Dissociation and convergence of the dorsal and ventral visual streams in the human prefrontal cortex.

Article (PDF Available)inNeuroImage 65 · October 2012with37 Reads
DOI: 10.1016/j.neuroimage.2012.10.002 · Source: PubMed
  • 29.55 · Boston Children's Hospital, Harvard Medical School
  • 32.45 · Kyushu University
  • 36.26 · Korea Advanced Institute of Science and Technology
Abstract
Visual information is largely processed through two pathways in the primate brain: an object pathway from the primary visual cortex to the temporal cortex (ventral stream) and a spatial pathway to the parietal cortex (dorsal stream). Whether and to what extent dissociation exists in the human prefrontal cortex (PFC) has long been debated. We examined anatomical connections from functionally defined areas in the temporal and parietal cortices to the PFC, using noninvasive functional and diffusion-weighted magnetic resonance imaging. The right inferior frontal gyrus (IFG) received converging input from both streams, while the right superior frontal gyrus received input only from the dorsal stream. Interstream functional connectivity to the IFG was dynamically recruited only when both object and spatial information were processed. These results suggest that the human PFC receives dissociated and converging visual pathways, and that the right IFG region serves as an integrator of the two types of information.
Cerebral Cortex August 2008;18:1771--1778
doi:10.1093/cercor/bhm204
Advance Access publication December 28, 2007
Dissociated Pathways for Successful
Memory Retrieval from the Human
Parietal Cortex: Anatomical and
Functional Connectivity Analyses
Emi Takahashi
1,2
, Kenichi Ohki
3
and Dae-Shik Kim
1
1
Department of Anatomy and Neurobiology, Center for
Biomedical Imaging, Boston University School of Medicine,
Boston, MA 02118, USA,
2
Department of Radiology,
Massachusetts General Hospital, Harvard Medical School, A. A.
Martinos Center for Biomedical Imaging, Charlestown, MA
02129 and
3
Department of Neurobiology, Harvard Medical
School, Boston, MA 02115, USA
The parietal cortex has traditionally been implicated in spatial
attention and eye-movement processes. Recent functional neuro-
imaging studies have found that activation in the parietal cortex is
related to successful recognition memory. The activated regions
consistently include the intraparietal sulcus in the lateral parietal
cortex and the precuneus in the medial parietal cortex. However,
little is known about the functional differences between lateral and
medial parietal cortices in the memory retrieval process. In this
study, we examined whether the human lateral and medial parietal
lobes have differential anatomical and functional connectivity with
the temporal lobe. To this end, we used functional magnetic
resonance imaging to constrain the analysis of anatomical
connectivity obtained by diffusion tensor imaging (DTI). Both DTI
tractography and functional connectivity analysis showed that the
lateral parietal region has anatomical and functional connections
with the lateral temporal lobe, and the medial parietal region has
connections with the medial temporal lobe. These results suggest
the existence of segregated lateral and medial parieto-temporal
pathways in successful memory retrieval.
Keywords: diffusion tensor imaging, functional connectivity, long-term
memory, parietal cortex, temporal lobe
Introduction
The parietal cortex is thought to be involved in attention and
eye-movement processes (Hyvarinen 1982; Corbetta 1998;
Colby and Goldberg 1999; Mesulam 1999). In addition, recent
functional neuroimaging studies have found activation in the
parietal cortex during various memory tasks, especially those
related to successful recognition memory (Henson et al. 1999;
Donaldson et al. 2001; Konishi et al. 2001; Cansino et al. 2002;
Dobbins et al. 2003; Rugg et al. 2003; Wheeler and Buckner
2003; Herron et al. 2004). Most studies have identified several
regions in the parietal cortex that respond to ‘‘Hits’ (when
subjects correctly recognize previously studied old items)
more than to ‘Correct rejections’’ (when they correctly
identify new items). The regions in which this effect was
identified consistently include intraparietal sulcus (IPS or
Brodmann areas [BA] 7), medial parietal cortex (the precuneus
[PCu] or medial BA 7, and posterior cingulate cortex or BA 23/
31), and several prefrontal regions. Although the roles of the
prefrontal cortex (PFC) in successful memory retrieval have
been relatively well studied (Buckner, Koutstaal, Schacter, Dale,
et al. 1998; Buckner, Koutstaal, Schacter, Wagner, et al. 1998;
Henson et al. 1999, followed by many other studies), those of
the parietal cortex are still poorly understood (Shannon and
Buckner 2004; Naghavi and Nyberg 2005; Wagner et al. 2005;
Cavanna and Trimble 2006). Because it is well known that
declarative memory relies on the medial temporal lobe (MTL)
and lateral temporal cortex, a key strategy for studying
successful memory retrieval in the parietal cortex is to study
its relationship with the temporal lobe. Given the accumulating
knowledge on the functionally dissociated roles of the
temporal lobe in terms of memory, studying connectivity
between IPS/PCu and the temporal lobe should be fundamental
to understand the roles of the parietal regions in memory.
Neuropsychological studies suggest that different types of
memory depend on separated cortical structures (Squire 1994;
Takahashi and Miyashita 2002). For example, patients with left
lateral temporal lobe damage have impaired memory for
semantic knowledge (De Renzi et al. 1987; Snowden et al.
1989; Hart and Gordon 1990), but relatively preserved memory
for episodic information (De Renzi et al. 1987; Snowden et al.
1994). Recent functional imaging studies also showed that
lateral temporal cortex is activated by semantic memory
retrieval or item-based memory, whereas the MTL is activated
by episodic memory retrieval or relational memory (Wiggs et al.
