Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods


Authors: Kenneth Hugdahl aff001;  Katarzyna Kazimierczak aff001;  Justyna Beresniewicz aff001;  Kristiina Kompus aff001;  Rene Westerhausen aff004;  Lars Ersland aff005;  Renate Grüner aff003;  Karsten Specht aff001
Authors place of work: Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway aff001;  Division of Psychiatry, Haukeland University Hospital, Bergen, Norway aff002;  Department of Radiology, Haukeland University Hospital, Bergen, Norway aff003;  Institute of Psychology, University of Oslo, Oslo, Norway aff004;  Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway aff005;  Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway aff006;  Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway aff007
Published in the journal: PLoS ONE 14(9)
Category: Research Article
doi: 10.1371/journal.pone.0218358

Summary

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose, we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

Keywords:

Biology and life sciences – Neuroscience – Brain mapping – Functional magnetic resonance imaging – Neuroimaging – Functional magnetic resonance imaging – Cognitive science – Cognition – Cognitive psychology – Attention – Neural networks – Anatomy – Head – Ears – Brain – Cingulate cortex – Psychology – Cognitive psychology – Attention – Medicine and health sciences – Diagnostic medicine – Diagnostic radiology – Magnetic resonance imaging – Functional magnetic resonance imaging – Radiology and imaging – Diagnostic radiology – Magnetic resonance imaging – Functional magnetic resonance imaging – Anatomy – Head – Ears – Brain – Cingulate cortex – Research and analysis methods – Imaging techniques – Diagnostic radiology – Magnetic resonance imaging – Functional magnetic resonance imaging – Neuroimaging – Functional magnetic resonance imaging – Research design – Experimental design – Social sciences – Psychology – Cognitive psychology – Attention – Computer and information sciences – Neural networks – Network analysis

