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Temporally truncate BOLD data

Temporally truncate BOLD data


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I'm analyzing some resting-state data and some of the scans are 10 min while others are 7 min. I need to cut the last 3 min off of those with 10 min scans so everyone has 7 min of resting-state data. I realize I could just delete timeseries columns after processing but I'd like to perform the truncation earlier so that my temporal filtering isn't biased. I believe there's an FSL tool to accomplish this but can't find it. Any suggestions?


It looks likefslroiis the tool I'm looking for. To temporally truncate a scan the usage would befslroi wherewould be zero andwould be the number of volumes you want to keep. Now I just need to figure out how many volumes make up 7 minutes…


Effective functional mapping of fMRI data with support-vector machines

There is a growing interest in using support vector machines (SVMs) to classify and analyze fMRI signals, leading to a wide variety of applications ranging from brain state decoding to functional mapping of spatially and temporally distributed brain activations. Studies so far have generated functional maps using the vector of weight values generated by the SVM classification process, or alternatively by mapping the correlation coefficient between the fMRI signal at each voxel and the brain state determined by the SVM. However, these approaches are limited as they do not incorporate both the information involved in the SVM prediction of a brain state, namely, the BOLD activation at voxels and the degree of involvement of different voxels as indicated by their weight values. An important implication of the above point is that two different datasets of BOLD signals, presumably obtained from two different experiments, can potentially produce two identical hyperplanes irrespective of their differences in data distribution. Yet, the two sets of signal inputs could correspond to different functional maps. With this consideration, we propose a new method called Effect Mapping that is generated as a product of the weight vector and a newly computed vector of mutual information between BOLD activations at each voxel and the SVM output. By applying this method on neuroimaging data of overt motor execution in nine healthy volunteers, we demonstrate higher decoding accuracy indicating the greater efficacy of this method.

Figures

Illustrations of characteristics of an…

Illustrations of characteristics of an SVM. ( A ) Two different datasets (red…

Illustration of mutual information in…

Illustration of mutual information in consistent (i.e., most of x i s are…

Functional maps from a group…

Functional maps from a group analysis of non‐smoothed data. In functional maps (F(SW)‐,…

Ratio of area of clusters…

Ratio of area of clusters remaining after applying the second‐level threshold to total…


Author information

Arielle Tambini and Ulrike Rimmele: These authors equally contributed to this work.

Affiliations

Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA

Department of Basic Neurosciences, University of Geneva Campus Biotech, Geneva, Switzerland

Department of Psychology, New York University, New York, New York, USA

Elizabeth A Phelps & Lila Davachi

Center for Neural Science, New York University, New York, New York, USA

Elizabeth A Phelps & Lila Davachi

Nathan Kline Institute, Orangeburg, New York, USA

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You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

Contributions

A.T., U.R., E.A.P. and L.D. designed the experiment and wrote the paper. A.T. and U.R. collected and analyzed the data.

Corresponding author


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Introduction

When faced with a difficult choice, people rarely make decisions in a social vacuum but use the information provided by the actions of other people who made the same choice previously. Consider the example of choosing between two restaurants to dine at in a foreign city. Your travel guide recommends both, but when you go to check them out, one is very busy and the other is empty. The previous actions of the other diners might bias you towards choosing the busier restaurant. Social information is fundamental and can combine with, or even substitute for, individually acquired information about options to guide our decisions.

The informational value of others’ actions can be high, as when rats observe conspecifics die after consuming a particular food. However, once a herd has formed it may become difficult to glean any information from the actions of others. In the restaurant example above, you might be unsure as to whether the other diners actually know something you do not, or just followed the first few people to go in. It is in this manner that a sound choice on the individual level can give rise to inefficient or detrimental behavior on the aggregate level (Camerer and Weigelt, 1991 Banerjee, 1992 Bikhchandani et al., 1992 Anderson and Holt, 1997 ). Indeed, herd behavior may contribute to bubbles in financial and housing markets (Baddeley 2005 Dale et al., 2005 ), online consumer preferences (Hansen and Putler, 1996 ) and conforming to a group in perceptual decision tasks, despite clear sensory evidence against it (Bond and Smith, 1996 ). The critical feature of economic and psychological accounts of herding is that observing the actions of others, and not the outcomes of those actions, exerts a measurable difference on the individual’s choice.

If the behavior of others would change the value of options, one would expect to detect such an effect in value coding regions of the brain. A core region of the value system is the ventral striatum. It processes economic value parameters such as the mean and the variance of choice options (Preuschoff et al., 2006 ). Here we tested the hypothesis that it would also be sensitive to the actions of others in the context of an economic decision making (stock buying) task. To do this, we first asked whether the ventral striatum is sensitive to the actions of others (social information) in the absence of economic information. Secondly, we asked if the same regions were sensitive to economic information (the mean and variance of various stock return data) in the absence of social information. Thirdly, if the ventral striatum was sensitive to both social and stock information, we investigated how this combined information is represented in the brain. For example, do the actions of others change the processing of objective stock value or variance or do they modulate subjective preferences for different options? Methodologically, we were in a position to answer these three questions by having trial types with only social, only stock, or with both kinds of information. For the fist two questions, we hypothesized that participants would make more 𠇋uy” decisions as more of the herd bought, even in the absence of any stock information. In addition to this, there would be an increase in ventral striatum activity inline with behavior. This reflection of behavior in ventral striatum activity would also be observed in response to stock information when social information was absent. For the third question, we hypothesized that social information would modulate the processing of stock parameters directly. For example, activity in the ventral striatum relating to a stock value signal would be increased if participants subsequently became aware that others had bought that particular stock. In agreement with our first and second hypotheses, we found that activity in the ventral striatum tracks social information (actions of others), and economic information (stock parameters). However, in contrast to the third hypothesis, social information influenced ventral striatum activity through participants’ subjective preferences for different stock types rather than by modulating the objective stock properties.