1999; Lee et al. 2002; Konishi et al. 2006). Based on these
functional dissociations between the MTL and lateral temporal
cortex studies of the specific connections between these areas
should provide insight into the process of memory retrieval in
parietal cortex.
Diffusion tensor imaging (DTI) is a technique that measures
the diffusion properties of water molecules from diffusion-
weighted magnetic resonance images (Basser et al. 1994).
Tractography algorithms are then applied to DTI data to
reconstruct connections between various brain regions
(Conturo et al. 1999; Jones et al. 1999; Mori et al. 1999). Many
studies have used DTI to show anatomical connections in the
human brain (Conturo et al. 1999; Basser et al. 2000; Stieltjes
et al. 2001; Xu et al. 2002; Behrens et al. 2003; Lehericy et al.
2004; Powell et al. 2004), and recent studies have used DTI to
assess connectivity between functionally defined regions of
interest (ROIs) (e.g., Guye et al. 2003; Toosy et al. 2004;
Dougherty et al. 2005; Kim et al. 2006; Takahashi et al. 2007). In
this study, we use a boot-trac algorithm (Lazar and Alexander
2005; Takahashi et al. 2007) to create probabilistic maps of DTI
tractography. We find that IPS is connected with a lateral
temporal region, whereas PCu is connected with MTL. This
result suggests that IPS has an important role in retrieval of
items’ information stored in the lateral temporal cortex, and
PCu interacts with MTL to recall relational memories.
Although DTI provides information about anatomical path-
ways in vivo, there is no functional interpretation in the
reconstructed fibers themselves. We performed a whole-brain
functional connectivity analysis, looking at the functional
coupling between parietal ROIs. This analysis based upon the
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functional magnetic resonance imaging (fMRI) data identified
significant interactions with the temporal lobe that showed
a regional specificity. We found that IPS was correlated with
the lateral temporal cortex, and PCu was correlated with MTL.
These results suggest that, with respect to successful memory
retrieval, lateral and medial parieto-temporal pathways are
anatomically and functionally dissociated.
Methods
Subjects
Twenty healthy normally sighted subjects were tested (8 males and
12 females, aged 21--39, mean age 25). All subjects reported themselves
to be native speakers of English, right handed, with no neurological or
psychiatric histories. Written informed consent in accordance with the
Declaration of Helsinki was obtained from each subject after the nature
and possible consequences of the studies were explained. The
procedures were approved by Boston University School of Medicine.
Stimuli
Word stimuli consisted of a pool of 216 words (upper case, 3--7 letters
in length, 1--200 occurrences per million; Kucera and Francis 1967). We
also used nonword stimuli (alphabetical) for the encoding task and the
number sign (####) for the retrieval task as control stimuli. The
nonword stimuli contained both consonants and vowels, but the order
of letters was randomized. Alphabetical stimuli were made using
random sequences of letters having the same length as the word
stimuli. All stimuli were presented on a tangential screen 1.1 m from
the subjects. Words were white on a black background, occupied 3.10°
3 1.30° to 7.30° 3 1.30° of visual angle and appeared at the center of
the screen. All stimuli were presented using presentation software
(Neurobehavioral Systems, Inc., Albany, CA).
Procedure
The experiment consisted of 2 parts, an encoding phase and a retrieval
phase. fMRI data were acquired during both the encoding and the
retrieval phase. In the current study, only the data of the retrieval phase
were used. The results of the encoding phase were reported previously
(Takahashi et al. 2007). In the encoding phase, subjects were asked to
perform 4 different ‘encoding’ tasks: 1) make a living/nonliving
judgment (deep encoding), 2) detect a given letter within a nonword
letter sequence (shallow encoding), 3) press a random button, and 4)
fixate on a central target. There were 48 blocks of 6 trials each in the
encoding phase. Each trial lasted 4.0 s. At the beginning of each block,
an instruction was shown that specified the type of encoding task to be
performed. In the ‘‘living/nonliving’ blocks, subjects decided whether
each word was animate. In the ‘detection’ blocks, they decided
whether each word contained an ‘‘E.’ In the both ‘‘living/nonliving’ and
‘detection’’ blocks, subjects reported their response by pressing 1 of
2 buttons held in the right hand. In the ‘random button press’ (visuo-
motor control) blocks, they looked at each nonword random letter
sequence and pressed 1 of the 2 buttons held in the right hand. In
the fixation blocks, they looked at the fixation cross and did not press
any buttons. Each word was presented only once throughout the
encoding phase. The length, percentage of living words, and the
percentage of words containing ‘E’ were each balanced across all
the living/nonliving and detection blocks. The ordering of living/
nonliving and detection blocks, and stimuli used in both blocks were
counterbalanced across subjects. The encoding phase lasted approx-
imately 25 min.
The retrieval phase (4 runs) started about 20 min after the end of the
encoding phase. During the interval between the encoding and the
retrieval phases, subjects performed a distracter task (white circle
detection out of 4 circles) to disengage various strategies for encoding.
In the retrieval phase, subjects performed randomly intermixed
retrieval trials, visuo-motor control trials, and fixation trials. Each run
consisted of 72 trials. In the retrieval trials, subjects made yes/no
recognition memory judgments for previously studied and new stimuli.
Subjects reported their response by pressing 1 of the 2 buttons held in
the right hand. Half of the words from the encoding phase were
presented again (72 words: 36 words were deep-encoded, the other 36
words were shallow-encoded), along with new words (72 words). In
the visuo-motor control trials, they looked at the number sign (####)
and pressed the 3rd button that was specifically assigned for this trial
type. In the fixation trials, they looked at the fixation cross and did not
press any buttons. Each trial was 4.0 s long, and the 4 trial types
occurred with equal probability across the experiment in pseudoran-
dom sequence. The stimulus onsets were not jittered. The retrieval
phase lasted approximately 20 min.
