Download Large-Scale Functional Connectivity in Associative Learning

Large-Scale Functional Connectivity in Associative Learning:
Interrelations of the Rat Auditory, Visual, and Limbic Systems
A. R. MCINTOSH 1 AND F. GONZALEZ-LIMA 2
1
Rotman Research Institute of Baycrest Centre and Department of Psychology, University of Toronto, Toronto, Ontario
M6A 2E1, Canada; and 2 Department of Psychology and Institute for Neuroscience, University of Texas, Austin,
Texas, 78712
The costs of publication of this article were defrayed in part by the
payment of page charges. The article must therefore be hereby marked
‘‘advertisement’’ in accordance with 18 U.S.C. Section 1734 solely to
indicate this fact.
INTRODUCTION
One approach to the study of the neural basis of learning
and memory involves the exploration of how multiple brain
regions interact in different learned behaviors (GonzalezLima and McIntosh 1994; McIntosh and Gonzalez-Lima
1994a). Rather than focusing on particular neural structures,
the approach emphasizes how the changing relationships
among many regions and/or systems lead to the appropriate
learned behavior (John and Schwartz 1978). It assumes that
learning and memory are ubiquitous properties of neural
tissue and that the involvement of an area in learning and
memory depends on the specific requirements for behavioral
change (Wolpaw and Lee 1989).
Our main emphasis has focused on the ability of the central auditory system to code both the physical parameters of
a given stimulus and its learned behavioral significance. For
example, across several studies we have shown that the determination of stimulus significance can involve auditory
regions as peripheral as the cochlear nuclei (Gonzalez-Lima
and McIntosh 1994). These findings agree with several electrophysiological studies in rodents (Edeline et al. 1990;
Weinberger et al. 1990) and monkeys (Recanzone et al.
1992) and in neuroimaging studies of humans (Molchan et
al. 1994; Schreurs et al. 1997). Furthermore, the classic
division of the central auditory system into lemniscal and
extralemniscal pathways (Weinberger and Diamond 1987)
seems to hold for the determination of stimulus significance
with the extralemniscal pathway becoming more engaged
when the stimuli come to acquire some relevance.
Learning-related changes manifest not only as changes in
activity but also in interactivity. Indeed, the change in the
operation of parallel pathways is best appreciated by examining the influences auditory system regions have on each
other. We observed such learning-related changes in the auditory system when comparing interactions elicited by presentation of a tone trained as a conditioned excitor (a conditioned stimulus that predicts the unconditioned stimulus)
versus the same tone trained as a conditioned inhibitor (a
conditioned stimulus that predicts the absence or withholding of the unconditioned stimulus) (McIntosh and GonzalezLima 1993). Here the most striking differences in neural
interactions were in the extralemniscal brain stem auditory
system at the level of the dorsal cochlear nuclei, suggesting
that the behavioral relevance of a stimulus impacts on the
auditory system at the very earliest stages of processing.
The interactions between parallel auditory pathways also
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McIntosh, A. R. and F. Gonzalez-Lima. Large-scale functional
connectivity in associative learning: interrelations of the rat auditory, visual, and limbic systems. J. Neurophysiol. 80: 3148–3162,
1998. Functional relations between specialized parts of the brain
may be important determinants of learned behaviors. To study this,
we examined the interrelations of the auditory system with several
extraauditory structures in two groups of rats having different behavioral histories. Both groups were trained to associate a tone
conditional stimulus (CS) with an aversive unconditional stimulus
(US). For one group, a light presented with the tone predicted the
absence of the US (group TL 0 ). In the other group, the light
was a neutral stimulus (group TL 0 ). Fluorodeoxyglucose (FDG)
incorporation was measured in the presence of the tone-light compound. Because the tone-light compound was physically identical
for both groups, neural differences between groups reflected differences in the learned associative properties of the stimuli. Covariances of FDG uptake in the auditory system and extraauditory
structures were examined using partial least squares. Three strong
covariance or functional connectivity patterns were identified. The
first pattern mainly reflected similarities between groups, with
strong interrelations between the subcortical auditory system and
the thalamocortical visual system, cerebellum, deep cerebellar nuclei, and midline thalamus. This pattern of interactions may represent part of a common circuit for relaying the associative value of
the tone CS to the cerebellum and the midline thalamus. The external nucleus of the inferior colliculus and medial division of the
medial geniculate nucleus were associated more strongly with this
pattern for group TL 0 , which was interpreted as representing the
change of the associative value of the tone by the light, mediated
through extraauditory influences on these two regions. A second
pattern involved midbrain auditory regions, superior colliculus,
zona incerta, and subiculum and was stronger for group TL 0 . The
relations between midbrain structures may represent the excitatory
conditioned response (CR) evoked by the tone in this group. The
final pattern was strongest in group TL 0 and involved interrelations
of the thalamocortical auditory system with hippocampus, basolateral amygdala, and hypothalamus. This pattern may represent the
learned inhibition of the CR to the tone in the presence of the light.
These findings are consistent with behavioral studies suggesting
that at least two types of associations are formed during associative
learning. One is the sensory relation of the stimuli and another is
the relation between the CS and the affective components of the
US. These behavioral associations are mapped to the patterns of
functional connectivity between auditory and extraauditory regions.
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
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to explore the interactions in terms of covariance patterns
between two or more brain regions. The second technique
incorporates additional information, such as anatomic connections, to quantify explicitly the effect one brain region
has on another. These two approaches are known as functional and effective connectivity, respectively. Both terms
were introduced in the context of electrophysiological recordings from multiple cells (Aertsen et al. 1989; Gerstein
et al. 1978). More recently, they have been used in reference
to neuroimaging data (Friston 1994). In most of our previous examinations of neural interactions, we have focused on
effective connectivity as quantified through structural equation modeling (McIntosh and Gonzalez-Lima 1991, 1994b).
In the present application, we used measures of functional
connectivity to assess large-scale relations of the auditory
system extraauditory regions. Such an examination would
be cumbersome with structural equation models because the
anatomic complexity needed to link all regions would make
the results intractable.
METHODS
Details of the experimental protocol have been published in our
report on the interactions within the auditory system (McIntosh
and Gonzalez-Lima 1995). The behavioral relevance of auditory
and visual stimuli was manipulated using a Pavlovian conditioned
inhibition paradigm (Domjan and Burkhard 1982; Rescorla 1975).
Two groups of rats received pairings of a low-frequency FM tone
(1–2 kHz, 65 dB SPL; stimulus T) with a mild footshock (tone
conditioned excitor: T / ). Conditioned inhibition (T / /TL 0 ) was
trained in group TL 0 (n Å 6) where the tone-light compound
signaled the absence of footshock, making the light the inhibitor
(L 0 , a flashing white light). Group TL 0 (n Å 7) was trained with
the tone as the excitor and the light as a ‘‘neutral’’ stimulus (T / /
TL 0 ). A summary of the training protocol is presented in Table
1. Both groups received an identical number of tone, light, and
footshock presentations. The difference between the two groups
was in the arrangement of the stimulus contingencies between
conditions.