Results

Behavioral Results

Our attempts to match memory performance across iEEG (patient) and fMRI (control) groups (by varying the number of sources, retention interval and encoding list length see methods) were successful ( Table 1 ). A “Pr” measure (probability of correct minus probability of incorrect “old” decisions to studied items) showed that overall recognition memory was significantly above chance (0), (fMRI: Pr = 70% (+/− 4), t19 = 16.18, P < .001 iEEG: Pr = 62% (+/− 9), t4 = 6.56, P = .003). Pr for source recognition (probability of correct minus incorrect source decisions to studied items excluding 𠇍on’t know” responses) was also significantly above chance (fMRI: Pr = 74% (+/− 6), t19 = 13.46, P < .001 iEEG: Pr = 52% (+/− 8), t4 = 6.15, P = .004). No significant differences were observed in item- or source Pr measures across fMRI vs. iEEG groups (both t23 < 1.63, P > .11). For further analyses, trials in which an item was recognized (“hit”), but either a 𠇍on’t know” response or an incorrect source response was given, were collapsed, and are referred to as “item recognition” (IR). Mean reaction times (RTs) for correct rejection (CR), item recognition (IR) and source recognition (SR) are shown in Table 1 . All pairwise comparisons were significant (fMRI: all t19 > 3.47, P < .01 iEEG: all t4 > 3.93, P < .05).

Table 1

Behavioral results for fMRI and iEEG version of the experiment. Memory performance is expressed in percentage of old or of new items. Standard errors are shown in parentheses. CR = correct rejection, IR = item recognition, SR = source recognition.

studied
items
unstudied itemssource memory out of hitsreaction times (sec)
hit missCR IRSRCRIRSR
correct
rejectio
n
false
alarm
“?”
respons
e
source
incorrec
t
source
correct
fMR
I
87
(3)
13 (3)82 (3)18 (3)35 (3)9 (2)56 (4)1.72
(.08)
2.27
(.06)
1.90
(.07)
iEE
G
82
(6)
18 (6)80 (10)20 (10)42 (11)14 (4)43 (8)1.65
(.19)
2.10
(.17)
1.86
(.20)

Imaging Results

For all imaging analyses, we defined two memory effects of interest: (i) an “item effect”, i.e., the difference between CR and IR, which emphasizes processes related to simple item recognition or novelty detection, while reducing the impact of target source retrieval (ii) a “source effect”, i.e., the difference between SR and IR, which emphasizes processes related to retrieval of target source details, while reducing the impact of item recognition/novelty.

For our fMRI data, we first queried item- and source memory effects via a conventional analysis of the blood-oxygenation-level-dependent (BOLD) response based on an assumed haemodynamic response function (HRF). We extracted the mean parameter estimates across voxels within each region of interest (ROI), where ROIs were defined anatomically and separately for each participant, based on their structural MRI ( Fig. 2 ). No hemispheric differences were seen in this or any subsequent analysis (see supplementary material), so the data reported here are averaged across left and right ROIs. We found that both hippocampus and perirhinal cortex exhibited a significant item effect (both t19 > 2.41, P < .05) as well as a significant source effect (both t19 > 2.18, P < .05), resulting in a “U-shaped” pattern across CR-IR-SR.

fMRI results for hippocampus (a) and perirhinal cortex (b). Left: Results from modeling conditions in a conventional analysis using an assumed HRF. Bar graphs represent mean (+ s.e.m.) parameter estimates. Note that, though differing with respect to baseline, item effects (CR vs. IR) and source effects (SR vs. IR) are indistinguishable within or across regions. Middle: Average (+/− s.e.m.) fMRI BOLD time courses versus baseline for the three conditions of interest. Right: Statistical development of the item effect (differential evoked response for CR vs. IR) and source effect (SR vs. IR), showing t-values for each effect across time. Points above the dashed line correspond to P < .05, two-tailed. Note the temporal dissociation of item- and source effects within and across regions.

Next we assessed whether item- and source effects might show different temporal BOLD profiles. As would be expected based on the above results, the integrated BOLD signal (averaged from 3-9 sec post stimulus onset) in both hippocampus and perirhinal cortex revealed a significant item effect (both t19 > 2.91, P < .01) as well as a significant source effect (both t19 > 3.27, P < .005). More critically, however, our data showed that these effects have different temporal characteristics within and across regions ( Fig. 2 ). In the hippocampus, the item effect was delayed relative to the source effect, whereas in the perirhinal cortex, there was an early and transient item effect, together with a more sustained source effect. These temporal dissociations within regions were confirmed by a repeated-measures ANOVA with the factors Effect (item, source) and Time (TR1-4), which revealed significant Effect x Time interactions in both regions (both F3,57 > 4.61, P < .01). Moreover, the BOLD data suggested that the sequence of item- and source effects differs across the two regions, as evidenced by a significant Region x Effect x Time interaction (F3,57 = 3.83, P = .01). Comparing the latencies of each participant’s effect peak (using a non-parametric Wilcoxon test), we observed that the perirhinal cortex item effect peaked significantly earlier than the hippocampal source effect (P = .02), whereas the perirhinal cortex source effect peaked significantly earlier than the hippocampal item effect (P < .005). Nonetheless, inferring the latency of neural activity from the temporal characteristics of the BOLD response is difficult (given potential nonlinear neural-vascular mappings 22 ). Thus, to better explore the temporal profiles of memory signals in the hippocampus and perirhinal cortex, we capitalized on the real-time resolution of the iEEG data.