Mean percent correct in the deep and shallow conditions did not
differ significantly during the encoding phase (P
>
0.05; 2-tailed t-test)
(Supplementary Table S1). Reaction times of the deep and shallow
encoding, and visuo-motor control tasks were significantly different
(F
2,54
= 13.8, P
<
10
4
, 1-way ANOVA). In the post hoc Tukey’s t-test,
the visuo-motor reaction times were significantly different from both
the deep and shallow encoding tasks (P
<
0.05), but the deep and
shallow encoding tasks were not significantly different (P
>
0.05).
In the retrieval phase, the percent correct for deeply encoded words
was significantly higher than the percent correct for shallowly
encoded words, thus confirming that the words were more deeply
encoded during the living/nonliving judgment task than in the
detection task. Reaction times were not significantly different between
deeply encoded words and shallowly encoded words (P
= 0.3; 2-tailed
paired t-test). Correct retrieval judgments were made on 73.6% of trials
for the studied words (‘‘Hits’’) and 64.5% for new words (‘‘correct
rejections’’).
Image Acquisition
A 3-Tesla whole-body scanner (Intera, Philips) was used to acquire T
1
-
weighted anatomical images, gradient-echo, echo-planar T
2
*-weighted
blood oxygen level--dependent sensitive images, and spin-echo echo-
planar imaging (SE-EPI) diffusion-weighted images (DWIs) for the DTI
data sets. For each subject, 16 data sets were acquired (15 diffusion
weighted
+
1 nondiffusion weighted images). From these data, diffusion
tensors were calculated for all image pixels. Functional data were taken
in the identical field of view (FOV) with diffusion tensor images to
simplify post hoc spatial registration. Subsequently, the foci of fMRI
activations were used as seeding points for DTI fiber reconstruction
algorithms.
Parameters for functional image acquisition were as follows:
repetition time (TR)
= 4 s; echo time (TE) = 35 ms; flip angle = 90°;
in-plane resolution 1.8
3 1.8 mm
2
; FOV = 230 3 230 mm
2
; number of
slices 36; slice thickness 4 mm. Slice orientation was axial, and the
imaging volume was aligned to cover the whole brain. For each subject,
conventional T
1
-weighted structural images were obtained to provide
anatomical information. Each scanning run commenced with the
acquisition of 2 dummy volumes, allowing tissue magnetization to
achieve a steady state, after which functional volumes were acquired
(85 volumes for each encoding run, and 73 volumes for each retrieval
run).
DWIs were acquired using multislice SE-EPI. Parameters for DTI
acquisition were as follows: TR
= 17.1 s, TE = 80 ms, matrix size 128 3
128, FOV 230 3 230 mm
2
, fat suppression, number of slices = 96, slice
thickness
= 1.5 mm, b = 1000 s/mm
2
, 15 directions, gradient strength =
0.2 G/mm, P reduction = 2.0. A total of 4 signal averages were collected
to ensure a sufficient signal-to-noise ratio for high-quality tensor
mapping. In order to compensate for motion, each scan was acquired
separately and then coregistered with the others before averaging. The
sensitivity encoding technique (SENSE) was used, which is known to
reduce susceptibility artifacts significantly (Jaermann et al. 2004).
FMRI Analyses
All functional images were analyzed with SPM99 (Wellcome De-
partment of Neurology, UK). For each subject, the acquired images
were realigned to the 1st volume to correct for head movement.
Differences in acquisition timing between each slice were corrected
for using sinc-interpolation. Each volume was spatially normalized to
a standard EPI template of 2-mm cubic voxels in the Talairach and
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Tournoux space (Talairach and Tournoux 1988) using nonlinear basis
functions. Each image was smoothed spatially with a Gaussian kernel of
8-mm full-width half-maximum (FWHM), and the time series was
smoothed temporally with a 4-s FWHM Gaussian kernel. Slow signal
drifts were removed by high-pass filtering using cut-off periods of 128 s.
For each voxel, data were best fitted (least square) using a linear
combination of regressors. The regressors were constructed to
correspond to each trial type for each subject and then convolved
with the standard hemodynamic response function (HRF). In the
retrieval phase, we separated correct and incorrect trials (‘‘Hit,’’
‘Correct Rejection,’ ‘‘Miss,’ and ‘‘False Alarm’’), and made 5 regressors,
corresponding to these trial types and ‘‘visuo-motor control.’’ Trials in
which the subject did not report a response by pressing a button in the
retrieval phase (9 trials) were explained by an extra regressor.
Contrasts were 1st performed at the single subject level and then the
resulting images were taken up to the group level using t-tests. The
statistical threshold was set to P
<
0.001 as an initial height threshold
and to P
<
0.05 corrected for whole-brain multiple comparisons at
cluster level, according to the SPM99 standard procedures (Friston
et al. 1994). The location of each cluster was indicated by peak voxels
on the normalized structural images and labeled using the nomencla-
ture of Talairach and Tournoux (1988).
DTI Analyses
DTI images were realigned using the diffusion toolbox in SPM2. The 1st
images of each run were realigned to the 1st image of the 1st run.