Introduction

Applying an inclusive conjunction analysis to fMRI data from nine different studies with a total of 187 subjects, and comprising nine different cognitive tasks, Hugdahl et al. [1] found a generalized cortical network that was independent of the specifics of the individual task and cognitive processes. The authors labeled this the extrinsic mode network (EMN), as a task non-specific network, with a fronto-temporo-parietal distribution, including the inferior and middle frontal gyri, inferior parietal lobule, supplementary motor area, and the inferior temporal gyrus. The EMN thus has a spatial architecture overlapping with what Fedorenko et al. [2] labeled the "cognitive flexibility network", and Duncan [3] labeled the "multiple demand network" (see also [4]). Common for all these networks is that they are suggested to be general-domain networks, i.e. they show up-regulation of activity across a range of cognitive tasks, e.g. [5], [6], [7], [8]. The general-domain networks further share characteristics with several domain-specific networks, such as the dorsal attention and salience networks [9], [10], [11], [12]), central executive network [13], [14], fronto-parietal network [15], and ventral attention network [16], see also Lee et al. [17], and Cabeza and Nyberg [18] for overviews. It is therefore clear that the dynamic interplay between various cortical networks, during different mind-states is far more complex than previously thought [19, 20, 21]. In addition, while there are similarities between the EMN and other task-positive networks with regard to overlapping activity patters, a clear difference is that the EMN is supposed to be a domain non-specific network, while the typical task-positive networks, like the dorsal attention and central executive networks are supposedly domain-specific networks. In the current study, we asked how the EMN as a general-domain network relates to the more known default mode network (DMN) [12], [22], [23], see also [24], [25], [26], [27], which is typically observed in the absence of specific tasks. It could be predicted that the DMN should be negatively, or anti- correlated with a general-domain network, like the EMN. This prediction is derived from previous findings that neuronal activity varies reciprocally between characteristic DMN- and task-positive network-areas, when studied under both resting-state [16], [26], [28], [29], and during active task conditions [30], [31], [32]. Lustig et al. [30] used alternating blocks of passive viewing of a fixation cross and active processing of a semantic judgment task. Results showed increased activations in the left frontal cortex during task-processing and deactivation in the same area during passive fixation blocks. An opposite pattern was seen in the lateral parietal cortex, with deactivations during task processing. The study by Lustig et al. [30] therefore showed inverse activations and deactivations in brain areas linked to active task-processing compared to passive viewing. Following previous findings we therefore asked whether a similar relationship should hold for general-domain networks, and in particular for the EMN, when different cognitive tasks are alternated during the scanning session. Thus, we alternated brief periods with task presentations with brief periods of resting with no tasks present. Conventional analysis of block-design data is to subtract activity during OFF-blocks from activity during ON-blocks, where the OFF-blocks act as a baseline control-condition (we leave out here the discussion in the literature whether the assumption of "pure insertions" in block-designs is a valid assumption or not, cf. [33]. The resulting activity pattern would thus reflect active task-processing. By subtracting activity obtained during task-processing ON-blocks from activity obtained during resting OFF-blocks, cf. [22], it should be possible to display activity that would be deactivated during task-processing blocks. We are not focusing here on whether task-positive and task-negative networks are anti-correlated per se in resting-state fMRI situations, cf. [30], [34], but on the time-dynamics of up- and down-regulations across the transitions between task-present and task-absent periods, with a focus on the interaction between the DMN and EMN networks. For this purpose, we used a finite-impulse response (FIR) function to model the BOLD response as implemented in the Statistical Parametric Mapping (SPM) analysis software (https://www.fil.ion.ucl.ac.uk/spm/). We re-analyzed fMRI data from >100 healthy individuals from a previous study in our laboratory [35], where the subjects had been tested with an auditory dichotic listening (DL) task with three instruction conditions that emphasized either perception [36], attention/vigilance [37] or executive control functions [38]. We chose this task because it reflects the changing cognitive demands and coping situations during an ordinary day, including both low-, perception, and high-, executive control, level demands. The so called forced-attention DL paradigm was originally developed by Hugdahl and Andersson [37] for the study of the role of cognitive factors in auditory perception, and it has repeatedly been shown to produce valid and reliable results with regard to perception, attention and executive control, see e.g. [39], [40], [41], [42], [43], [44], [45], [46]. We now report how the EMN interact with the DMN within a single paradigm which included alternating task-presence (ON) and task-absence (OFF) periods, and with recurring and varying cognitive tasks and demands. Such an experimental design would be a novel way of capturing the dynamic interaction between passive rest and active processing periods, going beyond a fMRI "resting period" data acquisition approach. In order to capture the dynamics of network up- and down-regulation over time, and especially at the transition points between ON- and OFF-blocks, we specified the onset of each condition and used a 5.5s finite-impulse response function (FIR) as basis function for estimation of the BOLD response. This approach modeled each scan per task-ON- and task-OFF-block separately, which would allow an analysis of the fine-grained dynamics in the critical time-window when the situation switched from active to passive, and from passive to active time periods.

Materials and methods

Participants

The participants were 104 healthy adults, with mean age of 29.3 years, standard deviation 8.3 years. Approximately half of the participants were males, and half were females. The participants volunteered for participation, and further details can be found in [36]. The study was conducted according to the Declaration of Helsinki regarding ethical standards. The re-analyzed data had in addition previously been approved by the Regional Ethics Committee for Medical Research in the Western Health Region of Norway (REK-Vest), and also been completely anonymized before the current analyses were made.

Cognitive tasks

The cognitive task was an auditory speech perception task, with repeated dichotic presentations of two different consonant-vowel (CV) syllables presented on each trial, one in the right ear and the other at the same time in the left ear. The participant is not told that there are two different sounds, one in each ear, on every trial. The baseline instruction to the participant was to report which syllable they perceived most clearly on each trial, emphasizing a single response, and with no instruction about allocation of attention to either the right or left ear. The task consists of repeated presentations of syllable-pair, using all combinations of the six stop-consonants /b/, /d/, /g/, /p/, /t/, /k/ paired with the vowel /a/, making up the CV-syllables /ba/, /ga/, /pa/, /da/, /ka/, /ta/. A trial could thus be the presentation of /ba/ in the left ear and simultaneously the syllable /pa/ in the right ear, see [36] for an overview of the dichotic listening task. The dichotic CV-syllable task has historically been used for the study of hemispheric asymmetry, which is reflected in the typical higher accuracy scores for reporting of the right ear stimulus, called a right-ear advantage (REA) [38], [47], [48], [49]. The paradigm has however also been used for the study of higher cognitive functions, like attention and executive control functions [46], [50], [51], [52], which Hugdahl and Andersson [36] labeled "the forced-attention" dichotic listening paradigm. In the latter case instructions to explicitly focus attention to and report from only the right or left ear is alternated between trial-blocks and mixed with blocks of no-attention-focus instruction. A methodological advantage with the forced-attention variant is that it allows for the study of perception, attention and executive functions within the same experimental paradigm, by simply changing the instructions to the participants in the course of the experimental session, see [38] for further examples. In the present fMRI-variant of the DL paradigm, each of the three instruction blocks (no instruction, instruction to focus on the right ear, instruction to focus on the left ear) were repeated three times during the session. The order of the presentation of the three conditions were pseudo randomized among the non-forced (NF), forced-right ear (FR), and forced-left ear (FL) instruction epochs. To approximate an every-day situation with brief processing and resting periods, the nine task-present epochs (ON-blocks) were alternated with nine resting epochs (OFF-blocks) with no stimuli or instruction present. Each ON- and OFF-block had a duration of 55 sec. Since the focus of the present study was the dynamic interaction of the EMN and DMN networks, we present data averaged across the three instruction conditions, since this will capture the conglomerate activity across the three cognitive tasks, to act as a proxy of for the fluctuations of cognitive demands experienced during the course of a day.

