore these learning mechanisms in post-stroke aphasia, spanning a continuum of consolidation success. After three weeks of daily anomia treatment, 16 patients completed a functional magnetic resonance imaging protocol; a picture naming task which probed (i) premorbid vocabulary retained despite aphasia, (ii) newly re-learned treated items and (iii) untreated/unknown and therefore unconsolidated items. The treatment was successful, significantly improving patients’ naming accuracy and reaction time post-treatment. Consistent with the Complementary Learning Systems hypothesis, patients’ overall naming of treated items, like that of controls when learning new vocabulary, was associated with increased activation of both episodic and language regions. Patients with relatively preserved left hemisphere language regions, aligned with the control data in that hippocampal activity during naming of treated items was associated with lower accuracy and slower responses – demonstrating the shifting division of labour from hippocampally-dependent new learning towards cortical support for the efficiently-named consolidated items. In contrast, patients with greater damage to the left inferior frontal gyrus displayed the opposite pattern (greater hippocampal activity when naming treated items was associated with quicker responses), implying that their therapy-driven learning was still wholly hippocampally reliant. Open access For the purpose of open access, the UKRI-funded authors have applied a Creative Commons Attribution (CC-BY) licence to any Author Accepted Manuscript version arising from this submission.
... 30 Functional neuroimaging suggests that the multidomain involvement of TPJ/IPL can be traced to its role in deploying key neurocomputational resources to meet demands of different cognitive tasks. 28,31 Three principal features of this region underpin its domain-general and domainselective functions: ...
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The logopenic variant of primary progressive aphasia is characterized by early deficits in language production and phonological short-term memory, attributed to left-lateralized temporoparietal, inferior parietal and posterior temporal neurodegeneration. Despite patients primarily complaining of language difficulties, emerging evidence points to performance deficits in non-linguistic domains. Temporoparietal cortex, and functional brain networks anchored to this region, are implicated as putative neural substrates of non-linguistic cognitive deficits in logopenic variant primary progressive aphasia, suggesting that degeneration of a shared set of brain regions may result in co-occurring linguistic and non-linguistic dysfunction early in the disease course. Here, we provide a Review aimed at broadening the understanding of logopenic variant primary progressive aphasia beyond the lens of an exclusive language disorder. By considering behavioural and neuroimaging research on non-linguistic dysfunction in logopenic variant primary progressive aphasia, we propose that a significant portion of multidimensional cognitive features can be explained by degeneration of temporal/inferior parietal cortices and connected regions. Drawing on insights from normative cognitive neuroscience, we propose that these regions underpin a combination of domain-general and domain-selective cognitive processes, whose disruption results in multifaceted cognitive deficits including aphasia. This account explains the common emergence of linguistic and non-linguistic cognitive difficulties in logopenic variant primary progressive aphasia, and predicts phenotypic diversification associated with progression of pathology in posterior neocortex.
... The overlap suggests these regions serve a domain-general role rather than one that is specialised towards processing social information. While there is evidence for a selective role of the right TPJ and mPFC in social and moral processing (Saxe & Kanwisher, 2003;Saxe & Wexler, 2005;, there is also evidence that they are engaged by a wide range of tasks including those outside the social domain (Bzdok et al., 2016;Cabeza et al., 2012;Diveica et al., 2021,;Humphreys et al., 2020;Seghier et al., 2010;van Overwalle, 2009). In other recent work, we have considered the possibility that activation of frontal regions, the TPJ and pMTG during ToM tasks, also reflects engagement of domain-general processes related to semantic cognition (Binney & Ramsey, 2020). ...
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A key challenge for neurobiological models of social cognition is to elucidate whether brain regions are specialised for that domain. In recent years, discussion surrounding the role of anterior temporal regions epitomises such debates; some argue the anterior temporal lobe (ATL) is part of a domain‐specific network for social processing, while others claim it comprises a domain‐general hub for semantic representation. In the present study, we used ATL‐optimised fMRI to map the contribution of different ATL structures to a variety of paradigms frequently used to probe a crucial social ability, namely ‘theory of mind’ (ToM). Using multiple tasks enables a clearer attribution of activation to ToM as opposed to idiosyncratic features of stimuli. Further, we directly explored whether these same structures are also activated by a non‐social task probing semantic representations. We revealed that common to all of the tasks was activation of a key ventrolateral ATL region that is often invisible to standard fMRI. This constitutes novel evidence in support of the view that the ventrolateral ATL contributes to social cognition via a domain‐general role in semantic processing and against claims of a specialised social function. A key challenge for neurobiological models of social cognition is to elucidate whether brain regions are specialised for that domain. We used ATL‐optimised fMRI to map the contribution of different ATL structures to a variety of paradigms frequently used to probe ‘theory of mind’, as well as a non‐social task probing semantic representations. We provide novel evidence in support of the view that the ventrolateral ATL contributes to social cognition via a domain‐general role in semantic processing and against claims of a specialised social function.