This procedure removed eddy current-induced distortions. Then all the
images were averaged across the 4 runs. For each voxel, the diffusion
tensor and fractional anisotropy (FA) were calculated using standard
procedures (Basser et al. 1994). We removed voxels that had extremely
large residuals after fitting the 15 DWIs by an ellipsoid tensor. When the
residual exceeds 35% of the value of apparent diffusion constant
averaged across the whole brain, those voxels were removed.
Approximately, 20% of the voxels were removed and those voxels
were distributed along the edge of the brain. Using the T maps
generated by SPM99, which are the result of the random effect analysis
of 20 subjects, we created starting points for DTI fiber tracking. We
used the same statistical criteria (P
<
0.05 corrected at the cluster
level) as a criterion for identifying regions that were the reference or
seed points. The starting points for fiber tracking were set in intervals
of 1 mm in the foci of fMRI activation clusters. The coordinates of the
activated clusters in Talairach coordinates were reverse normalized
into each subject’s coordinates, and used as the basis for fiber tracking
and determining the coordinates of the end points of the fibers. Then
we normalized the end points’ coordinates, averaged the data across all
the subjects, and superimposed the resulting maps onto the normalized
T
1
-weighted images. The reverse normalization and normalization of
the coordinates were performed in the following way. We took T
1
and
DWI scans in the same FOV, and using SPM99, we transformed the end
points coordinates of each subject into Talairach space, averaged the
data across all the subjects, and superimposed the resulting maps onto
the T
1
-weighted images in Talairach space. For each subject, we applied
reverse conversion of normalization to the seed points, using in-house
Matlab (The Mathworks, Inc., Natick, MA) programs. This approach is
better than normalizing DWIs directly because the resolution of DWIs
was reduced when we resample DWIs during normalization.
DTI Tractography
Diffusion tensors, FA, and fiber tracts were calculated using custom-made
Matlab programs. We used a tractography algorithm based on the
method described in Lazar et al. (2003). At every position along the fiber
trajectory, a diffusion tensor is interpolated (linear) and eigenvectors are
computed. The eigenvector associated with the greatest eigenvalue
indicates the principal direction of water diffusion. The fiber tract is
propagated along this direction over a small distance (0.5 mm) to the
next point where a new diffusion tensor is interpolated. Fiber tracking
terminates when the angle between 2 consecutive eigenvectors is
greater than a given threshold (60°), or when the FA value is smaller than
a given threshold (0.14). The criteria of FA
<
0.14--0.15 is reported to
provide the best tradeoff between fewer erroneous tracts and
penetration into the white matter (Thottakara et al. 2006). This tensor
deflection algorithm is 1 of the tensorline approaches that was devel-
oped to overcome crossing fiber problem (Alexander et al. 2001) by
using the entire tensor information instead of reducing it to a single
eigenvector (Lazar et al. 2003).
Group Study in DTI Tractography
For Figure 1A, we performed DTI tractography from all voxels in each
activated cluster. To establish a dissociation between the anatomical
connectivity of the medial and lateral parietal areas, we used a heuristic
algorithm that propagated streamlines from both regions. The
termination points of the streamlines were assessed by evaluating the
probability of an end point, from a particular seed region, falling within
a voxel, over subjects. The resulting map was superimposed onto
normalized T
1
-weighted anatomical images. The maps were reported as
percentage of subjects in whom connections were found.
Estimation of Tractography Error by the Bootstrap Method
In this study, the dispersion errors in DTI tractography were estimated
by a statistical nonparametric bootstrap method (Lazar and Alexander
2005; Takahashi et al. 2007) to ensure that the tractography was reliable
by evaluating intra- and intersubject variability. For each gradient
direction, 2 out of 4 data sets (volumes) were selected randomly and
averaged. This comprised one 15-direction data set, and diffusion tensors
and reconstructed fibers were calculated. We repeated this procedure
100 times, and created probabilistic maps that are based on how many
times, out of 100, fibers passed each voxel in the brain. A probabilistic
map from multiple seeds was obtained as follows. First, we performed
fiber tracking from all the seed points, for each bootstrap sample. We
defined a voxel, which was passed by at least 1 fiber as ‘‘1,’ and a voxel
which was not passed by any fiber as ‘‘0.’ When more than 1 fiber passed
a voxel, we defined it as ‘1. We performed these procedures for 100
bootstrap samples, obtained probabilities for each voxel to be ‘1,’ and
defined this as a probabilistic map.
Functional Connectivity Analyses
Functional connectivity analysis was performed from the same ROIs with
DTI fiber-tracking analyses. The fMRI signals were preprocessed for
realignment, slice timing correction, normalization, and spatial smooth-
ing, as the same way as described above (see fMRI Analyses). Then low-
frequency drifts in the time course in each voxel were removed. We
excluded the task-specific regressor and used 2 regressors, which are the
time series from the 2 parietal regions, not convolved with the HRF.
Because of the spatial smoothing of the fMRI data, a single voxel’s activity
can be thought of as representing the activity of the region around the
voxel. Peak voxels were used for functional connectivity analysis
(Talairach coordinates
44,
66, 46 in left IPS and 24,
62, 22 in PCu).
We obtained maps of correlation coefficients between residual time
courses in the peak voxel (either IPS or PCu) and other voxels were
calculated for individual subjects. To make inferences at the between-
subject level about the functional connectivity encoded by these
correlation coefficient maps, we transform them into summary statistics
using Fisher’s Z transform. These within-subject maps were then
passed to a 2nd-level or between-subject 1-sample t-test to provide
statistical parametric mapping (SPMs) in the usual way. The same analysis
was previously used by other groups (Dosenbach et al. 2007). T values
were corrected for multiple comparisons for a whole brain at voxel level
(P
<
0.05), and superimposed onto normalized T
1
-weighted anatomical
images. Mean z-values across all subjects were also calculated. To find
areas that displayed a functional connectivity with both parietal regions,
we show the voxels surviving an uncorrected threshold of P
= 0.001 in
both functional connectivity maps.