Rats were trained to drink in an operant chamber before conditioning began. The day after baseline training was completed, a
single pretraining session was conducted for each group. In this
session, a high-frequency FM tone (10–20 kHz, 65 dB SPL; stimulus C) was trained as a weak excitor. During the pretraining session, the high-frequency tone was presented 75 times and paired
with the footshock 3 times [C r unconditional stimulus (US);
reinforced probability 0.04]. This made the C stimulus a weak
excitor. The C stimulus was used as a comparator stimulus in the
subsequent probe sessions to assess conditioned inhibition. The
light (L) also was presented, but not reinforced, during pretraining
to help counterbalance the total number of presentations. The pretraining was conducted in a different context than the subsequent
excitatory and inhibitory conditioning to minimize the possibility
of latent inhibition developing (habituation) to the light (Miller
and Schachtman 1985).
For 2 days after pretraining, phase 1 training was conducted.
For both groups, training sessions consisted of three paired presentations of the low-frequency tone and US (T r US, or T / ). The
tone was presented for 15 s and immediately followed by the
footshock (US). The first trial began Ç1 min after the rat was
placed into the chamber, and the intertrial interval averaged 4 min.
On the third day, phase 2 training began. For group TL 0 , sessions
consisted of two reinforced trials of the low tone (T r US) and
four unreinforced trials where the low tone and the flashing light
were presented simultaneously (TL compound). The order of pre-
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appear to change with learning. This was especially evident
in the case where the behavioral relevance of an auditory
stimulus depended on a visual stimulus (McIntosh and Gonzalez-Lima 1995). Two groups of rats received pairings of
a tone (conditioned excitor: T / ) with a mild footshock.
Group TL 0 was trained in a Pavlovian conditioned inhibition
paradigm (T / /TL 0 ) where the tone-light compound signaled the absence of footshock, making the light an inhibitor
(L 0 ). Group TL 0 was trained with the tone as the excitor
and the light as a ‘‘neutral’’ stimulus in that it predicted the
absence of footshock on only 50% of trials. During fluorodeoxyglucose (FDG) incorporation, both groups were presented with the TL compound. The functional interactions in
the auditory system were assessed with anatomically-based
structural equation modeling using the covariances of FDG
activity. Group differences in interactions between the two
pathways were noted mainly at the level of the inferior colliculus (IC) and medial geniculate, possibly reflecting the
unique extraauditory anatomic relation of these regions. Ascending and descending influences from the IC, particularly
the extralemniscal component, were stronger for group TL 0 .
Altered converging effects on auditory cortex (AC) from
the two parallel paths were noted from the ventral division of
medial geniculate (MGV), which is considered a lemniscal
structure, and the medial division of the medial geniculate
nucleus (MGM), which is considered extralemniscal. Effects from MGM on AC were positive and from MGV were
negative for group TL 0 and reversed for group TL 0 . These
data emphasize that auditory system operations are modified
by learning, even in the face of physically identical stimuli.
Because, in the aforementioned study, the relevance of
the auditory stimulus depended on a visual event, it is possible that auditory system interactions with the visual system
and with brain regions that receive information from more
than one modality may have showed a learning-related
change. The goal of the present paper was to focus on extraauditory regions to determine whether the interactions of
these regions with the auditory system depended on the behavioral relevance of a stimulus event. Given that the behavioral task involved integration among auditory, visual, and
somatosensory stimuli, one hypothesis is that the extraauditory regions having the anatomic capacity to integrate information from these sensory modalities should be engaged
preferentially. Anatomic studies of rat neocortex have identified some possible candidates in the perilimbic cortical areas
(Paperna and Malach 1991). These include perirhinal, entorhinal, insular, medial frontal, and retrosplenial cortices. All
of these areas are related anatomically to two or more of
the above sensory modalities. Subcortical candidates are numerous. The superior and inferior colliculi are prime candidates for mediation of auditory-visual interactions, especially
considering the multimodal sensory maps in the deeper layers of the superior colliculus (Huffman and Henson 1990).
Finally, it has been shown that the amygdala is an important
mediator in fear conditioning (LeDoux 1993). Given the
potential for auditory interactions via anatomic projections
from the MGM (LeDoux et al. 1984) and cortical influences
from auditory and visual cortices, there is also potential for
interactions involving the amygdala.
The measurement of neural interactions in neuroimaging
has proceeded using two methods. The first method seeks
3149
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TABLE
A. R. MCINTOSH AND F. GONZALEZ-LIMA
1.
Behavioral design
Pretraining
Group TL0
Group TLo
C r US
C
L
C r US
C
L
Phase 1
(Excitatory Training)
Phase 2
(Inhibitory Training)
FDG Injection
(Neural Activity Test)
T r US
T r US
TL
TL (tone excitor inhibited by
light)
T r US
T r US
TL
TL r US
TL (tone excitor not
inhibited by light)
Total number of stimuli presented was equal for the two groups. Each row indicates the types of stimulus combinations presented during each training
phase, where ‘‘r US’’ indicates the event was followed by footshock. A behavioral test (probe trials) was conducted between phase 2 and fluorodeoxyglucose (FDG) injection. T, low-frequency FM tone; C, high-frequency FM tone; L, flashing light; TL, tone-light compound; unconditioned stimulus (US),
footshock.
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label in the present study reflects initial responses to the tone-light
compound, long before behavioral extinction is observed.
On completion of the 60-min test period, the animal was removed from the chamber and quickly decapitated. The brain was
processed for autoradiography, and after development of autoradiographs, the sections were counterstained as described elsewhere
(Gonzalez-Lima 1992). Quantification of FDG incorporation was
performed using JAVA image analysis software (version 1.4, Jandel Scientific). Areas chosen for image analysis were based on
prior findings (McIntosh and Gonzalez-Lima 1993, 1994a). Three
adjacent brain sections were chosen for the analysis of each region
of interest (ROI). In the present experiment, 63 ROIs were sampled (Table 2). Eleven of these were classified as auditory system
regions. The 14C values from each brain area were divided by the
average 14C value for all regions measured for each animal (wholebrain ratio). There was no systematic difference in the wholebrain means between the groups, validating this adjustment. All
subsequent references to activity values are expressed in terms
of the 14C labeling of the ROI relative to the mean of all ROIs
measured.
Three observations for each structure for each subject were used
to compute the interregional correlations for the analysis of functional connectivity. This was done to ensure that rank of the covariance matrix used for the analysis of functional connectivity (described in the next section) was sufficient to account for the interactions of the 11 auditory regions. Because the number of
observations resulted from multiple measures from the same subject, the resulting correlations were inflated because of this extra
source of variance. But, as the source of this variance was known,
it was removed using a regression procedure (Pedhazur 1982).
Regional activity measures were regressed on two predictor variables that contrast the within-subjects repeated measure, namely
the measures of the same structure. Regressing out the betweensection variance rendered the observations mathematically independent for the purposes of computing interregional correlations.
Thus the resulting correlations were corrected for the extra source
of variance, and the matrix to be analyzed was of sufficient rank
for analysis (McIntosh and Gonzalez-Lima 1991, 1993, 1994a,
1995). Although the procedure does not affect the pattern of covariances (McIntosh and Gonzalez-Lima 1994b), it does not substitute for a larger sample size.
Partial least-squares analysis of functional connectivity
Partial least squares (PLS) is a multivariate tool that can be
used to describe the relation between a one set of measures, like
experimental design or behavioral measures, and a large set of
dependent measures, in this case brain activity. It has been used
extensively for one-dimensional images from spectrographs, as in
chemometrics or remote sensing or toxicology and behavioral teratology (e.g., Heise et al. 1989; Hellberg et al. 1986; Streissguth et
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sentation of the T r US trials and TL trials was randomized each
day. For group TL 0 , three types of trials were conducted, T r US,
TL, and TL r US (where the compound was reinforced). The
combination of reinforced and unreinforced compound stimulus
trials was used to ensure that the light did not acquire properties
consistent with a strong conditioned inhibitor in group TL 0 . Training continued daily for 14 consecutive days.