... This is in line with previous research on language processing, including reading 61 and sentence comprehension. 62 The clusters associated with the Generation component of the fluency tasks as well as the Suppression component of the Hayling comprised mainly left frontal areas. They partly overlapped, most notably in the left middle and inferior frontal gyrus, in line with previous research on the Hayling 63 or fluency tests. ...
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It is increasingly acknowledged that, often, patients with post-stroke aphasia not only have language impairments but also deficits in other cognitive domains (e.g. executive functions) that influence recovery and response to therapy. Many assessments of executive functions are verbally based and therefore usually not administered in this patient group. However, the performance of patients with aphasia in such tests might provide valuable insights both from a theoretical and clinical perspective. We aimed to elucidate (i) if verbal executive tests measure anything beyond the language impairment in patients with chronic post-stroke aphasia, (ii) how performance in such tests relates to performance in language tests and nonverbal cognitive functions, and (iii) the neural correlates associated with performance in verbal executive tests. In this observational study, three commonly used verbal executive tests were administered to a sample of patients with varying aphasia severity. Their performance in these tests was explored by means of principal component analyses, and the relationships with a broad range of background tests regarding their language and nonverbal cognitive functions were elucidated with correlation analyses. Furthermore, lesion analyses were performed to explore brain-behaviour relationships. In a sample of 32 participants, we found that: (i) a substantial number of patients with aphasia were able to perform the verbal executive tests; (ii) variance in performance was not explained by the severity of an individual's overall language impairment alone but was related to two independent behavioural principal components per test; (iii) not all aspects of performance were related to the patient's language abilities; and (iv) all components were associated with separate neural correlates, some overlapping partly in frontal and parietal regions. Our findings extend our clinical and theoretical understanding of dysfunctions beyond language in patients with aphasia.
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The posterior lateral temporal cortex is implicated in many verbal, nonverbal, and social cognitive domains and processes. Yet without directly comparing these disparate domains, the region’s organization remains unclear; do distinct processes engage discrete subregions, or could different domains engage shared neural correlates and processes? Here, using activation likelihood estimation meta-analyses, the bilateral posterior lateral temporal cortex subregions engaged in 7 domains were directly compared. These domains comprised semantics, semantic control, phonology, biological motion, face processing, theory of mind, and representation of tools. Although phonology and biological motion were predominantly associated with distinct regions, other domains implicated overlapping areas, perhaps due to shared underlying processes. Theory of mind recruited regions implicated in semantic representation, tools engaged semantic control areas, and faces engaged subregions for biological motion and theory of mind. This cross-domain approach provides insight into how posterior lateral temporal cortex is organized and why.
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Recent research has highlighted the importance of domain-general processes and brain regions for language and semantic cognition. Yet, this has been mainly observed in executively demanding tasks, leaving open the question of the contribution of domain-general processes to natural language and semantic cognition. Using fMRI, we investigated whether neural processes reflecting context integration and context update - two key aspects of naturalistic language and semantic processing - are domain-specific versus domain-general. Thus, we compared neural responses during integration of contextual information across semantic and non-semantic tasks. Whole-brain results revealed both shared (left posterior-dorsal inferior frontal gyrus, left posterior inferior temporal gyrus, and left dorsal angular gyrus/intraparietal sulcus) and distinct (left anterior-ventral inferior frontal gyrus, left anterior ventral angular gyrus, left posterior middle temporal gyrus for semantic control only) regions involved in context integration and update. Furthermore, data-driven functional connectivity analysis clustered domain-specific versus domain-general brain regions into distinct but interacting functional neural networks. These results provide a first characterisation of the neural processes required for context-dependent integration during language processing along the domain-specificity dimension, and at the same time, they bring new insights on the role of left posterior lateral temporal cortex and left angular gyrus for semantic cognition.
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Semantic cognition is a complex multifaceted brain function involving multiple processes including sensory, semantic, and domain-general cognitive systems. However, it remains unclear how these systems cooperate with each other to achieve effective semantic cognition. Here, we used independent component analysis (ICA) to investigate the functional brain networks that support semantic cognition. We used a semantic judgment task and a pattern-matching control task, each with 2 levels of difficulty, to disentangle task-specific networks from domain-general networks. ICA revealed 2 task-specific networks (the left-lateralized semantic network [SN] and a bilateral, extended semantic network [ESN]) and domain-general networks including the frontoparietal network (FPN) and default mode network (DMN). SN was coupled with the ESN and FPN but decoupled from the DMN, whereas the ESN was synchronized with the FPN alone and did not show a decoupling with the DMN. The degree of decoupling between the SN and DMN was associated with semantic task performance, with the strongest decoupling for the poorest performing participants. Our findings suggest that human higher cognition is achieved by the multiple brain networks, serving distinct and shared cognitive functions depending on task demands, and that the neural dynamics between these networks may be crucial for efficient semantic cognition.