Results
Subjects Performances and Functional MRI
Twenty healthy normally sighted subjects were tested. Correct
retrieval judgments were made on 73.6% of trials for the
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studied words (‘‘Hits’’) and 64.5% for new words (‘‘Correct
rejections’’). In the comparison between Hits and Correct
rejections, the following brain areas were activated: left IPS,
right PCu, left superior temporal gyrus (STG), right middle
temporal gyrus (MTG), left middle frontal gyrus, right inferior
frontal gyrus, and cerebellum (Table 1).
Group Study in DTI Tractography
We used DTI tractography to reconstruct fibers originating
from IPS and PCu. The distribution of fiber terminals from 20
subjects is shown in Figure 1A. Probabilistic maps were
obtained by a statistical nonparametric bootstrap method
(Lazar and Alexander 2005; Takahashi et al. 2007), and
displayed in Figure 1B (note that only terminal points of the
reconstructed fibers are displayed in Figure 1A, whereas the
entire fibers are displayed in Fig. 1B). The IPS had connections
with lateral temporal cortex (STG [BA 22], the MTG [BA 21],
and the fusiform gyrus [FG, BA 37]) and bilateral superior
colliculi (Fig. 1A, upper row). PCu had connections with MTL
(hippocampus, parahippocampal gyrus, the occipital cortex
[BA 17, 18, 19], and posterior cingulate cortex (Fig. 1A, lower
row).
Functional Connectivity
Functional connectivity analysis was performed from the same
ROIs with DTI fiber-tracking analyses. Regions with a significant
functional correlation with an activated region in PCu
(Talairach coordinates: x, y, z
= 24,
62, 22) is shown in Figure
1C (upper row) and Figure 1D (yellow and green). Activity in
bilateral hippocampi, parahippocampal gyri, occipital cortices
(BA 17, 18, 19), posterior insula, anterior cingulate cortices (BA
32, 24), posterior cingulate cortices (BA 23, 31), PCu (BA 7),
inferior parietal lobes (BA 39) was highly correlated with
activity in PCu (P
<
0.05, corrected for whole-brain multiple
Figure 1. (A, B) Results of fiber-tracking analyses. (A) The average results of 20 subjects’ terminal points of DTI fiber tracking. The left hemisphere of the brain corresponds to
the left side of the image. Fiber tracking was performed from the PCu (upper row) and left IPS (lower row) activation clusters revealed by ‘‘Hits versus Correct rejections’’
condition in all the 20 subjects. The activation clusters are shown in red. The terminals of the reconstructed fibers are displayed in yellow/green. The color scale represents the
proportion of the subjects. White arrows show MTL (upper row) and the lateral temporal cortex (lower row). (B) Probabilistic maps of fiber tracking based on the boot-trac
analysis from the same activated clusters in (A) (upper row: PCu, lower row: left IPS). The color scale represents probabilities of fibers. (C, D) Results of functional connectivity
analyses. (C) The results of t-test on z transformed of correlation coefficients using activated regions in PCu (upper row) and left IPS (lower row) corrected for multiple
comparisons for whole-brain at the voxel level (P \ 0.05). (D) A map of mean z value of correlation (z [ 0.3) using activated regions in PCu and left IPS.
1774 Dissociated Connections for Successful Memory Retrieval
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comparisons at voxel level). Figure 1C (lower row) and Figure
1D (blue and green) show maps of significant functional
correlation with an activated region in left IPS (x, y, z
=
44,
66,
46). The activity in bilateral frontal operculum (BA 47), IPS
(BA 7), inferior parietal lobes (BA 39, 40), superior parietal
lobes (BA 7), supplementary motor area (BA 8), PCu (BA 7),
middle frontal gyri (BA 8), left MTG (BA 21, 37), inferior frontal
gyrus (BA 44), superior frontal gyrus (BA 6) was highly
correlated with activity in the left IPS (P
<
0.05, corrected
for multiple comparisons at voxel level).
Segregations of the Two Pathways in Anatomical and
Functional Connectivity
The terminal points of reconstructed fibers from IPS and PCu
exhibited very little overlap (11.0% compared with the total
terminal points from IPS, or 3.3% compared with those from
PCu; using a threshold of 5 subjects). Functional connectivity
(at the threshold of P
<
0.05, corrected for multiple
comparisons for a whole brain at voxel level) also exhibited
little overlap (5.68% compared with all the correlated areas of
IPS, and 3.58% compared with those of PCu). At a lower
threshold of z
>
0.3 (mean across all the subjects), overlapping
areas were 10.1% compared with functionally correlated areas
with IPS, and 6.92% compared with those with PCu (Fig. 1D,
yellow: areas correlated with PCu, blue: areas correlated with
IPS). Overlaps were found in posterior cingulate/PCu and
bilateral inferior parietal lobes (Fig. 1D, brown).
Functional Segregations of the Two Pathways
Mean percent signal changes in PCu (Fig. 2A) and IPS (Fig. 2B)
showed clearly different patterns in the ‘‘Hit’ condition. In PCu,
there is almost no signal change in the ‘Hit’ compared with the
control condition, whereas IPS showed almost the same
amount of signal change with ‘Hit’’ and ‘Miss’’ conditions. In
both areas, ‘‘False alarm’’ and ‘Correct rejection’ conditions
showed decreased signal changes compared with baseline.