After phase 2 training, probe trials were conducted to determine
if subjects in both group TL 0 and group TL 0 showed behavior
consistent with conditioned excitation and if subjects in group TL 0
showed conditioned inhibition. This probe session consisted of two
nonreinforced presentations each of the T, L, and the C stimuli, as
well as two presentations of the TL and TC compounds. The TC
compound was presented for the first time during the probe trials.
The presentation of the C stimulus, both alone and in compound
with the T stimulus, was to verify that the conditioned inhibition
developed specifically to the L stimulus and that the behavioral
effects observed were not simply due to the presence of another
stimulus with the T stimulus during compound presentations. Conditioning was evaluated with two behavioral criteria: suppression
of drinking and latency to start drinking after termination of the
test stimulus (Miller and Schachtman 1985). Suppression was
measured using suppression ratios (Annau and Kamin 1961) where
a ratio of zero indicates complete suppression of drinking and a
ratio of 0.5 indicates no suppression. Latency was measured °5
min after stimulus presentation. The expectation for group TL 0 ,
where no conditioned inhibition was trained, was that there would
be little change in the response to the conditioned excitor (T) when
presented in compound with either L or C stimuli. On the day
before FDG injection, all subjects were given another session of
phase 2 training.
For the FDG uptake session, animals received an intraperitoneal
injection of 18 mCi/100 g body wt of [ 14C(U)]2-fluoro-2-deoxyglucose (300–360 mCi/mM spec. activity; American Radiolabeled
Chemicals) in 0.2 ml sterile saline and were immediately placed
in the operant chamber where stimulus presentations began. All
rats were presented with the TL compound during the 60-min FDGuptake period. The TL compound was given in a 5-s on, 1-s off
cycle to optimize the uptake of FDG evoked by the stimuli. To
assess the neural effects of the TL compound, it was not reinforced
during the FDG-uptake period. The effect of the change in conditional stimulus (CS) duration (from 15 to 5 s) was assessed previously with another group of animals, and no detectable behavioral
differences were observed when CS duration was modified (McIntosh and Gonzalez-Lima 1994a). The average time to extinction
of suppressive behavior during FDG uptake was 45 min (standard
error Å 1.57). It has been estimated that peak uptake of the tracer
occurs in the first 5 min postinjection, and most of the remaining
tracer is trapped by 30 min postinjection, and unincorporated FDG
is cleared in the final 30 min (Gonzalez-Lima 1992; Sokoloff et
al. 1977). This suggests that the majority of the resulting FDG
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
TABLE
2.
3151
Regional FDG mean ratios for all areas sampled by group
TL0
Area
R2
P
1.064
1.029
1.145
1.289
{
{
{
{
0.049
0.053
0.067
0.078
0.991
0.972
1.056
1.276
{
{
{
{
0.079
0.063
0.070
0.052
0.282
0.181
0.342
0.003
0.050
0.148
0.032
0.886
1.730
1.118
0.999
1.213
1.107
1.250
0.895
{
{
{
{
{
{
{
0.117
0.061
0.087
0.082
0.062
0.065
0.085
1.750
1.164
0.960
1.202
1.105
1.249
0.864
{
{
{
{
{
{
{
0.193
0.123
0.091
0.086
0.105
0.084
0.061
0.002
0.081
0.013
0.007
0.001
0.011
0.015
0.906
0.350
0.728
0.806
0.900
0.736
0.712
1.004
1.076
1.291
0.742
0.771
0.563
0.736
{
{
{
{
{
{
{
0.021
0.046
0.062
0.042
0.033
0.038
0.025
1.140
1.089
1.338
0.828
0.794
0.596
0.766
{
{
{
{
{
{
{
0.080
0.057
0.038
0.055
0.039
0.047
0.034
0.644
0.055
0.134
0.515
0.169
0.211
0.282
0.002
0.456
0.228
0.016
0.158
0.136
0.058
0.940
1.475
0.741
0.985
1.127
1.023
1.362
1.390
1.194
1.171
0.935
1.006
{
{
{
{
{
{
{
{
{
{
{
{
0.044
0.170
0.021
0.048
0.041
0.065
0.093
0.087
0.069
0.083
0.055
0.056
0.954
1.408
0.741
1.008
1.112
0.988
1.331
1.413
1.227
1.131
0.967
0.997
{
{
{
{
{
{
{
{
{
{
{
{
0.036
0.095
0.027
0.057
0.071
0.060
0.123
0.101
0.023
0.048
0.062
0.048
0.100
0.035
0.011
0.092
0.001
0.091
0.010
0.067
0.169
0.078
0.142
0.007
0.312
0.516
0.724
0.308
0.938
0.316
0.726
0.386
0.148
0.358
0.194
0.778
1.210
0.864
1.091
1.289
{
{
{
{
0.101
0.123
0.136
0.070
1.185
0.847
1.040
1.198
{
{
{
{
0.107
0.051
0.087
0.066
0.069
0.061
0.119
0.346
0.360
0.408
0.242
0.022
1.186
1.000
0.795
0.845
0.721
1.310
1.175
0.881
0.929
1.222
0.998
1.248
1.223
0.647
0.660
0.959
0.747
1.076
1.179
1.154
0.796
0.784
0.756
0.752
0.644
0.933
0.573
0.466
0.870
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
0.087
0.128
0.139
0.147
0.102
0.169
0.125
0.077
0.071
0.103
0.045
0.047
0.036
0.034
0.043
0.096
0.053
0.066
0.070
0.136
0.096
0.101
0.102
0.105
0.097
0.133
0.082
0.079
0.104
1.167
0.929
0.766
0.837
0.684
1.281
1.160
0.831
0.877
1.185
1.002
1.227
1.227
0.682
0.620
0.970
0.775
1.078
1.249
1.231
0.802
0.800
0.767
0.772
0.660
0.949
0.627
0.525
0.863
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
{
0.062
0.062
0.042
0.062
0.040
0.085
0.087
0.071
0.066
0.113
0.060
0.106
0.135
0.049
0.059
0.109
0.086
0.069
0.092
0.135
0.042
0.059
0.041
0.037
0.036
0.085
0.026
0.025
0.024
0.085
0.191
0.079
0.034
0.134
0.007
0.001
0.088
0.117
0.038
0.001
0.036
0.000
0.225
0.150
0.010
0.062
0.021
0.223
0.016
0.001
0.035
0.036
0.049
0.028
0.022
0.214
0.262
0.000
0.382
0.154
0.396
0.528
0.296
0.752
0.930
0.318
0.242
0.502
0.900
0.652
0.982
0.106
0.188
0.736
0.424
0.676
0.084
0.676
0.840
0.524
0.542
0.538
0.654
0.628
0.088
0.036
0.950
Column marked ‘‘R2’’ indicates the squared correlation from a simple regression analysis for difference between groups (column ‘‘P’’ is the probability
value). Areas with probabilities less or equal to 0.05 (uncorrected) are shown in italics. Values are means { SD.