MR imaging

The MR scanner was a 3T GE SignaHDx scanner, and for the initial anatomical scanning, a T1 3D Fast Spoiled Gradient Recall sequence (FSPGR) was applied: TE = 14 ms, TR = 400 ms, TI = 500 ms), with 188 consecutive sagittal slices (1 mm thick, no gap, scan matrix: 256 x 256; FOV 256 mm). For the following echo-planar functional imaging (EPI), a sparse-sampling sequence was applied with TR of 5.5 sec, and with acquisition time (TA) of 1.5 sec, leaving a silent gap of 4 sec when the stimuli were presented, see [46]. 180 EPI-volumes were acquired, consisting of 25 axial slices in each volume (FOV: 220 mm; scan matrix 64 x 64; 5 mm slice thickness, 0.5 mm gap; TE = 30 ms). There were 10 EPI-volume acquisitions, or scans, during each of the task-present and task-absent blocks, each of 55 sec. Total session time for the EPI-imaging part was thus (9 x 55) x 2 = 16.5 min, with regularly alternating task-present and task-absent blocks.

Statistical analysis and visualization of fMRI data

The fMRI-data were analyzed with the SPM12 software package (Wellcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm/), following standard SPM settings. In brief, the raw DICOM images were converted to nifti-format, and pre-processed following SPM-implanted routines for realignment and unwarping, normalizing the EPI-images to the MNI template, and smoothing with an 8 mm kernel. Thereafter, 1st-level analysis was performed, by specifying the onset of each condition and using a finite-impulse response function (FIR) as basis function, which modeled each scan per ON and OFF block separately but averaged across repetitions of the same condition. The resulting beta-images/time-bins (TBs) (20 per condition, NF, FR, FL) were then used as input for the next, 2nd-level analysis, which was defined as a 3 x 20 repeated measure ANOVA model, with the factor condition (NF, FR, FL) and the factor time-bin (TB) (1–20). This model allows exploring not only averaged block-effects, mimicking “classical” ON-OFF contrasts, but also the temporal dynamics and time derivatives by specifying contrasts for each TB separately (see S1 Fig).

The following contrasts were specified: First, the average ON-OFF and OFF-ON contrasts, and second, corresponding averaged TBs for the ON and OFF contrast were compared, i.e. TB1 ON against TB1 OFF, TB2 ON against TB2 OFF, etc., and repeated for the remaining eight pairs of time-bins, and t-contrasts were specified in both directions. Third, we explored the time-derivatives of the activity by contrasting averaged time-bins next to each other, i.e. TB1 against TB2, TB2 against TB3, etc, i.e., TB n against TB n+1. These latter contrasts will reveal consecutive significant changes from one TB to the next, i.e. from TB n to TB n+1 as a “sliding window”. The “sliding window” contrast would be sensitive for picking up the time dynamics in the relative up- and down-regulation of the task-positive and task-negative networks, i.e. sensitive to the transition from the last TB for an ON-block to the first TB for an OFF-block, and vice versa. If an activity remained unchanged from one TB to the next (as one would expect for adjacent TBs in the middle of a block), this contrast would not show anything. Again, both directions of contrasts were estimated, i.e. whether there was a significant increase or decrease from one TB to the next. For simplicity, differential effects between the three conditions (NF, FR, FL) were not explored, since the focus was on activity that was common across diverse cognitive tasks, cfr. [1], [2], [4]. Results were explored for statistical significance, using a family-wise error (FWE)-corrected threshold level of p < .05 in the main analyses, to protect against Type-I errors, and with at least 10 voxel per cluster.