In a first step, we defined two time windows of interest: An early window ranging from 250-750ms post stimulus onset that encompassed the initial peak responses in both hippocampus and perirhinal cortex when collapsing across conditions (Fig. S1a), and a late window from 800-2000ms post stimulus onset that captured a sustained, second component in both regions (Fig. S1b). (As in the fMRI data, no hemispheric differences were seen supplementary material.)

In the hippocampus ( Fig. 3a ), the early analysis window (250-750ms) showed a significant source effect (t4 = 4.88, P = .008), but no evidence of an item effect (t4 = 0.05, P = .960). In the late analysis window (800-2000ms), the item effect was now significant (t4 = 3.17, P = .034), whereas the source effect no longer reached significance (t4 = 1.82, P = .142). The late onset of the item effect suggests that this hippocampal response might reflect post-retrieval processes for new items, rather than the fast identification of old items that is expected by the behavioral evidence for rapid familiarity-based recognition. To assess more directly whether the hippocampal item effect reflects post-retrieval processes, we compared item- and source effects in response-locked (instead of stimulus-locked) ERPs. More specifically, we compared a 500ms time window from �ms to �ms with that from +250ms to +750ms, defined relative to the participants’ key-press on each trial. Results revealed an interaction between Time Window (pre, post) and Effect (item, source) (F1,4 = 11.07, P = .029), due to the pre-response window showing a source effect (t4 = 3.49, P = .025), but no item effect (t4 = 1.09, P = .339), and the post-response window showing an item effect (t4 = 3.33, P = .029), but no source effect (t4 = 0.95, P = .398). This result suggests that the hippocampal source effect precedes the memory judgment, whereas the item effect only occurs after the memory judgment, and thus likely reflects processes such as incidental encoding of experimentally novel information (see Discussion).

iEEG results for hippocampus (a) and perirhinal cortex (b). Left: Stimulus-locked ERPs. Right: Response-locked ERPs. Shaded areas show the two time windows used for statistical analysis.

In the perirhinal cortex ( Fig. 3b ), the early time window (250-750ms) showed both an item effect (t4 = 5.82, P = .004) and a source effect (t4 = 3.20, P = .033). In the late time window (800-2000ms), the source effect was still significant (t4 = 5.30, P = .006), whereas the item effect was not (t4 = 0.34, P = .752). For the response-locked analysis, there was - unlike in the hippocampus - no differential size of source- vs. item effects with respect to pre- vs. post-response time windows (F1,4 = 0.12, P = .743) only a main effect of Time Window (F1,4 = 12.12, P = .025), reflecting the fact that the combined item- and source effects were stronger in the pre- than in the post-response time window. However, only the source effect reached significance in the pre-response time window (t4 = 7.46, P = .002), whereas the item effect did not (t4 = 2.24, P = .088), suggesting that the source effect was sustained and terminated with the memory response compared to a rapid and transient item effect.

To further investigate the latency of these effects, we calculated the earliest timepoint to show a reliable item and source effect within each region. The first effect was an item effect in perirhinal cortex at 200ms post-stimulus onset. Next, we observed a source effect in the hippocampus at 250ms, followed by a source effect in perirhinal cortex at 400ms. A hippocampal item effect was first observed at 1050ms ( Fig. 4 ).

Temporal sequence of iEEG item- and source effects in perirhinal cortex (bottom) and hippocampus (top). ERPs are identical to Fig. 3 (left). Vertical lines demarcate the onset of the first statistically reliable item- and source effect in each region. Darker portion of vertical lines highlights the relevant condition differences.

In summary, although item- and source effects were indistinguishable within and across regions via conventional fMRI analysis, time-resolved BOLD data showed that these effects can be temporally dissociated. This dissociation was confirmed by the iEEG data: In the hippocampus, we observed an early source effect that terminated with the participants’ memory response, and an item effect that onset much later in the trial, after the memory decision had been made. In perirhinal cortex, our data suggest that there might be two independent processes: A fast and transient item effect and a later-onsetting source effect that is sustained throughout the retrieval period and terminates with participants’ memory decision. Thus, while both regions seem to conjointly support source retrieval (see below), the item effect appears to reflect different processes in each case: a fast novelty signal in perirhinal cortex and a late, post-retrieval encoding process in the hippocampus.

To test the notion that successful source retrieval is accompanied by increased functional coupling across hippocampus and perirhinal cortex, we conducted connectivity analyses in both fMRI and iEEG data. For the fMRI data, we used a Psychophysiological interaction (PPI) analysis (see Methods). Using the individually- and anatomically-defined hippocampi as seed regions, a set of bilateral perirhinal cortex clusters emerged showing greater functional coupling with the hippocampus during SR vs. IR (Pcorrected = .042 Fig. 5a ). No differences in hippocampal-perirhinal coupling were observed for the item effect (CR vs. IR) at P < .001, uncorrected.