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Auditory
Medial geniculate Nucleus
Dorsal division (MGD)
Medial division (MGM)
Ventral division (MGV)
Primary Auditory Cortex (AC)
Inferior colliculus
Central (ICC)
External (ICE)
Ventral nucleus of the lateral lemniscus (LL)
Lateral superior olivary nucleus (LSO)
Medial superior olivary nucleus (MSO)
Dorsal cochlear nucleus (DCN)
Ventral cochlear nucleus (VCN)
Extraauditory
Medial frontal cortex (MFC)
Lateral frontal cortex (LFC)
Sulcal frontal cortex (SFC)
Accumbens (ACB)
Medial septum (MS)
Lateral septum (LS)
Vertical diagonal band (VDB)
Lateral preoptic area/horizontal diagonal
band (LPO/HDB)
Parietal cortex 1 (Par1)
Anterior granular insular cortex (AI)
Anterior reticular n. (ARN)
Anteroventral n. (AVN)
Paratenial n. (PT)
Centrolateral/centromedial n. (CM)
Lateral habenula (LHAB)
Ventromedial n. (VM)
Medial dorsal n. (MD)
Zona incerta (ZI)
Subthalamic n. (STh)
Lateral geniculate n.
Dorsal (LGNd)
Ventral (LGNv)
Lateral posterior n. (LPN)
Anterior pretectal n. (APT)
Superior colliculus
Superficial layer (SGS)
Deep layer (SGM)
Midbrain reticular formation (MRF)
Red n. (Red N)
Ventral tegmental area (VTA)
Cingulate cortex (ACG)
Parietal Cortex 2 (Par2)
Subiculum (SUB)
Presubiculum (PrS)
Retrosplenial cortex (RS)
Occipital cortex 2-medial (OC2m)
Occipital cortex 1 (OC1)
Occipital cortex 2-Lateral (OC21)
Perirhinal cortex (PRH)
Entorhinal cortex (ENTO)
Cerebellar vermis (VERM)
Cerebellar hemisphere (CBLM)
Deep cerebellar nuclei (DCBL N.)
Vestibular n. (VEST n.)
Flocculus (FLOCC)
Medial portion hippocampal field CA1 (MCA1)
Hippocampal field CA1
Hippocampal field CA2
Hippocampal field CA3
Dentate gyrus (DG)
Caudal caudate-putamen (cCPU)
Lateral hypothalamus (LH)
Ventromedial hypothalamus (VMH)
Basolateral amygdala (BL)
TL0
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al. 1993). PLS recently has been adapted for neuroimaging analysis
(McIntosh et al. 1996); here we describe modifications of the
method to address issues of functional connectivity (McIntosh et
al. 1997). The PLS procedure used here is understood most easily
as an extension of a univariate interregional correlation analysis
used by Horwitz et al. (1993) and has strong similiarities to the
cross-hemispheric functional connectivity analysis described by
Friston (1994). The specific purpose of the present analysis was
to identify dominant patterns of auditory-extraauditory functional
connections and determine which of these patterns were different
between the two groups.
A highly idealized graphic description of the PLS procedure
used to analyze interregional correlation changes is presented in
Fig. 1 (a mathematical description and a small numerical example
are presented in the appendix). In Fig. 1A, activity from a single
ROI from the auditory system is correlated with the activity from
the rest of the ROI data set (referred to here as extraauditory ROIs
for simplicity) within two groups (Fig. 1A, left). This produces a
correlational map of extraauditory ROIs that are correlated with
the auditory ROI for each group (Fig. 1A, right). The correlation
procedure usually stops here, and the maps are compared between
tasks to ascertain any experimental changes in the patterns.
The PLS extends the correlation analysis by combining correlation maps into a single matrix referred to as the cross-block covariance (correlation) matrix, where the blocks are the auditory and
extraauditory ROIs. This matrix is analyzed with singular value
decomposition (SVD) providing sets of mutually orthogonal latent
variable (LV) pairs. Each LV extracted through the SVD accounts
for progressively less of the summed squared cross-block covariance (SSCC), a rough index of importance. A single value is
calculated for each LV, which is the covariance between the auditory and extraauditory LVs in the pair, and indexes the proportion
of SSCC accounted for.
The LVs can represent common correlation patterns or correlation patterns that show group differences. The first element of the
LV pair contains the numerical weights for the auditory ROI, the
auditory saliences, from each group and the pattern of weights
indicates whether the LV represents a commonality or difference
of correlations between groups. The second element of the LV pair
contains the weights for the extraauditory ROIs, the extraauditory
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saliences, and their variation across the brain shows the pattern of
ROIs that show either the commonalities or differences in their
correlations with the auditory ROIs salient on the LV. For example,
the first LV in Fig. 1B represents the commonalities between
groups because the auditory salience is the same between groups.
The auditory salience on the second LV depicts a difference between the two groups with the weight being positive for one group
and negative for the other, and the pattern of extraauditory saliences
locates the differences in auditory-extraauditory correlations between groups.
To summarize, each LV contains two components: saliences for
the extraauditory ROIs and saliences for the auditory ROI indicating how the groups relate to the extraauditory pattern of saliences—i.e., is it a common pattern or a group difference? The
PLS analysis also can be extended to include more than one auditory ROI, as in the present application.
In the present application, the 11 auditory ROIs were correlated
with 52 extraauditory ROIs within each of the two groups. For a
given LV, auditory saliences that were similar for both groups
would indicate a common pattern of functional connectivity with
the extraauditory regions salient on that LV. Conversely, if saliences across auditory regions differed between groups, this would
indicate a pattern of functional connectivity with extraauditory
regions that distinguished groups.
Statistical evaluation
UNIVARIATE TESTS. Regional FDG uptake was evaluated for
group differences using the regression approach to the analysis of
variance (Pedhazur 1992). The probability values for the regression analysis are based on the evaluation of the squared correlation
of this regression and are derived from a permutation test of group
assignment (Edgington 1980).
MULTIVARIATE TESTS. With 11 auditory regions for two groups,
there are 22 LVs that are computed through SVD. Obviously, not
all LVs that are extracted through SVD are meaningful, either
statistically or theoretically. In other applications of PLS to imaging
data, permutation tests have been used to assign a measure of
statistical significance to the LV structure (Cabeza et al. 1997;
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FIG . 1. Graphic representation of the
partial least-squares analysis used to assess
functional connectivity patterns. A: region
of interest (ROI) from the auditory system
(auditory ROI) is correlated with several
extraauditory ROIs (displayed on a midsagittal rat brain schematic) within 2 groups, resulting in 1 correlation map per group. j,
strong negative correlation; h, strong positive
correlation. Correlations of the auditory ROI
with a medial prefrontal region are similar
between groups, whereas extraauditory ROIs
at posterior neocortex and pons are different.
B: correlation maps are stacked into 1 large
cross-block covariance matrix and decomposed with singular value decomposition resulting in 2 latent variable (LV) pairs. Each
pair consists of the weights or saliences for
the auditory ROIs for each group and for
extraauditory ROIs. Auditory saliences on
LV1 are the same for both groups, suggesting
a common pattern of correlations, or functional connections, with the salient extraauditory region—medial prefrontal cortex. For
LV2, the auditory saliences are different between groups, indicating that the salient extraauditory regions, posterior neocortex and
pons, show different correlations with the auditory region between groups.