Next, we show that the pattern of activity in the clusters in
the left MTG and parahippocampal gyrus depend on the
memory performance of the subjects (Hits, Misses, False alarms,
and Correct rejections). These clusters were defined by
functional connectivity analysis with the IPS and the PCu,
respectively. For the MTG (Fig. 2C), F
4,95
= 4.66, P = 0.0018, and
there were significant differences between Misses and False
alarms, and Misses and Correct rejections (Tukey’s t-test P
<
0.05). For the parahippocampal gyrus (Fig. 2D), F
4,95
= 3.6, P =
0.009, and there were significant differences between Misses
and False alarms, and Misses and Correct rejections (Tukey’s t-
test P
<
0.05). Correlations between the z-scores for functional
connectivity and success rates (Buchel et al. 1999; Hampson
et al. 2006) were also examined across all the subjects (n
= 20).
The functional connectivity between IPS and MTG was
positively correlated to success rates (r
= 0.63, P = 0.0028)
and that between PCu and MTL was negatively correlated to
success rates (r
=
0.7167, P = 0.004, see also Supplementary
Fig. S1). Thus, the MTG and parahippocampal gyrus show
a similar pattern of activity to the IPS and PCu, which suggests
that the connections between the IPS and MTG and that
between PCu and parahippocampal gyrus are involved in
successful memory retrieval.
Discussion
The major goal of this study was to examine memory-related
function of the parietal cortex and its connectivity with the
temporal lobe. Using a combination of DTI tractography and
functional connectivity analyses, we demonstrated that the
lateral parietal region (IPS) is connected with the lateral
temporal cortex (MTG), and the medial parietal region (PCu) is
connected with the MTL (hippocampus/parahippocampal
gyrus). The pattern of activity in memory retrieval conditions
also showed a similar dissociation between the parietal and
temporal cortices. The IPS region had more activity than PCu in
the ‘‘Hit,’ ‘‘Correct rejection,’ and ‘False alarm’’ compared with
‘Control’’ conditions, but not in the ‘‘Miss’ compared with
‘Control’’ condition. These differences were observed in all
areas dissociated by the functional connectivity analyses. Our
Table 1
Hit versus correct rejection
Cluster size T values Coordinate (mm) BA
xyzL/R
2896 6.41 24 62 22 R BA7 PCu
729 5.88 44 60 6 R BA37 MTG
562 5.4 44 66 46 L BA7 IPS
188 5.18 34 12 58 L BA6 Middle frontal gyrus
223 5.14 48 38 18 L BA22 STG
110 4.66 8 78 40 R Cerebellum
174 4.51 34 16 18 R BA47 Inferior frontal gyrus
84 4.48 38 40 18 L Cerebellum
98 4.03 4 64 12 R Cerebellum
Note: Regions activated in the comparison ‘‘Hits versus Correct rejections.’’ Only clusters with
a significant activity of P \ 0.05 corrected for whole-brain multiple comparisons are reported.
The coordinates and their T values are at the peak voxels in each cluster, and the coordinates and
approximate BA are indicated in the Talairach and Tournoux atlas space. 1 voxel 5 8mm
3
.
Figure 2. (A, B) Mean percent signal changes in the activated cluster in PCu and IPS.
(A) Mean percent signal changes of the cluster in PCu (including the peak voxel, x, y, z
5 24, 62, 22). (B) Mean percent signal changes of the cluster in IPS (including the
peak voxel, x, y, z 5 44, 66, 46). FA, false alarms; CR, correct rejections; Ctrl,
control. (C, D) Pattern of activity in the clusters in the left MTG and parahippocampal
gyrus. (C) Mean percent signal changes of the cluster in the parahippocampal gyrus.
(D) Mean percent signal changes of the cluster in the left MTG.
Cerebral Cortex August 2008, V 18 N 8 1775
at Harvard University on September 30, 2011cercor.oxfordjournals.orgDownloaded from
results suggest that IPS and PCu regions contribute differen-
tially to successful episodic memory retrieval through dissoci-
ated pathways.
Comparison with Previous Human and Animal Studies
of Anatomical Connections
Intraparietal Sulcus
Nonhuman primate studies showed that the inferior bank of IPS
has connections with the lateral temporal cortex, especially
with the superior temporal sulcus (STS), inferior temporal (IT)
cortex, and the surrounding areas (Neal et al. 1988; Cavada and
Goldman-Rakic 1989; Neal et al. 1990). STS is a multimodal
region in monkeys, and is thought to be a homolog to human
lateral temporal regions (Pandya and Kuypers 1969; Seltzer and
Pandya 1983; Cavada and Goldman-Rakic 1989), and IT is
known as a visual association area that may be homologous to
a region of the human FG.
A recent DTI study distinguished 3 regions in the human lateral
parietal cortex based on tractography results from the superior
colliculus, parahipocampal gyrus, and premotor cortex (Rush-
worth et al. 2006). They found that the medial IPS, posterior
angular gyrus, and anterior IPS were strongly connected with the
superior colliculus, parahippocampal gyrus, and premotor cortex,
respectively. Our results of the tractography from IPS are
compatible with these results. Given the connections between
IPS and the superior colliculi (Fig. 1A,B), our ROI in the IPS
probably includes a homologous area of the monkey’s lateral
intraparietal area (LIP) (see also discussions and topographical
comparisons of IPS in humans and monkeys: Grefkes and Fink
2005). In monkeys, LIP has connections with the superior
colliculi, superior temporal, medial parietal, and frontal eye field
(Tyan and Lynch 1996; Grefkes and Fink 2005; Stanton et al.