Functional coupling between hippocampus and perirhinal cortex increases during source recognition (SR vs. IR). a. MTL regions showing a psychophysiological interaction (PPI) using participants’ individually drawn hippocampi as seed regions (schematized in the sagittal view, right panel). Results are shown at P < .001 (uncorrected) for display purposes. Left peak: x = �, y = 2, z = � right peak: x = 21, y = 2, z = �. b. Time/frequency clusters of significant iEEG coherence differences between SR vs. IR (P < .05, corrected for multiple comparisons). Color reflects absolute t values for SR vs. IR (only significant t values shown). c. Average (+/− s.e.m.) time course of coherence in the significant clusters. Left: Coherence in the low gamma band (30-35 Hz) is enhanced for SR relative to IR between

700 to 800 ms. Right: Coherence in the alpha band (8-12 Hz) is reduced for SR relative to IR between

For the iEEG data, we conducted spectral coherence analysis (see Methods for details). For the comparison of SR vs. IR, this analysis revealed two clusters (P < .05, corrected via a cluster-based statistic across time and frequencies 23 see Methods) in which coherence differed significantly across conditions ( Fig. 5 b,c ). First, SR showed significantly greater coherence between hippocampus and perirhinal cortex in the low gamma band (30-35 Hz) from

700 to 800 ms. Second, we observed increased coherence for IR – or, conversely, increased decoupling for SR - between hippocampus and perirhinal cortex in the alpha band (8-12 Hz) from

500 to 900 ms. Because the temporal resolution of spectral analysis is inferior to that of ERPs we refrain from making strong conclusions about the timing of these gamma- and alpha-coupling effects, but it is worth noting that both effects overlap in time and coincide with the period in which both regions show differential ERPs for SR vs. IR. The increase in low gamma coupling for successful source recognition is consistent with previous findings during successful relative to unsuccessful memory encoding 24, 25 , and the inverse alpha coupling effect (greater for unsuccessful source recognition) is interesting in light of the emerging role of alpha oscillations in functional inhibition of brain regions 26 . Again, no changes in coherence were observed for CR vs. IR (using the same statistical threshold as for SR vs. IR).

Together, the fMRI and iEEG connectivity results provide strong support for the notion that successful source retrieval is accompanied by an increase in functional coupling between hippocampus and perirhinal cortex.


Temporally truncate BOLD data - Psychology

<p>Spatial maps, BOLD activity around the trial onsets, and the relationship between their activity and the response time (RT) for the nine RT-predictive TCNs. (A) dorsal anterior cingulate cortex TCN (IC53) (B) anterior insula TCN (IC38) (C) frontal operculum TCN (IC41) (D) right middle frontal gyrus TCN (IC50) (E) left fusiform gyrus TCN (IC23) (F) right fusiform gyrus TCN (IC24) (G) sensorimotor TCN (IC13) (H) left sensorimotor TCN (IC05) (I) right sensorimotor TCN (IC04). For each TCN, a spatial map (converted to <i>z</i>-score and thresholded with <i>z</i> >3.0) shows its average distribution over all subjects and sessions, sectioned at the highest peak position [with its Montreal Neurological Institute (MNI) coordinates given], and superimposed on the MNI 152 standard space T1 template image. Dots in the BOLD activity represent pre-trial activity (time ≤ 0.0 s) and task response (time > 0.0 s) averaged over the trials in four task sessions for each subject, with the black line showing the grand average over all subjects. The time course for the relationship between activity and RT is shown as the group-averaged time course of estimated coefficients of an analysis of covariance (ANCOVA) model explaining the variance of RT. Error bars in the graphs show standard error of the mean (SEM) over subjects.</p

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Keywords : qualitative-quantitative integration, observational methods, assessment methods, transdisciplinary approach, quantitative methods in the social sciences, measurement, quantification, data

Citation: Uher J (2018) Quantitative Data From Rating Scales: An Epistemological and Methodological Enquiry. Front. Psychol. 9:2599. doi: 10.3389/fpsyg.2018.02599

Received: 14 May 2018 Accepted: 03 December 2018
Published: 21 December 2018.

Ulrich Dettweiler, University of Stavanger, Norway

Martin Junge, University of Greifswald, Germany
Barbara Hanfstingl, Alpen-Adria-Universität Klagenfurt, Austria
Jennifer Hofmann, University of Zürich, Switzerland

Copyright © 2018 Uher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks

Spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal, as measured by functional magnetic resonance imaging (fMRI) at rest, exhibit a temporally coherent activity thought to reflect functionally relevant networks. Antero-mesial temporal structures are the site of early pathological changes in Alzheimer's disease and have been shown to be critical for declarative memory. Our study aimed at exploring the functional impact of basal connectivity of an anterior temporal network (ATN) on declarative memory. A heterogeneous group of subjects with varying performance on tasks assessing memory was therefore selected, including healthy subjects and patients with isolated memory complaint, amnestic Mild Cognitive Impairment (aMCI) and mild Alzheimer's disease (AD). Using Independent Component Analysis on resting-state fMRI, we extracted a relevant anterior temporal network (ATN) composed of the perirhinal and entorhinal cortex, the hippocampal head, the amygdala and the lateral temporal cortex extending to the temporal pole. A default mode network and an executive-control network were also selected to serve as control networks. We first compared basal functional connectivity of the ATN between patients and control subjects. Relative to controls, patients exhibited significantly increased functional connectivity in the ATN during rest. Specifically, voxel-based analysis revealed an increase within the inferior and superior temporal gyrus and the uncus. In the patient group, positive correlations between averaged connectivity values of ATN and performance on anterograde and retrograde object-based memory tasks were observed, while no correlation was found with other evaluated cognitive measures. These correlations were specific to the ATN, as no correlation between performance on memory tasks and the other selected networks was found. Taken together, these findings provide evidence that basal connectivity inside the ATN network has a functional role in object-related, context-free memory. They also suggest that increased connectivity at rest within the ATN could reflect compensatory mechanisms that occur in response to early pathological insult.


Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities.

Keywords: BOLD Combined intra-cranial EEG-fMRI Distributed source modeling Gamma Intra-cranial EEG Multi-modal imaging fMRI.