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
TABLE 3. Singular values, percentage, and cumulative
percentage of the summed squared cross-block covariance
Latent
Variable
Singular
Value
Percentage
Cumulative
Percentage
1
2
3
4
5
6
7
8
9
8.6164
6.3894
5.3273
3.4542
3.2261
2.2944
2.0009
1.7017
1.3507
0.4052
0.2228
0.1549
0.0651
0.0568
0.0287
0.0218
0.0158
0.01
0.4052
0.6279
0.7828
0.8479
0.9047
0.9335
0.9553
0.9711
0.9811
Nine latent variables were statistically significant. Only the first three,
shown above were retained for further examination.
signed-ranks test). In group TL 0 , this was not the case. The
suppression ratios for the T, TL, and TC compound did not
differ from each other (T, M Å 0.06 { 0.05; TL, M Å
0.08 { 0.05; TC, M Å 0.00 { 0.00). Latency measures
showed a ceiling effect for all stimuli in group TL 0 and were
therefore equal.
Regional means
Mean ratio-adjusted values, along with SDs, from the
63 regions for both groups are presented in Table 1. When
evaluated statistically at the conventional level of P õ
0.05, differences in the auditory system FDG incorporation were observed at the dorsal and ventral divisions of
the medial geniculate only, although probability for the
dorsal division was exactly 0.05 evaluated over 5,000 permutations. Both areas showed lower means in group TL 0
compared with group TL 0 . Extraauditory regions showed
higher mean values for group TL 0 in the medial frontal
cortex, nucleus accumbens, and ventromedial hypothalamus and reduced mean FDG incorporation for the anterior
pretectal nucleus. If the criterion for significance was to
be adjusted for the number of independent comparisons
( in this case 63 ) , none of these regional differences would
remain significant.
Partial least-squares analysis
RESULTS
Behavior
Suppression ratios and latency measurements taken during
the probe session after presentation of each of the stimuli
demonstrated that the light had acquired conditioned inhibitory properties for group TL 0 but not for group TL 0 . In
group TL 0 , the mean suppression ratio ( {SE) for the T
alone was 0.03 { 0.02 and for the TL compound was 0.35 {
0.03. The difference between these was significant [t(6) Å
5.31, P õ 0.01]. Further, in group TL 0 the mean suppression
ratio for the compound of the two tones (TC) was the same
as for the tone alone (M Å 0.03 { 0.03), further suggesting
that the light was acting as a conditioned inhibitor. The
latency to drink, for group TL 0 , after presentation of T was
significantly greater compared with the latency after the TL
compound (log-latency, T, M Å 1.74 { 0.15; TL, M Å
1.26 { 0.15; z Å 2.52, P õ 0.01; Wilcoxon matched-pairs
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Nine of the 22 LVs were significant by permutation
tests at P õ 0.05, and Table 3 lists the singular values
for the 9 LVs with percentage and cumulative percentage
SSCC accounted for. Three LVs were dominant from the
PLS analysis and were considered for interpretation. Together these three accounted for 79% of the SSCC. Although the subsequent LVs were significant, only one or
two auditory regions were salient within a group, and there
was little obvious regionalization for the extraauditory
LVs beyond the third. The functional connectivity patterns
first will be described as they pertain to overall statistical
reliability and spatial distribution, then in terms of auditory system involvement that was most closely linked to
group differences.
LV1 ( SSCC Å 41% ) . This latent variable reflected a similar
pattern of auditory-extraauditory connectivity in both
groups, with a somewhat stronger pattern in group TL 0 .
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McIntosh et al. 1996; Nyberg et al. 1996). There are two statistical
questions that can be answered about the present data set using
permutation tests. The first is whether the patterns represented on
the LVs are on the whole significant. The second, assuming the
pattern is significant, is whether there are group differences on the
pattern.
The first question was addressed by evaluating the singular values from the SVD because they are the covariances of the latent
variables. The computed probability represented the number of
times, out of 5,000 random permutations of the auditory-extraauditory pairings across subjects, a singular value greater than or equal
to the original singular value for each LV was obtained. This
permutation also served to assess individual saliences, giving a
threshold to decide which regions were contributing the most of
the LV pattern. Because the saliences and singular values were
calculated in a single mathematical step, there was no need to
correct for multiple comparisons across the ROIs.
The assessment of group effects was done by evaluating the
difference of auditory saliences for the two groups on an particular LV. Auditory saliences would differ the most on LVs where
there was a strong group effect. Referring back to Fig. 1, the
group difference would be small for LV1 because there is no
group effect on the LV, but differences would be higher for
LV2. Correction for multiple comparisons was afforded by a
Bonferroni correction of the statistical threshold for comparison
of the 11 auditory regions on each LV, which yielded a threshold
of significance at P õ 0.005 ( 0.05 / 11 with rounding ) .
THEORETICAL CONSTRAINTS. Statistical tests use a mathematical
criteria to aid the investigator in trying to decide which, out of a
large number of potential findings, are the most reliable. However,
few statistical tests explicitly combine the mathematical criteria
with theoretical or biological constraints. These additional constraints require intervention from the investigator. The merits of
using theory as the final judge of statistical outcomes have been
debated in several disciplines (e.g., Freedman 1987). For PLS,
this issue is no less important. Because PLS is dealing with relations across a large number of dimensions, it is plausible that some
of the structure identified represents statistical noise and may not
be biologically relevant despite attaining statistical significance
(the reverse will also be true in some instances). Because of the
small sample size in the present study and the inherent redundancy
of the nervous system, determining whether the saliences cluster
into known anatomic or functional groupings was used to add
another constraining dimension to the interpretation of the PLS
results.
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For both groups, auditory regions from the cochlear nuclei
to the midbrain colliculus were salient, but only in group
TL 0 was the auditory thalamic areas also salient. Figure
2 presents the saliences on LV1 for all the auditory ( top )
and extraauditory regions ( bottom ) with an indication of
significance. All saliences are presented, rather than just
those significant by permutations, so an appreciation for
the full pattern of relations can be gained. For Fig. 2, and
subsequent figures, the saliences were rescaled through
multiplication by their singular value ( Streissguth et al.
1993 ) . The saliences for all auditory regions, with the
exception of cortex, were negative for both groups, indicating a negative correlation with the extraauditory pattern. Fewer auditory regions met statistical significance
in group TL 0 .
Extraauditory regions showed two distinct clustering of
saliences. The most prominent clustering comprised the
thalamocortical components of the visual system [ dorsal
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lateral geniculate nucleus ( LGNd ) , ventral lateral geniculate n. ( LGNv ) , and lateral posterior n. ( LPN ) , medial
occipital cortex 2 ( OC2M ) , occipital cortex 1, lateral occipital cortex 2 ( OC2L ) ] and anterior pretectal n. ( APT ) ,
and parts of posterior limbic cortices [ retrosplenial cortex
( RS ) , perirhinal cortex, and entorhinal cortex ( ENTO ) ] .
Only the thalamic regions and OC2L were significant by
permutation tests. The saliences for these areas were all
positive, suggesting that for both groups, these areas were
correlated negatively with the auditory system, though
somewhat stronger in group TL 0 . For example, the correlation of LGNd and ventral nucleus of the lateral lemniscus ( LL ) was 00.81 for group TL 0 and 00.71 for group
TL 0 . Another area of note showing a similar pattern of
correlations was the paratenial n. in the thalamus. Negative saliences were quite focal and restricted to the deep
cerebellar nuclei ( DCBL ) , cerebellar hemisphere, and
vestibular n. Correlations of these regions were strongest
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FIG . 2. LV1 from the analysis of functional connectivity between auditory and
extraauditory regions. Top: graph contains
the saliences for auditory regions for the 2
groups (legend: top right). Bottom: saliences for extraauditory regions. – – – ,
approximate cutoff for statistical significance of the saliences as assessed through
permutation tests (P Å 0.05). Direction
and magnitude of most auditory saliences
are similar for both groups, suggesting a
common pattern of correlations with the
pattern of extraauditory region saliences
(bottom). Region abbreviations for areas
are defined in Table 2.