Results

The results of the averaged ON-OFF contrast revealed the typical task-positive activity pattern with bilateral activity in the auditory cortex and surroundings, and of the task-positive, EMN, network [1] in the prefrontal cortex, including the anterior and middle cingulate cortex, supplementary motor area (SMA/preSMA), and thalamus. The opposite contrast revealed areas, belonging to the task-negative, DMN, network [23], revealed significant activity in the precuneus, inferior parietal, and medial orbitofrontal areas, and in occipital areas (Figs 1 and 2) and (Tables 1 and 2). This could be activity returning to baseline during resting periods, as previously found, see e.g. [23], or as a new finding with increases above baseline in situations with alternating task-negative and task-positive periods.

<h2>fMRI BOLD activity for the ON-OFF contrast.</h2>
Fig. 1.

fMRI BOLD activity for the ON-OFF contrast.


Fig 1 shows BOLD activity for the ON–OFF contrast, thresholded at FWE .05, 10 voxels, in sagittal images through the entire brain volume, from “ear-to-ear”, rendered on an average MNI template.

<h2>fMRI BOLD activity for the OFF-ON contrast.</h2>
Fig. 2.

fMRI BOLD activity for the OFF-ON contrast.


Fig 2 shows BOLD activity for the OFF-ON OFF contrast, thresholded at FWE .05, 10 voxels, in sagittal images through the entire brain volume, from “ear-to-ear”, rendered on an average MNI template.

Tab. 1.

ON-OFF activated clusters and peak voxel coordinates.

<h2>ON-OFF activated clusters and peak voxel coordinates.</h2>

Summary of significantly activated clusters (with local maxima), and peak voxel x, y, z coordinates and corresponding t- and z-values and AAL atlas anatomical localizations, for the OFF-ON contrast (STG = superior temporal gyrus, SMA = supplementary motor area).

Tab. 2.

OFF-ON activated clusters and peak voxel coordinates.

<h2>OFF-ON activated clusters and peak voxel coordinates.</h2>

Summary of significantly activated clusters (with local maxima), and peak voxel x, y, z coordinates and corresponding t- and z-values and AAL atlas anatomical localizations, for the ON-OFF contrast (Precun = precuneus, SFG = superior frontal gyrus, MFG = middle frontal gyrus, ACC = anterior cingulate cortex).

The differential, TB-wise contrasts are presented as activity changes rather than contrast-by-contrast activity. First, the temporal evolution of the task-positive networks, including the EMN network, was explored. As can be seen from Fig 3, the task-positive activity started with a strong visual activity, reflecting the on-screen instruction, followed by activity of the auditory and EMN network after about 5.5 sec (one time-bin), which remained constant throughout the entire ON period and faded away during the first scan of the OFF block. Interestingly, the task-negative and DMN networks showed more dynamic changes than the task-positive and EMN networks. There was an initial time-lag of about 5.5 sec where neither the EMN nor the DMN or any other task-related networks was active, as is typically observed for the BOLD-response [53]. After this initial period, the DMN showed the strongest recurrence, which however faded away towards the end of the OFF block. The only activity that later evolved during the OFF blocks and remained throughout the block was the orbitofrontal recurrence. Fig 3 shows the overall activity including also line plots for the activity profiles from the posterior cingulate cortex and the right inferior frontal gyrus, representing hubs of the DMN and EMN networks, respectively. The line-graphs in the middle of the figure show the development of the BOLD response across time for the ON (left side) and OFF (right side) period, respectively, and extracted from the posterior cingulate cortex (PCC) and inferior frontal gyrus (IFG), representing the default mode network (blue line, DMN) and extrinsic mode network (red line, EMN), respectively.

<h2>fMRI BOLD activity split for time-bins.</h2>
Fig. 3.

fMRI BOLD activity split for time-bins.