Temporally truncate BOLD data - Psychology

<p>Spatial maps, BOLD activity around the trial onsets, and the relationship between their activity and the response time (RT) for the nine RT-predictive TCNs. (A) dorsal anterior cingulate cortex TCN (IC53) (B) anterior insula TCN (IC38) (C) frontal operculum TCN (IC41) (D) right middle frontal gyrus TCN (IC50) (E) left fusiform gyrus TCN (IC23) (F) right fusiform gyrus TCN (IC24) (G) sensorimotor TCN (IC13) (H) left sensorimotor TCN (IC05) (I) right sensorimotor TCN (IC04). For each TCN, a spatial map (converted to <i>z</i>-score and thresholded with <i>z</i> >3.0) shows its average distribution over all subjects and sessions, sectioned at the highest peak position [with its Montreal Neurological Institute (MNI) coordinates given], and superimposed on the MNI 152 standard space T1 template image. Dots in the BOLD activity represent pre-trial activity (time ≤ 0.0 s) and task response (time > 0.0 s) averaged over the trials in four task sessions for each subject, with the black line showing the grand average over all subjects. The time course for the relationship between activity and RT is shown as the group-averaged time course of estimated coefficients of an analysis of covariance (ANCOVA) model explaining the variance of RT. Error bars in the graphs show standard error of the mean (SEM) over subjects.</p

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Results

Behavioral Results

Our attempts to match memory performance across iEEG (patient) and fMRI (control) groups (by varying the number of sources, retention interval and encoding list length see methods) were successful ( Table 1 ). A “Pr” measure (probability of correct minus probability of incorrect “old” decisions to studied items) showed that overall recognition memory was significantly above chance (0), (fMRI: Pr = 70% (+/− 4), t19 = 16.18, P < .001 iEEG: Pr = 62% (+/− 9), t4 = 6.56, P = .003). Pr for source recognition (probability of correct minus incorrect source decisions to studied items excluding 𠇍on’t know” responses) was also significantly above chance (fMRI: Pr = 74% (+/− 6), t19 = 13.46, P < .001 iEEG: Pr = 52% (+/− 8), t4 = 6.15, P = .004). No significant differences were observed in item- or source Pr measures across fMRI vs. iEEG groups (both t23 < 1.63, P > .11). For further analyses, trials in which an item was recognized (“hit”), but either a 𠇍on’t know” response or an incorrect source response was given, were collapsed, and are referred to as “item recognition” (IR). Mean reaction times (RTs) for correct rejection (CR), item recognition (IR) and source recognition (SR) are shown in Table 1 . All pairwise comparisons were significant (fMRI: all t19 > 3.47, P < .01 iEEG: all t4 > 3.93, P < .05).

Table 1

Behavioral results for fMRI and iEEG version of the experiment. Memory performance is expressed in percentage of old or of new items. Standard errors are shown in parentheses. CR = correct rejection, IR = item recognition, SR = source recognition.

studied
items
unstudied itemssource memory out of hitsreaction times (sec)
hit missCR IRSRCRIRSR
correct
rejectio
n
false
alarm
“?”
respons
e
source
incorrec
t
source
correct
fMR
I
87
(3)
13 (3)82 (3)18 (3)35 (3)9 (2)56 (4)1.72
(.08)
2.27
(.06)
1.90
(.07)
iEE
G
82
(6)
18 (6)80 (10)20 (10)42 (11)14 (4)43 (8)1.65
(.19)
2.10
(.17)
1.86
(.20)

Imaging Results

For all imaging analyses, we defined two memory effects of interest: (i) an “item effect”, i.e., the difference between CR and IR, which emphasizes processes related to simple item recognition or novelty detection, while reducing the impact of target source retrieval (ii) a “source effect”, i.e., the difference between SR and IR, which emphasizes processes related to retrieval of target source details, while reducing the impact of item recognition/novelty.

For our fMRI data, we first queried item- and source memory effects via a conventional analysis of the blood-oxygenation-level-dependent (BOLD) response based on an assumed haemodynamic response function (HRF). We extracted the mean parameter estimates across voxels within each region of interest (ROI), where ROIs were defined anatomically and separately for each participant, based on their structural MRI ( Fig. 2 ). No hemispheric differences were seen in this or any subsequent analysis (see supplementary material), so the data reported here are averaged across left and right ROIs. We found that both hippocampus and perirhinal cortex exhibited a significant item effect (both t19 > 2.41, P < .05) as well as a significant source effect (both t19 > 2.18, P < .05), resulting in a “U-shaped” pattern across CR-IR-SR.

fMRI results for hippocampus (a) and perirhinal cortex (b). Left: Results from modeling conditions in a conventional analysis using an assumed HRF. Bar graphs represent mean (+ s.e.m.) parameter estimates. Note that, though differing with respect to baseline, item effects (CR vs. IR) and source effects (SR vs. IR) are indistinguishable within or across regions. Middle: Average (+/− s.e.m.) fMRI BOLD time courses versus baseline for the three conditions of interest. Right: Statistical development of the item effect (differential evoked response for CR vs. IR) and source effect (SR vs. IR), showing t-values for each effect across time. Points above the dashed line correspond to P < .05, two-tailed. Note the temporal dissociation of item- and source effects within and across regions.