2005). These are in agreement with our results. These results
suggest that there is a common neural network for attention/eye-
movement processes and successful memory retrieval from IPS.
Precuneus
Based on the similarities in global anatomical positions, human
PCu is thought to be a homolog of area 7m (or PGm) in
nonhuman primates, which is located in the posterior medial
parietal cortex, and an upper adjacent area of the posterior
cingulate gyrus. Area 7m is reported to have reciprocal cortico-
cortical connections with adjacent areas of the posteromedial
cortex (the posterior cingulate and retrosplenial cortices)
(Selemon and Goldman-Rakic 1988; Cavada and Goldman-Rakic
1989; Leichnetz 2001). These observations are in agreement
with our current tractography results. Our results also showed
anatomical connections between PCu and the MTL. In non-
human primates, some studies reported connections between
MTL and area 7m (Leichnetz 2001), whereas there are number
of studies that showed connections between the MTL and the
posterior cingulate/retrosplenial cortices (Cavada and Goldman-
Rakic 1989; Kobayashi and Amaral 2003). Our ROI in the PCu for
the tractography analyses might have a small overlap with the
posterior cingulate cortex (Fig. 1 A ).
Role of the Frontal Cortex in Episodic Memory Retrieval
PFC is thought to be a component of the neural network
underlying cognitive control, including the control of memory
(Stuss and Benson 1984; Schacter 1987; Fuster 1997). Models of
PFC function suggest that PFC represents the current task goal
and supports top-down mechanisms that facilitate the process-
ing and maintenance of goal-relevant representations in
posterior cortices (Shallice 1988; Desimone and Duncan
1995; Miller and Cohen 2001). In our results of functional
connectivity, the activity of the left IPS was correlated with that
of bilateral ventrolateral PFC (VLPFC; inferior frontal gyrus,
BA47) and left dorsolateral PFC (DLPFC; middle frontal gyrus
extending into inferior frontal gyrus, BA9/46), whereas the
activity in the right PCu was not highly correlated with the
lateral frontal cortex. Previous neuroimaging studies have
shown that the left VLPFC is important for the control of
semantic memory, including the recovery and evaluation of
meaning (e.g., Petersen et al. 1988; Wagner et al. 1997).
Furthermore, a 2-stage model of PFC on cognitive control
processes proposed that VLPFC subserves controlled retrieval
of the information from posterior cortices (e.g., Wagner et al.
2001), whereas DLPFC mediates the monitoring and manipu-
lation of the information maintained by VLPFC (Petrides 1994;
Owen et al. 1996). In our previous diffusion tractography work
(Takahashi et al. 2007), we showed that there are 2 direct
anatomical pathways to the left lateral temporal cortex from
the left DLPFC and VLPFC. In the current work, we have shown
that activity in the left DLPFC and VLPFC is correlated with that
in the left IPS, but not with that in the PCu. These results
suggest that the left DLPFC, VLPFC, IPS, and lateral temporal
cortex constitute a subnetwork, separated from the PCu and
MTL (parahippocampal/hippocampal areas).
Role of the Parietal Cortex in Episodic Memory Retrieval
In this study, we found anatomical connections and functional
connectivity between IPS and a lateral temporal region, and
between PCu and MTL. MTL has been implicated in recollec-
tion of past episodes (Aggleton and Brown 1999; Eldridge et al.
2000; Takahashi et al. 2002; Yonelinas 2002; Yonelinas et al.
2005) and relational processing (Cohen and Eichenbaum 1993;
Squire 1994; Henke et al. 1999; Davachi and Wagner 2002;
Giovanello et al. 2004; Preston et al. 2004; Prince et al. 2005;
Konishi et al. 2006). On the other hand, the lateral temporal
cortex has been implicated in nonrelational item-based
memory (Wiggs et al. 1999; Lee et al. 2002; Konishi et al.
2006). Our results in the current study suggest that IPS is
related to successful retrieval of items’ information stored in
the lateral temporal cortex, and PCu has interactions with MTL
to recall relational information. Specific functional connectivity
between prefrontal areas (DLPFC/VLPFC) and IPS reinforces
the suggestion. We showed higher activity with successful
memory performance in MTL and MTG. Furthermore, func-
tional connectivity between IPS and MTG was positively
correlated to success rates, whereas that between PCu and
MTL was negatively correlated to success rates. The positive
correlation of success rates to the functional connectivity
between IPS and MTG may indicate that this connectivity is
important to successfully recognize previously presented
items. Interestingly, success rates were negatively correlated
to the functional connectivity between PCu and MTL. This may
suggest that relational memory is recruited when subjects were
not confident about their judgments. In other words, when IPS
failed to retrieve items’ information stored in the lateral
temporal cortex, PCu might try to retrieve relational memory
stored in MTL. Although these results suggest the functional
dissociation of the lateral and medial parietal lobes in episodic
1776 Dissociated Connections for Successful Memory Retrieval
d
Takahashi et al.
at Harvard University on September 30, 2011cercor.oxfordjournals.orgDownloaded from
memory retrieval, further direct evidence to show the detailed
roles in these 2 regions remains for future challenges.
Supplementary Material
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org/
Funding
National Institutes of Health (NS44825); the Human Frontiers
Science Program; and the Uehara Memorial Foundation (Japan)
supported E.T.
Notes
We gratefully acknowledge Jeff Thompson, Robert Levy, and Szymon
Mikulski for their helpful editorial comments. Conflict of Interest : None
declared.