Fig 3 shows the corresponding axial glass brains for the ON- (upper row) and OFF-periods (lower row), and split for corresponding time-bins (TBs).

When the time-derivative (TD) contrasts were explored, which reflect a significant increase or decrease of activity from one time-bin to the next, activity changes were observed only at the transitions between the blocks. Fig 4 shows the axial glass brains obtained from the time-derivative (TD) analysis during ON (left side) and OFF (right side) periods, separately for each time-derivative contrast, contrasting time-bin (TB)1 with TB2, TB2 with TB3 etc. in a “sliding window” through all TBs (1–20). The upper row of glass brains shows increases in activity, the lower row shows decreases in activity.

<h2>Illustration "sliding-window".</h2>
Fig. 4.

Illustration "sliding-window".


Fig 4 shows example of the time-bin analysis and "sliding window" technique .

When the ON block started (see TD1 in Fig 4), the EMN switched on at once and remained stable (no further effects detected by the temporal derivatives), together with the visual response to the instructions on the screen (Fig 4). The visual activity faded away over the first three TBs [53], i.e. over the first 11–16.5 sec (TD2 and TD3, lower row in Fig 4), while the auditory activity simultaneously faded in from the first to the second time bin (TD2) and remained stable after that (no increasing effects in the temporal derivatives TD3 and following (upper row in Fig 4)). At the end of the ON block, the auditory and articulatory motor activity rapidly disappeared in the transition from TB10 to TB11,(i.e. TD11) with a remaining decrease into TB12 within the right planum temporale (lower row in Fig 4). Interestingly, the DMN was not observed in these time-derivatives-like contrasts, indicating that the DMN did not show a sharp onset of activity at the beginning of the OFF block, comparable to the EMN did at the beginning of the ON block. Neither EMN, nor DMN showed a sharp decrease of activity in this analysis. This indicates that the offsets of the activity of these networks are smooth, as it can be seen from Fig 3. In conclusion, the EMN showed a sharp onset immediately when a response and attention was required, while the DMN gradually increased its activity towards the anticipated end of the ON block and continued increasing into the OFF block. Therefore, the time-derivatives-like contrasts could not detect these monotonic changes.

Discussion

To sum up the main findings, the ON- and OFF-blocks, corresponding to active task-processing versus passive resting, produced two orthogonal, non-overlapping activity patterns (see Figs 1 and 2). As is obvious from the figures, while task-present epochs resulted in a more anterior activity pattern, with the SMA/preSMA and the auditory cortices as the dominant activity regions, the task-absent epochs resulted in a more posterior activity pattern, including activity in the precuneus, and the parietal lobules as the dominant regions. These activity patterns would partially correspond to the EMN and DMM networks, respectively [1] [20]. The FIR analysis of the network dynamics based on time-derivative contrasts in addition revealed that the EMN showed a relatively sharp onset of the up-regulation at the beginning of the ON-blocks, while there was a more gradual up-regulation of the DMN during OFF-blocks. As expected, the DMN was more active during the OFF-blocks (see Fig 3), but no abrupt change was seen in the time-derivative analysis at the ON-OFF block intersections (see Fig 4). In their meta-analysis of nine PET blood-flow studies [18] found significant decreases for the active minus passive task condition in cortical areas that today would qualify as the "classic" areas for the default mode network. One interpretation of the decreases in the Schulman et al. [22] study is that task-processing may inhibit activity in areas that otherwise would be tonically activated in the absence of a task, as was suggested by Popa et al. [54]. These authors used electrophysiology recordings and found that local field potential power was lower in anterior cingulum and retrosplenial cortex during task-OFF compared to task-ON periods, while the reverse was found in somato-sensory association cortex and middle temporal gyrus. A further confirmation of this hypothesis would be if activity in approximately the same areas would be increased during passive resting periods in between active task processing periods, which the current results have shown. The areas showing decreased activity during active minus passive epochs in the Schulman et al. [23] study were primarily in the posterior cingulate/precuneus, dorsolateral and inferior frontal cortex, and in the inferior temporal gyrus (passive here meaning being exposed to the same stimulus, but without instruction to act on the stimulus). Approximately the same areas were activated in the present study, but now when subtracting active task-processing periods from activity during a resting period, which would confirm the hypothesis of inhibition of tonically active areas during phasic task-processing. Similarly, Schulman et al. [23] found increases in the visual cortex in the occipital lobe during active visual task processing, after averaging data from 10 different studies with visual tasks. This is paralleled in the present study which found corresponding increases in the auditory cortex in the temporal lobe to an auditory task. Previous studies have shown that the DMN is still up-regulated during task-presence periods but attenuated or suspended compared to task-related activity, e.g. [10], [55], [56]. Although the present findings are in line with these results, we cannot say if the corresponding networks were down-regulated or merely attenuated during reversals. An advantage with the present paradigm over previous paradigms that have been used, e.g. [2], [57], [58] is that the three different cognitive tasks were all embedded within the same experimental paradigm. The dichotic listening (DL) task is moreover exceptionally easy to understand and perform, so that the understanding of the task in itself does not require the allocation of additional cognitive resources which could confound task processing.