Next we assessed whether item- and source effects might show different temporal BOLD profiles. As would be expected based on the above results, the integrated BOLD signal (averaged from 3-9 sec post stimulus onset) in both hippocampus and perirhinal cortex revealed a significant item effect (both t19 > 2.91, P < .01) as well as a significant source effect (both t19 > 3.27, P < .005). More critically, however, our data showed that these effects have different temporal characteristics within and across regions ( Fig. 2 ). In the hippocampus, the item effect was delayed relative to the source effect, whereas in the perirhinal cortex, there was an early and transient item effect, together with a more sustained source effect. These temporal dissociations within regions were confirmed by a repeated-measures ANOVA with the factors Effect (item, source) and Time (TR1-4), which revealed significant Effect x Time interactions in both regions (both F3,57 > 4.61, P < .01). Moreover, the BOLD data suggested that the sequence of item- and source effects differs across the two regions, as evidenced by a significant Region x Effect x Time interaction (F3,57 = 3.83, P = .01). Comparing the latencies of each participant’s effect peak (using a non-parametric Wilcoxon test), we observed that the perirhinal cortex item effect peaked significantly earlier than the hippocampal source effect (P = .02), whereas the perirhinal cortex source effect peaked significantly earlier than the hippocampal item effect (P < .005). Nonetheless, inferring the latency of neural activity from the temporal characteristics of the BOLD response is difficult (given potential nonlinear neural-vascular mappings 22 ). Thus, to better explore the temporal profiles of memory signals in the hippocampus and perirhinal cortex, we capitalized on the real-time resolution of the iEEG data.

In a first step, we defined two time windows of interest: An early window ranging from 250-750ms post stimulus onset that encompassed the initial peak responses in both hippocampus and perirhinal cortex when collapsing across conditions (Fig. S1a), and a late window from 800-2000ms post stimulus onset that captured a sustained, second component in both regions (Fig. S1b). (As in the fMRI data, no hemispheric differences were seen supplementary material.)

In the hippocampus ( Fig. 3a ), the early analysis window (250-750ms) showed a significant source effect (t4 = 4.88, P = .008), but no evidence of an item effect (t4 = 0.05, P = .960). In the late analysis window (800-2000ms), the item effect was now significant (t4 = 3.17, P = .034), whereas the source effect no longer reached significance (t4 = 1.82, P = .142). The late onset of the item effect suggests that this hippocampal response might reflect post-retrieval processes for new items, rather than the fast identification of old items that is expected by the behavioral evidence for rapid familiarity-based recognition. To assess more directly whether the hippocampal item effect reflects post-retrieval processes, we compared item- and source effects in response-locked (instead of stimulus-locked) ERPs. More specifically, we compared a 500ms time window from �ms to �ms with that from +250ms to +750ms, defined relative to the participants’ key-press on each trial. Results revealed an interaction between Time Window (pre, post) and Effect (item, source) (F1,4 = 11.07, P = .029), due to the pre-response window showing a source effect (t4 = 3.49, P = .025), but no item effect (t4 = 1.09, P = .339), and the post-response window showing an item effect (t4 = 3.33, P = .029), but no source effect (t4 = 0.95, P = .398). This result suggests that the hippocampal source effect precedes the memory judgment, whereas the item effect only occurs after the memory judgment, and thus likely reflects processes such as incidental encoding of experimentally novel information (see Discussion).

iEEG results for hippocampus (a) and perirhinal cortex (b). Left: Stimulus-locked ERPs. Right: Response-locked ERPs. Shaded areas show the two time windows used for statistical analysis.

In the perirhinal cortex ( Fig. 3b ), the early time window (250-750ms) showed both an item effect (t4 = 5.82, P = .004) and a source effect (t4 = 3.20, P = .033). In the late time window (800-2000ms), the source effect was still significant (t4 = 5.30, P = .006), whereas the item effect was not (t4 = 0.34, P = .752). For the response-locked analysis, there was - unlike in the hippocampus - no differential size of source- vs. item effects with respect to pre- vs. post-response time windows (F1,4 = 0.12, P = .743) only a main effect of Time Window (F1,4 = 12.12, P = .025), reflecting the fact that the combined item- and source effects were stronger in the pre- than in the post-response time window. However, only the source effect reached significance in the pre-response time window (t4 = 7.46, P = .002), whereas the item effect did not (t4 = 2.24, P = .088), suggesting that the source effect was sustained and terminated with the memory response compared to a rapid and transient item effect.

To further investigate the latency of these effects, we calculated the earliest timepoint to show a reliable item and source effect within each region. The first effect was an item effect in perirhinal cortex at 200ms post-stimulus onset. Next, we observed a source effect in the hippocampus at 250ms, followed by a source effect in perirhinal cortex at 400ms. A hippocampal item effect was first observed at 1050ms ( Fig. 4 ).

Temporal sequence of iEEG item- and source effects in perirhinal cortex (bottom) and hippocampus (top). ERPs are identical to Fig. 3 (left). Vertical lines demarcate the onset of the first statistically reliable item- and source effect in each region. Darker portion of vertical lines highlights the relevant condition differences.

In summary, although item- and source effects were indistinguishable within and across regions via conventional fMRI analysis, time-resolved BOLD data showed that these effects can be temporally dissociated. This dissociation was confirmed by the iEEG data: In the hippocampus, we observed an early source effect that terminated with the participants’ memory response, and an item effect that onset much later in the trial, after the memory decision had been made. In perirhinal cortex, our data suggest that there might be two independent processes: A fast and transient item effect and a later-onsetting source effect that is sustained throughout the retrieval period and terminates with participants’ memory decision. Thus, while both regions seem to conjointly support source retrieval (see below), the item effect appears to reflect different processes in each case: a fast novelty signal in perirhinal cortex and a late, post-retrieval encoding process in the hippocampus.