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
3155
with DCN and VCN for both groups, though stronger for
group TL 0 . All of these areas showed positive correlations
with the auditory regions.
LV2 ( SSCC Å 22% ) . Saliences for the subcortical auditory
system showed opposite pattern for the two groups with only
the saliences for group TL 0 reaching statistical significance
(Fig. 3). Midbrain and brain stem components of the auditory system were salient especially DCN and external nucleus of the inferior colliculus (ICE). The dominant positive
saliences for LV2 were in the superficial layer of the superior
colliculus (SGS), deep layer of the superior colliculus
(SGM), and midbrain reticular formation (MRF). Dominant on the negative saliences were the subiculum (SUB)
and zona incerta (ZI). All of these extraauditory regions
were more strongly correlated with auditory regions in group
TL 0 , save for SUB, which showed correlations of equal
magnitude but different sign between groups.
LV3 ( SSCC ) Å 16% ) . The auditory system regions loading
on this LV were thalamocortical ( Fig. 4 ) , and only the
auditory areas from group TL 0 were significant. Positive
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saliences were noted in lateral frontal cortex and lateral
preoptic area / horizontal diagonal band, but these areas
were not reliable by permutation tests. Negative saliences
were observed in the hippocampus proper in the CA subfields, lateral hypothalamus, ventromedial hypothalamus
( VMH ) , and basolateral amygdala, all of which were significant.
In Fig. 5, the patterns of functional connectivity between the auditory and
extraauditory regions are presented on rat brain schematics.
The highlighted auditory regions in the figure are those
where the differences in saliences between groups were significant by permutation tests.
LV1 is shown in Fig. 5A where only the MGM and ICE
were different between groups. For both areas, the relations
of MGM and ICE were stronger in group TL 0 , although
there was also a change in sign for MGM. To help illustrate
this difference, the MGM was correlated with the DCBL at
0.48 for group TL 0 and 00.28 in group TL 0 ; the ICE showed
a correlation with the DCBL of 0.71 for group TL 0 and
EXPERIMENTAL EFFECT ON LV PATTERN.
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FIG . 3. LV2 from the analysis of functional connectivity between auditory and
extraauditory regions. Direction and magnitude of auditory saliences are opposite for
the groups indicative of an opposite pattern
of correlations with the pattern of extraauditory region saliences (bottom).
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A. R. MCINTOSH AND F. GONZALEZ-LIMA
00.02 for group TL 0 . (We emphasize that the PLS is optimized formally to use the entire pattern of correlations. The
univariate correlations serve only as an illustration of the
pattern that contributed to the LV and not as a formal test
statistic.)
The summary for LV2 is presented in Fig. 5 B. Although
the saliences for most auditory regions showed opposite
signs between groups, only the DCN was statistically significant after Bonferroni correction. To aid in interpretation,
in group TL 0 , the correlation of DCN with SGM was 0.55,
whereas for group TL 0 , the correlation was 00.60; the DCNSUB correlation was 00.75 for group TL 0 and 0.51 for
group TL 0 .
For LV3, shown in Fig. 5C, auditory cortex was the only
region that showed a significant difference between groups.
The extraauditory areas that were salient on this pattern
showed negative correlations with auditory cortex in group
TL 0 and weak positive correlations in group TL 0 . For exam-
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ple, the correlation of AC and VMH was 00.66 for group
TL 0 and 0.37 for group TL 0 .
DISCUSSION
Simple associative learning, such as was studied in the
present paper, requires the integration of information across
or within sensory modalities. We propose that this integration occurs at the level of large-scale interactions between
neural systems specialized for processing the particular sensory modalities. The present study was designed to test this
hypothesis and begin to describe these interacting systems.
To that end, functional connections of the rat auditory system
with several extraauditory regions were assessed in two
groups. An important point is that for both groups the physical properties of the stimuli were identical, but the behavioral
relevance differed, therefore differences in the pattern of
functional connectivity may reflect the unique behavioral
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FIG . 4. LV3 from the analysis of functional connectivity between auditory and
extraauditory regions. Auditory saliences
are strongest for group TL 0 , suggesting the
pattern of extraauditory correlations is
strongest in this group.
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
3157
As an internal validation of the use of PLS for exploration of functional connectivity, the pattern observed on LV1
is reassuring. For both groups, there was a consistent relation
between auditory and visual system regions, which would
be anticipated given that auditory and visual stimuli were
presented to both groups. Most of the auditory regions
showed similar negative correlations between auditory and
visual areas in the two groups. Because the involvement of
these regions did not depend on the experimental manipulation, it is possible that these functional connections are present whenever visual and auditory stimuli are presented together. Whether this negative relation is a reflection of an
attentional mechanism would require further experiments,
but such an inverse relation of activity between auditory and
visual systems has been observed in earlier FDG studies
using MRF stimulation (Gonzalez-Lima 1989) and in neuroimaging studies of selective attention in humans (Haxby
et al. 1994).
The involvement of auditory regions ICE and MGM in
the pattern of extraauditory connectivity differed between
groups. Both have extensive connections outside the auditory
system (Aitken et al. 1978; LeDoux et al. 1984) that allow
for extraauditory effects to be conferred to other auditory
regions through the ICE and MGM. Auditory system structural equation models for the two groups showed that both
areas had clear distinguishing interactions (McIntosh and
Gonzalez-Lima 1995). ICE showed stronger ascending and
descending influences in group TL 0 , whereas the MGM
showed a change in the sign of its interactions with AC
between the two groups. Considered in light of the present
results, the group differences in the interactions of these
areas within the auditory system may represent the detection
and transmission of the extraauditory effects that signaled
the change in the tone’s behavioral significance.
For the extraauditory pattern, the APT may have been
recruited because of the nature of the visual stimulus. The
pretectal area has been suggested to mediate brightness discrimination (Legg 1988), and the activation of this area
in classical conditioning of visual stimuli has been noted
(Gonzalez-Lima et al. 1987). Because the visual stimulus
used was a flashing diffuse light, the involvement of the
APT seems reasonable. The same logic certainly would hold
LV1.
FIG . 5. Schematic representation of the areas with the strongest contributions to the pattern of functional connectivity across the 3 latent variables.
Areas highlighted in white showed positive saliences and those in black
showed negative saliences. A: 2 auditory regions indicated by an asterisk
(ICE and MGM) showed significant group differences in the pattern of
covariance with the extraauditory regions highlighted in the schematic. B:
DCN of the auditory system showed significantly higher covariance patterns
with the highlighted extraauditory areas in group TL 0 . C: auditory cortex
(AC) showed a significantly different pattern of covariances with the highlighted extraauditory areas between groups. Although the figure highlights
particular regions, the collective contribution of all regions is emphasized
in the analysis.
history of the group. In the next section, we consider the
patterns of functional connectivity from the perspective of
interacting systems.
Patterns of functional connectivity
For the present paper, we focused on the functional relations between the auditory system and other brain regions.