Address correspondence to Emi Takahashi, PhD, Department of
Radiology, Massachusetts General Hospital, Harvard Medical School, A.
A. Martinos Center for Biomedical Imaging, 149 13th St., Room 2301.
Charlestown, MA 02129. Email: emi@nmr.mgh.harvard.edu.
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    • "Unsigned prediction error can be viewed as a measure of absolute deviation from the expected outcome (feedback) and therefore as a proxy for surprise, which is a typical learning signal (Hayden, Heilbronner, Pearson, & Platt, 2011; Pearce & Hall, 1980). Ventrolateral PFC receives converging input from the ventral visual stream, for example, information about the shape and color of stimuli (Takahashi, Ohki, & Kim, 2012; Sakagami & Pan, 2007), which it can then convert into templates for motor commands (Sakagami & Pan, 2007). Previous studies have shown that different regions in medial and lateral frontal cortex selectively interact with task-relevant and task-irrelevant brain areas to maintain sensory information needed for future decisions in memory (Spitzer et al., 2014; King et al., 2010; see Gazzaley & Nobre, 2012, for a review). "
    [Show abstract] [Hide abstract] ABSTRACT: Negative feedback after an action in a cognitive task can lead to devaluing that action on future trials as well as to more cautious responding when encountering that same choice again. These phenomena have been explored in the past by reinforcement learning theories and cognitive control accounts, respectively. Yet, how cognitive control interacts with value updating to give rise to adequate adaptations under uncertainty is less clear. In this fMRI study, we investigated cognitive control-based behavioral adjustments during a probabilistic reinforcement learning task and studied their influence on performance in a later test phase in which the learned value of items is tested. We provide support for the idea that functionally relevant and memory-reliant behavioral adjustments in the form of posterror slowing during reinforcement learning are associated with test performance. Adjusting response speed after negative feedback was correlated with BOLD activity in right inferior frontal gyrus and bilateral middle occipital cortex during the event of receiving the feedback. Bilateral middle occipital cortex activity overlapped partly with activity reflecting feedback deviance from expectations as measured by unsigned prediction error. These results suggest that cognitive control and feature processing cortical regions interact to implement feedback-congruent adaptations beneficial to learning.
    Full-text · Article · May 2016
    • "On the other hand, the ventral visual pathway transfers information from the inferior temporal lobe and has the role of forming representations, color recognition, and object recognition. A study using diffusion tensor imaging indicated that there is a visual pathway between the right temporal cortex and the right frontal gyrus in the ventral stream (Takahashi et al., 2013). Damage to this neural network may cause the positive correlations seen between rCBF and the RCPM scores. "
    [Show abstract] [Hide abstract] ABSTRACT: Objective: Impairment of visual perception frequently occurs in Alzheimer's disease (AD) and can cause severe constraints in daily activities. The nonverbal Raven's Colored Progressive Matrices (RCPM) test consists of sets A, AB, and B and is easily performed in a short time to evaluate both visual perception and reasoning ability. The purpose of this study was to evaluate the neural basis of visual perception and reasoning ability in patients with AD using RCPM and single-photon emission computed tomography (SPECT). Methods: Fifty patients who fulfilled the National Institute on Aging/Alzheimer's Association criteria for probable AD dementia were examined with RCPM and SPECT. All SPECTs were performed using N-isopropyl-p-[(123) I]-iodoamphetamine. A multiple regression model was used to perform multivariate analyses of the relationships between regional cerebral blood flow (rCBF) and RCPM scores. Results: There was a significant positive correlation between RCPM total score and rCBF in the inferior parietal lobes bilaterally, the right inferior temporal gyrus, and the right middle frontal gyrus. Set A was positively correlated with rCBF in the right temporal and right parietal lobes. Set AB was positively correlated with rCBF in the right temporal, right parietal, and right frontal lobes. Set B was positively correlated with rCBF in the right parietal and right frontal lobes. Conclusion: Our findings suggest that deteriorations of specific brain regions are associated with dysfunction of visual perception and reasoning ability in AD. RCPM is another informative assessment scale of cognition for use in patients with AD.
    Full-text · Article · Apr 2016
    • "Sustained activation association with working memory load engaged a network that included a fronto-parieto-striatal network (SupplementalTable 3 ). This is in line with much evidence showing increased activation in these regions in response to working memory demands (Courtney SM et al., 1998; D'Esposito M et al., 1998; Jonides J et al., 1998; Pessoa L et al., 2002; Owen AM et al., 2005; D'Ardenne K et al., 2012; Takahashi E et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: In prospective memory (PM), an intention to act in response to an external event is formed, retained, and at a later stage, when the event occurs, the relevant action is performed. PM typically shows a decline in late adulthood, which might affect functions of daily living. The neural correlates of this decline are not well understood. Here, 15 young (6 female; age range=23-30years) and 16 older adults (5 female; age range=64-74years) were scanned with fMRI to examine age-related differences in brain activation associated with event-based PM using a task that facilitated the separation of transient and sustained components of PM. We show that older adults had reduced performance in conditions with high demands on prospective and working memory, while no age-difference was observed in low-demanding tasks. Across age groups, PM task performance activated separate sets of brain regions for transient and sustained responses. Age-differences in transient activation were found in fronto-striatal and MTL regions, with young adults showing more activation than older adults. Increased activation in young, compared to older adults, was also found for sustained PM activation in the IFG. These results provide new evidence that PM relies on dissociable transient and sustained cognitive processes, and that age-related deficits in PM can be explained by an inability to recruit PM-related brain networks in old age.
    Full-text · Article · Nov 2015
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