To test the notion that successful source retrieval is accompanied by increased functional coupling across hippocampus and perirhinal cortex, we conducted connectivity analyses in both fMRI and iEEG data. For the fMRI data, we used a Psychophysiological interaction (PPI) analysis (see Methods). Using the individually- and anatomically-defined hippocampi as seed regions, a set of bilateral perirhinal cortex clusters emerged showing greater functional coupling with the hippocampus during SR vs. IR (Pcorrected = .042 Fig. 5a ). No differences in hippocampal-perirhinal coupling were observed for the item effect (CR vs. IR) at P < .001, uncorrected.

Functional coupling between hippocampus and perirhinal cortex increases during source recognition (SR vs. IR). a. MTL regions showing a psychophysiological interaction (PPI) using participants’ individually drawn hippocampi as seed regions (schematized in the sagittal view, right panel). Results are shown at P < .001 (uncorrected) for display purposes. Left peak: x = �, y = 2, z = � right peak: x = 21, y = 2, z = �. b. Time/frequency clusters of significant iEEG coherence differences between SR vs. IR (P < .05, corrected for multiple comparisons). Color reflects absolute t values for SR vs. IR (only significant t values shown). c. Average (+/− s.e.m.) time course of coherence in the significant clusters. Left: Coherence in the low gamma band (30-35 Hz) is enhanced for SR relative to IR between

700 to 800 ms. Right: Coherence in the alpha band (8-12 Hz) is reduced for SR relative to IR between

For the iEEG data, we conducted spectral coherence analysis (see Methods for details). For the comparison of SR vs. IR, this analysis revealed two clusters (P < .05, corrected via a cluster-based statistic across time and frequencies 23 see Methods) in which coherence differed significantly across conditions ( Fig. 5 b,c ). First, SR showed significantly greater coherence between hippocampus and perirhinal cortex in the low gamma band (30-35 Hz) from

700 to 800 ms. Second, we observed increased coherence for IR – or, conversely, increased decoupling for SR - between hippocampus and perirhinal cortex in the alpha band (8-12 Hz) from

500 to 900 ms. Because the temporal resolution of spectral analysis is inferior to that of ERPs we refrain from making strong conclusions about the timing of these gamma- and alpha-coupling effects, but it is worth noting that both effects overlap in time and coincide with the period in which both regions show differential ERPs for SR vs. IR. The increase in low gamma coupling for successful source recognition is consistent with previous findings during successful relative to unsuccessful memory encoding 24, 25 , and the inverse alpha coupling effect (greater for unsuccessful source recognition) is interesting in light of the emerging role of alpha oscillations in functional inhibition of brain regions 26 . Again, no changes in coherence were observed for CR vs. IR (using the same statistical threshold as for SR vs. IR).

Together, the fMRI and iEEG connectivity results provide strong support for the notion that successful source retrieval is accompanied by an increase in functional coupling between hippocampus and perirhinal cortex.


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Keywords : qualitative-quantitative integration, observational methods, assessment methods, transdisciplinary approach, quantitative methods in the social sciences, measurement, quantification, data

Citation: Uher J (2018) Quantitative Data From Rating Scales: An Epistemological and Methodological Enquiry. Front. Psychol. 9:2599. doi: 10.3389/fpsyg.2018.02599

Received: 14 May 2018 Accepted: 03 December 2018
Published: 21 December 2018.

Ulrich Dettweiler, University of Stavanger, Norway

Martin Junge, University of Greifswald, Germany
Barbara Hanfstingl, Alpen-Adria-Universität Klagenfurt, Austria
Jennifer Hofmann, University of Zürich, Switzerland

Copyright © 2018 Uher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Basal functional connectivity within the anterior temporal network is associated with performance on declarative memory tasks

Spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) signal, as measured by functional magnetic resonance imaging (fMRI) at rest, exhibit a temporally coherent activity thought to reflect functionally relevant networks. Antero-mesial temporal structures are the site of early pathological changes in Alzheimer's disease and have been shown to be critical for declarative memory. Our study aimed at exploring the functional impact of basal connectivity of an anterior temporal network (ATN) on declarative memory. A heterogeneous group of subjects with varying performance on tasks assessing memory was therefore selected, including healthy subjects and patients with isolated memory complaint, amnestic Mild Cognitive Impairment (aMCI) and mild Alzheimer's disease (AD). Using Independent Component Analysis on resting-state fMRI, we extracted a relevant anterior temporal network (ATN) composed of the perirhinal and entorhinal cortex, the hippocampal head, the amygdala and the lateral temporal cortex extending to the temporal pole. A default mode network and an executive-control network were also selected to serve as control networks. We first compared basal functional connectivity of the ATN between patients and control subjects. Relative to controls, patients exhibited significantly increased functional connectivity in the ATN during rest. Specifically, voxel-based analysis revealed an increase within the inferior and superior temporal gyrus and the uncus. In the patient group, positive correlations between averaged connectivity values of ATN and performance on anterograde and retrograde object-based memory tasks were observed, while no correlation was found with other evaluated cognitive measures. These correlations were specific to the ATN, as no correlation between performance on memory tasks and the other selected networks was found. Taken together, these findings provide evidence that basal connectivity inside the ATN network has a functional role in object-related, context-free memory. They also suggest that increased connectivity at rest within the ATN could reflect compensatory mechanisms that occur in response to early pathological insult.