Because the task involved the modification of the associative
properties of an auditory stimulus, a reasonable starting point
for the investigation would be the auditory system. Even
within this narrow focus, we have shown a rich set of interrelations that bridge across several other neural systems (e.g.,
visual, cerebellar, limbic), adding to the idea that learning
and memory operations engage several brain areas and that
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the relations between those areas are the important determinants of behavior. The three patterns of functional connectivity are orthogonal within the confines of PLS, but it is likely
that within the confines of the brain the three patterns combine and interact to affect the response of the organism to
the conditioned stimuli.
Because behavioral studies have been able to reliably distinguish between different processes in learning (e.g., Konorski 1967; Wagner and Brandon 1989), it seems reasonable that these processes should be served by different neural
systems or by changing interactions between the same neural
systems. For example, one behavioral distinction is between
the sensory or perceptual relation of the CS and US, and the
association of the CS and emotive or affective aspects of
the US (McNish et al. 1997; Wagner and Brandon 1989).
In the present paper, we interpret the patterns of functional
connectivity as they may relate to the sensory-sensory and
sensory-affective associative dimensions.
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A. R. MCINTOSH AND F. GONZALEZ-LIMA
LV2. The pattern for LV2 showed opposite functional connectivity patterns for TL 0 and TL 0 and was strongest for TL 0 .
The auditory system involvement was restricted to subcortical
structures and was particularly strong for the DCN. Extraauditory regions engaged were in the midbrain (SGS, SGM, and
MRF) and subiculum. The spatial clustering of saliences in
the extraauditory pattern implies a greater representation in
the midbrain extending to RN and VTA and subcortically to
the subiculum. Midbrain involvement also included a cluster
focused on ZI with added recruitment of subthalamic n. and
lateral habenula. The addition of the superior colliculus to this
pattern is interesting on two fronts. First, the SGS is a primary
retinorecipient site in the rat and sends projection to the SGM.
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Visual, somatic, and auditory maps have been found in SGM,
which receives auditory inputs from ICE (Huffman and Henson 1990). Both colliculi are connected with the MRF, which
was also salient on LV2, so part of the functional connectivity
may reflect the interactions between these areas that depend
on the defensive CR evoked by the CS in group TL 0 . These
auditory-extraauditory interrelations would be expected to differ for group TL 0 because the presence of the light inhibited
the defensive CR.
The involvement of ZI in the pattern for LV2 could be
related to two dimensions of the experiment: drinking behavior and orienting movements. Several investigators using
pharmacological, anatomic and behavioral studies have
linked ZI to drinking behavior (e.g., Tonelli and Chiaraviglio
1995). FDG investigations into drinking behavior have
shown greater FDG uptake in ZI in rats that consumed water
relative to a satiated control group (Gonzalez-Lima et al.
1993). In the present study, rats were trained to drink in the
operant chamber and suppression of drinking behavior was
an index of conditioning. The difficulty with an interpretation of ZI involvement reflecting drinking behavior is that
rats were not drinking during the FDG uptake period. The
anatomic relation of ZI to the superior colliculus and somatosensory cortex leads to another possible interpretation emphasizing orienting behavior (Nicolelis et al. 1995). The
opposite sign for the saliences of SGM and ZI in the LV2
pattern is interesting because the connections from ZI to the
SGM are predominantly through GABAergic cells, implying
a postsynaptic inhibitory influence (Kim et al. 1992). The
midbrain regions identified on LV2 also have been proposed
to comprise part of a mesencephalic motor system, which
contributes to the locomotor component of adaptive behaviors resulting from limbic forebrain (possibly the subiculum)
operations (Mogenson and Yang 1991; Mogenson et al.
1985). For group TL 0 rats, stronger functional connections
of ZI, SGM, and subiculum with auditory structures may
be related to the conditioned suppression of ongoing motor
behavior, given the auditory signal from ICE. For group
TL 0 , these functional connections were weak.
The reason for the recruitment of subicular cortex to this
pattern, without other related limbic structures, may be anticipated from the unique involvement of this region in the limbicmotor integration proposed by Mogenson (1987). The subiculum had auditory system correlations of equal magnitude in
both groups, but the sign of the correlations differed. We have
observed this opposing pattern of correlations in other limbic
regions when a conditioned stimulus had opposite associative
meanings (McIntosh and Gonzalez-Lima 1994a). Of further
interest, given the pattern of limbic connectivity in LV3 discussed below, is that chemical lesion work has noted significant differences in the behavioral deficits elicited by subicular
versus hippocampal damage (Jarrard 1983). Taken together,
these observations add to increasing data that demonstrate the
potential for different parts of the hippocampal formation to be
related to somewhat different components of learned behaviors
(Rosen et al. 1992).
The final LV pattern of functional connectivity fits
into several notions of the neural basis of auditory fear conditioning. Dominant on the auditory side were the thalamocortical components of the system. The three divisions of the
LV3.
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for the LGN and probably the occipital cortex regions. Although they did not exceed the significance threshold, the
cortical pattern of extraauditory saliences encompassed all
parts of the rat visual cortex and extended into limbic cortices (RS and ENTO). The involvement of these cortical areas
is consistent with other behavioral studies (e.g., Gabriel
1990; McIntosh and Gonzalez-Lima 1994a) and has strong
anatomic foundation (Paperna and Malach 1991), but the
present findings should be regarded as providing only tentative support given the statistical outcome.
The involvement of the DCBL nuclei in the pattern of
functional connectivity is consistent with the hypothesis that
this area is involved intimately in associative behaviors (Lavond et al. 1993). We have suggested previously that the
DCBL may mediate the interactions between the auditory
brain stem, particularly the DCN and the other forebrain
regions (McIntosh and Gonzalez-Lima 1994a). DCN and
DCBL were connected functionally in both groups, but there
was no group difference in this association. Instead, differences were observed more rostrally in the interactions between DCBL and the ICE and MGM. The ICE has wellcharacterized projections to the cerebellum (Huffman and
Henson 1990), and there are connections between DCBL
and the medial geniculate nucleus (Carpenter 1960). The
interrelations of the ICE, MGM, and DCBL may reflect the
modification of the tone CS in the presence of the light in
group TL 0 . This is particularly noteworthy because all three
regions receive multimodal sensory information and have the
potential for cross-modal integration. The weaker functional
connections between these areas in group TL 0 may be expected because the presence of the light had no consistent
bearing on the tone CS.
In summary, LV1 appears mainly to reflect a common
neural system for auditory conditioning that involves DCN,
DCBL, and the midline thalamus and is similar to one we
presented previously (McIntosh and Gonzalez-Lima 1994a).
The commonality between groups may reflect the same
learned CS-US sensory association (T/ ), independent of
the differential conditioned responding. Recruitment of additional areas (APT, OC2L, OC2M, LGNd, LGNv, LPN)
likely reflects the presence of the visual stimulus. However,
the involvement of ICE and MGM was stronger for group
TL 0 compared with group TL 0 . Given the unique extraauditory anatomic connections of these areas, we interpret this
aspect of LV1 as representing the change of the associative
value of the tone by the light, mediated through these extraauditory influences.