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Self-generated cognitions, such as recalling personal memories or empathizing with others, are ubiquitous and essential for our lives. Such internal mental processing is ascribed to the default mode network - a large network of the human brain - although the underlying neural and cognitive mechanisms remain poorly understood. Here, we tested the hypothesis that our mental experience is mediated by a combination of activities of multiple cognitive processes. Our study included four functional magnetic resonance imaging experiments with the same participants and a wide range of cognitive tasks, as well as an analytical approach that afforded the identification of cognitive processes during self-generated cognition. We showed that several cognitive processes functioned simultaneously during self-generated mental activity. The processes had specific and localized neural representations, suggesting that they support different aspects of internal processing. Overall, we demonstrate that internally directed experience may be achieved by pooling over multiple cognitive processes.
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After consolidation, information belonging to a mental schema is better remembered, but such memory can be less specific when it comes to details. A neuronal mechanism consistent with this behavioral pattern could result from a dynamic interaction that entails mediation by a specific cortical network with associated hippocampal disengagement. We now report that, in male and female adult human subjects, encoding and later consolidation of a series of objects embedded in a semantic schema was associated with a buildup of activity in the angular gyrus (AG) that predicted memory 24 h later. In parallel, the posterior hippocampus became less involved as schema objects were encoded successively. Hippocampal disengagement was related to an increase in falsely remembering objects that were not presented at encoding. During both encoding and retrieval, the AG and lateral occipital complex (LOC) became functionally connected and this interaction was beneficial for successful retrieval. Therefore, a network including the AG and LOC enhances the overnight retention of schema-related memories and their simultaneous detachment from the hippocampus reduces the specificity of the memory. SIGNIFICANCE STATEMENT This study provides the first empirical evidence on how the hippocampus and the neocortex interact dynamically when acquiring and then effectively retaining durable knowledge that is associated to preexisting knowledge, but they do so at the cost of memory specificity. This interaction is a fundamental mnemonic operation that has thus far been largely overlooked in memory research.
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Numerous cognitive domains have been associated with the lateral parietal cortex, yet how these disparate functions are packed into this region remains unclear. Whilst areas within the dorsal and the ventral parietal cortex (DPC and VPC) show differential function, there is considerable disagreement as to what these functions might be. Studies focussed on individual domains have plotted out variations of function across the region. Direct cross-domain comparisons are rare yet, when they have been undertaken, at least some regions (particularly the intraparietal sulcus [IPS] and core angular gyrus [AG]) appear to have contrastive domain-general qualities. In order to pursue this parietal puzzle, this study utilized both functional and resting-state magnetic resonance imaging to investigate a potential unifying neurocomputational framework-in which both domain general as well as domain-selective regions arise from differential patterns of connectivity into subregions of the lateral parietal cortex. Specifically we found that, consistent with their contrastive patterns of functional connectivity, subregions of DPC (anterior IPS) and VPC (AG) exhibit counterpointed functions sensitive to task/item-difficulty irrespective of cognitive domain. We propose that these regions serve as top-down executively penetrated and automatic bottom-up domain-general buffers of active information, respectively. In contrast, other parietal and nonparietal regions are tuned toward specific domains.
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Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.
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Significance Functional neuroimaging has established that most cognitive functions are supported by distributed neural networks. Hundreds of studies have investigated the semantic network (SN) and the default mode network (DMN) (neural deactivation when undertaking a variety of tasks). These stable networks are increasingly used as biomarkers in neurological and psychiatric investigations. Despite implicating overlapping neural regions and shared cognitive mechanisms, the relationship between the two networks has received minimal attention. Analyses of a large multitask distortion-corrected functional MRI (fMRI) dataset established that both networks fractionate, depending on the semantic nature of the task, stimulus type, modality, and task difficulty. The implications for the SN, variability in the DMN and its cognitive coherence, and interpretation of resting-state fMRI data are discussed.
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How is higher cognitive function organized in the human parietal cortex? A century of neuropsychology and 30 years of functional neuroimaging has implicated the parietal lobe in many different verbal and nonverbal cognitive domains. There is little clarity, however, on how these functions are organized, that is, where do these functions coalesce (implying a shared, underpinning neurocomputation) and where do they divide (indicating different underlying neural functions). Until now, there has been no multi-domain synthesis in order to reveal where there is fusion or fission of functions in the parietal cortex. This aim was achieved through a large-scale activation likelihood estimation (ALE) analysis of 386 studies (3952 activation peaks) covering 8 cognitive domains. A tripartite, domain-general neuroanatomical division and 5 principles of cognitive organization were established, and these are discussed with respect to a unified theory of parietal functional organization.
We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.
Many sources of fluctuation contribute to the functional magnetic resonance imaging (fMRI) signal, complicating attempts to infer those changes that are truly related to brain activation. Unlike methods of analysis of fMRI data that test the time course of each voxel against a hypothesized waveform, data-driven methods, such as independent component analysis and clustering, attempt to find common features within the data. This exploratory approach can be revealing when the brain activation is difficult to predict beforehand, such as with complex stimuli and internal shifts of activation that are not time-locked to an easily specified sensory or motor event. These methods can be further improved by incorporating prior knowledge regarding the temporal and spatial extent of brain activation.