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Figures

Illustrations of characteristics of an…

Illustrations of characteristics of an SVM. ( A ) Two different datasets (red…

Illustration of mutual information in…

Illustration of mutual information in consistent (i.e., most of x i s are…

Functional maps from a group…

Functional maps from a group analysis of non‐smoothed data. In functional maps (F(SW)‐,…

Ratio of area of clusters…

Ratio of area of clusters remaining after applying the second‐level threshold to total…


Cortex-based inter-subject analysis of iEEG and fMRI data sets: application to sustained task-related BOLD and gamma responses

Linking regional metabolic changes with fluctuations in the local electromagnetic fields directly on the surface of the human cerebral cortex is of tremendous importance for a better understanding of detailed brain processes. Functional magnetic resonance imaging (fMRI) and intra-cranial electro-encephalography (iEEG) measure two technically unrelated but spatially and temporally complementary sets of functional descriptions of human brain activity. In order to allow fine-grained spatio-temporal human brain mapping at the population-level, an effective comparative framework for the cortex-based inter-subject analysis of iEEG and fMRI data sets is needed. We combined fMRI and iEEG recordings of the same patients with epilepsy during alternated intervals of passive movie viewing and music listening to explore the degree of local spatial correspondence and temporal coupling between blood oxygen level dependent (BOLD) fMRI changes and iEEG spectral power modulations across the cortical surface after cortex-based inter-subject alignment. To this purpose, we applied a simple model of the iEEG activity spread around each electrode location and the cortex-based inter-subject alignment procedure to transform discrete iEEG measurements into cortically distributed group patterns by establishing a fine anatomic correspondence of many iEEG cortical sites across multiple subjects. Our results demonstrate the feasibility of a multi-modal inter-subject cortex-based distributed analysis for combining iEEG and fMRI data sets acquired from multiple subjects with the same experimental paradigm but with different iEEG electrode coverage. The proposed iEEG-fMRI framework allows for improved group statistics in a common anatomical space and preserves the dynamic link between the temporal features of the two modalities.

Keywords: BOLD Combined intra-cranial EEG-fMRI Distributed source modeling Gamma Intra-cranial EEG Multi-modal imaging fMRI.


Author information

Arielle Tambini and Ulrike Rimmele: These authors equally contributed to this work.

Affiliations

Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA

Department of Basic Neurosciences, University of Geneva Campus Biotech, Geneva, Switzerland

Department of Psychology, New York University, New York, New York, USA

Elizabeth A Phelps & Lila Davachi

Center for Neural Science, New York University, New York, New York, USA

Elizabeth A Phelps & Lila Davachi

Nathan Kline Institute, Orangeburg, New York, USA

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Contributions

A.T., U.R., E.A.P. and L.D. designed the experiment and wrote the paper. A.T. and U.R. collected and analyzed the data.

Corresponding author


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Introduction

When faced with a difficult choice, people rarely make decisions in a social vacuum but use the information provided by the actions of other people who made the same choice previously. Consider the example of choosing between two restaurants to dine at in a foreign city. Your travel guide recommends both, but when you go to check them out, one is very busy and the other is empty. The previous actions of the other diners might bias you towards choosing the busier restaurant. Social information is fundamental and can combine with, or even substitute for, individually acquired information about options to guide our decisions.

The informational value of others’ actions can be high, as when rats observe conspecifics die after consuming a particular food. However, once a herd has formed it may become difficult to glean any information from the actions of others. In the restaurant example above, you might be unsure as to whether the other diners actually know something you do not, or just followed the first few people to go in. It is in this manner that a sound choice on the individual level can give rise to inefficient or detrimental behavior on the aggregate level (Camerer and Weigelt, 1991 Banerjee, 1992 Bikhchandani et al., 1992 Anderson and Holt, 1997 ). Indeed, herd behavior may contribute to bubbles in financial and housing markets (Baddeley 2005 Dale et al., 2005 ), online consumer preferences (Hansen and Putler, 1996 ) and conforming to a group in perceptual decision tasks, despite clear sensory evidence against it (Bond and Smith, 1996 ). The critical feature of economic and psychological accounts of herding is that observing the actions of others, and not the outcomes of those actions, exerts a measurable difference on the individual’s choice.

If the behavior of others would change the value of options, one would expect to detect such an effect in value coding regions of the brain. A core region of the value system is the ventral striatum. It processes economic value parameters such as the mean and the variance of choice options (Preuschoff et al., 2006 ). Here we tested the hypothesis that it would also be sensitive to the actions of others in the context of an economic decision making (stock buying) task. To do this, we first asked whether the ventral striatum is sensitive to the actions of others (social information) in the absence of economic information. Secondly, we asked if the same regions were sensitive to economic information (the mean and variance of various stock return data) in the absence of social information. Thirdly, if the ventral striatum was sensitive to both social and stock information, we investigated how this combined information is represented in the brain. For example, do the actions of others change the processing of objective stock value or variance or do they modulate subjective preferences for different options? Methodologically, we were in a position to answer these three questions by having trial types with only social, only stock, or with both kinds of information. For the fist two questions, we hypothesized that participants would make more 𠇋uy” decisions as more of the herd bought, even in the absence of any stock information. In addition to this, there would be an increase in ventral striatum activity inline with behavior. This reflection of behavior in ventral striatum activity would also be observed in response to stock information when social information was absent. For the third question, we hypothesized that social information would modulate the processing of stock parameters directly. For example, activity in the ventral striatum relating to a stock value signal would be increased if participants subsequently became aware that others had bought that particular stock. In agreement with our first and second hypotheses, we found that activity in the ventral striatum tracks social information (actions of others), and economic information (stock parameters). However, in contrast to the third hypothesis, social information influenced ventral striatum activity through participants’ subjective preferences for different stock types rather than by modulating the objective stock properties.



Comments:

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  3. Keefer

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  4. Moogulmaran

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