FUNCTIONAL CONNECTIVITY IN AUDITORY LEARNING
Conclusion
Based on neuroanatomy, an hypothesis was put forth in
the introduction proposing that the extraauditory regions
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having the anatomic capacity to integrate information from
visual, auditory, and somatosensory modalities should show
strong patterns of interactions. While this may seem obvious
at some level, the examination of interregional interrelations
confirmed this hypothesis and extended it by identifying
three strong patterns of large-scale functional connectivity
that were related to different dimensions of the learned associative behavior. LV1 reflects the common sensory associations between the two group, with the added difference in
the auditory-visual association for group TL 0 mediated by
auditory regions with extensive extraauditory connections.
LV2 represents the interactions among midbrain regions supporting the defensive CR and was most salient in group TL 0 .
The final pattern we considered, LV3, was interpreted as the
identifying neural interactions supporting the change in the
associative and affective value of the tone CS in the presence
of the light-conditioned inhibitor. Together, these data support the contention that learning results from the interactions
among different brain regions depending on the stimuli, the
process, and behavioral response.
APPENDIX
Mathematical description of partial least-squares analysis
For the PLS analysis of functional connectivity, the data from
k groups were each partitioned into an Nk 1 A auditory (Ma ) and
a Nk 1 B extraauditory (Me ) matrices, where Nk is the number of
observations within group k, and A and B are the number of auditory and extraauditory regions, respectively. Each data matrix is zscore transformed so that the operation
M aT ∗ Me / (Nk 0 1)
yields a matrix Y K that is an A 1 B nonsymmetric correlation
matrix of each auditory region with each extraauditory region (superscript T represents a matrix transpose).
The correlation matrices from each group Y 1 and Y 2 then are
stacked into a single matrix Y, having C Å 2(A) rows and B
columns. Y then is subjected to a singular value decomposition
(SVD)
[USV ] Å SVD[Y T ]
where
U ∗ S ∗ VT Å [Y T ]
From the decomposition, U is an C 1 B matrix containing the
extraauditory saliences, V is an C 1 C matrix of auditory saliences,
and S is a diagonal matrix of the C nonzero singular values. The
first A rows of matrix U are the auditory saliences for group 1 and
the next A rows are the saliences for group 2.
Worked example
Consider a simple correlation matrix Y
E
F
Group 1
0.90
00.40
Group 2
0.80
0.40
where the row for group 1 contains the correlation of an ‘‘auditory’’ ROI with two ‘‘extraauditory’’ ROIs (E and F) for that
group, and the row for group 2 contains the correlation vector for
group 2. The correlation of E with the auditory ROI is roughly
equal between the two groups while the correlation of F differs in
sign.
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medial geniculate nucleus (MGD, MGV, and MGM) were
salient and somewhat stronger in group TL 0 , and auditory
cortex showed oppositely signed saliences between groups.
Several investigators have suggested that the medial geniculate is a central region for auditory fear conditioning, especially given the anatomic relations with key extraauditory
regions (LeDoux et al. 1984). Although some of the work
has emphasized the MGM, other investigators have also
noted functional plasticity in the MGV and MGD (Edeline
and Weinberger 1991).
Functional plasticity in auditory cortex also has been well
documented. Field-potential and single-cell electrical recordings in rodent auditory cortex have noted reliable learning-related changes in receptive fields (Bakin and Weinberger 1990; Weinberger and Diamond 1987). Other work has
extended this to suggest that auditory cortex shows learningrelated responses across different memory tasks (Sakuri
1994). While some lesion studies have suggested the auditory cortex and thalamus may have an equal role in simple
excitatory conditioning (Romanski and LeDoux 1992), the
present result of opposite relations of the AC with extraauditory structures between groups suggest a more prominent
role for the cortex when the relevance of the auditory signal
is modified by a signal from another sensory modality. Stated
more generally, auditory cortex functional interactions may
be particularly strong when the relevance of an auditory
signal is contextually dependent, such as in discrimination
learning or compound stimulus conditioning.
Extraauditory saliences were strongest for the hypothalamus, the basolateral amygdala, and the hippocampus. There
is a long history implicating the relation of the hypothalamus
in motivation (Olds 1962), and lateral hypothalamus activity
seems to be most related to positively reinforced behaviors
(Kozhedub et al. 1997; Nakamura and Ono 1986). Lesions
of the amygdala disrupt the expression of conditioned fear
(LeDoux 1993), and electrical recordings show changes in
firing patterns that follow the acquisition of conditioned fear
responses (Maren et al. 1991). The hippocampus has shown
electrophysiological activity—and, interestingly, interactivity—related to the acquisition of associative behavior
(Deadwyler et al. 1996). Particularly important is the speculation that the hippocampus is involved partly in the identification of contiguity between spatial and temporal events
(Larochem et al. 1987). In this light, we interpret the
stronger functional connections between the thalamocortical
auditory system and hippocampus for group TL 0 to reflect
detection of the change of the associative value of the tone
in the presence of the light. Such connections would not be
there in group TL 0 because the associative value of the tone
did not differ in the presence of the light. At the same time,
the affective value of the tone also would have differed in
group TL 0 , which may be carried through the functional
interactions with the hypothalamus and amygdala. The
change in affective value for the tone would not be expected
in group TL 0 . The third LV may therefore represent the
inhibition of the affective value of the tone CS when presented in compound with the light conditioned inhibitor.
3159
3160
A. R. MCINTOSH AND F. GONZALEZ-LIMA
An SVD performed on matrix Y yields
Auditory saliences (matrix U from previous section)
LV1
LV2
Group 1
0.76
0.65
Group 2
0.65
00.76
LV2
E
0.99
00.04
F
00.04
00.99
E
F
Sum (Y1 / Y2)
1.70
0
Diff (Y1 0 Y2)
0.10
00.80
Decomposition of this matrix yields
Auditory saliences
LV2
Sum
0.99
00.08
Diff
0.08
0.99
Extraauditory saliences (matrix V)
LV1
LV2
E
0.99
00.04
F
00.04
00.99
The singular values for LV1 and LV2 are 1.700 and 0.798, respectively.
Note that the extraauditory saliences are identical to those from
the analysis of the original correlation vectors. The interpretation
of the LV structure would be identical for both analyses. With a
small number of ROIs, one could construct the matrix Y to contain
the sum (grand mean) and the difference (deviation) of correlation
vectors, but this becomes rather cumbersome when several ROIs
are used. The invariance of the SVD solution to orthogonal rotation
obviates such preprocessing steps.
The authors thank Dr. N. J. Lobaugh and two anonymous referees for
valuable comments in the development of this paper.
This work was supported by National Institute of Mental Health Grant
MH-43353 and National Science Foundation Grant IBN9222075 to F. Gonzalez-Lima and partly by Natural Sciences and Engineering Council of
Canada Grant OGP017034 and Medical Research Council of Canada Grant
MT-13623 to A. R. McIntosh.
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The singular values for the two latent variables are 1.20 and 0.56.
Because the auditory saliences on LV1 are similar between
groups, the LV represents the similarities of correlations between
auditory and extraauditory ROIs, and LV2 represents the differences between groups, in this case a difference in sign. The extraauditory saliences identify which extraauditory areas show these relations. Region E is most salient on LV1, the similarities of correlations, and region F is salient on LV2, the difference of correlations.
The PLS analysis is equivalent to a fully parameterized multivariate linear model containing terms for the grand mean and terms
for the deviations from that mean. In a previous paper (McIntosh
et al. 1996), it was stated that the analysis of correlation patterns
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To illustrate, matrix Y was recomputed as the sum of groups 1
and 2 and difference of groups 1 and 2.
LV1
Received 30 March 1998; accepted in final form 31 August 1998.
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