Fig 3 shows that the time-course for the task-positive EMN network followed a square-like trajectory, with a rapid initial up-regulation during ON-periods which peaked latest after about 11 sec (TB2), followed by a similarly rapid down-regulation during OFF-periods, peaking after about 22 sec (TB14). The corresponding trajectory for the task-negative DMN network showed a similar rapid down-regulation during ON-periods, peaking after about 22 sec (TB4), while the up-regulation during OFF-periods were more gradual than for the EMN network, actually beginning already in the middle of the ON-period (Fig 3). This could be an anticipatory effect of waiting for the "next task to be presented" and may represent an anticipatory shift of attention focus from task processing to resting, a kind of “readiness” for what is to come [59], which previously has been associated with alterations in EEG alpha-activity [60], [61]. A look at the time-course trajectories in Fig 3 shows a marked difference in the “gap” between the up-regulated and down-regulated network for ON versus OFF blocks. Although the two networks to a certain extent were down-regulated at about the same level, the level of up-regulation for the EMN was about twice the level of up-regulation for the DMN, which caused a gap difference between the two networks. The gap difference could reflect the additional increase in metabolism demands during active task-processing compared to resting. There was finally a delay for EMN up- and down-regulation of about 5.5 sec, which may reflect the delay and slowness of the BOLD-response in itself [61], and not an effect of network interaction and interference.

In conclusion, the EMN and DMN networks seem to alternate with the same frequency as the switch from active task-periods (ON-blocks) to passive rest-periods (OFF-blocks) which alternated on a 55 sec basis. From recent work that the DMN may represent a unique state of the mind e.g. [55], [62], [63], [64], we now suggest that the EMN likewise may represent a unique mind-state. Moreover, previous research has suggested a kind of "auto-pilot" role for the DMN [65], and fronto-striatal co-activation with task-induced network activation [66]. These states dynamically fluctuate over time, such that the individual is either in one or the other state, with corresponding network activity being dominant in a particular state, and that this is mapped onto how environmental processing demands change over time. It may finally be suggested that the dynamic interaction between task-positive and task-negative networks may be disrupted in certain psychiatric and neurological disorders, extending what previously has been suggested for DMN abnormality [67], [68], [69] [70]. We now suggest that what is abnormal in certain mental disorders may not be so much abnormality of a single network, but rather abnormality of network interaction and network dynamics, and that this is better captured in an experimental design with alternating task-ON and task-OFF periods, rather than a prolonged resting period during the scanning e.g. [71], [72, 73]. Before drawing to definitive conclusions, we would like to point out limitations with the study. First of all, the selection of a 5.5s TR may not have been optimal for the aims of the study, and we would like to point this out as a limitation of the results. However, the choice of a 5.5s TR was motivated from the nature of the task used in the study, which was an auditory dichotic listening task. In order to reduce interference from the scanner noise, a sparse sampling data acquisition technique was used, for which purpose a TR of 5.5s (with 1.5s acquisition time and 4s silent gap until next acquisition) is quite optimal considering the nature of the task presented. Another limitation may be that "The use of an auditory task may obscure comparisons with other studies using a visual task. Having said that, it is our belief that the process of cognitive challenge is not bound to the sensory channel it is initiated through but depends on the capacity of the task for cognitive engagement."

Supporting information

S1 Fig [tb]
Illustration of the experimental design.


Zdroje

1. Hugdahl K., Raichle M. E., Mitra A., Specht K. (2015). On the existence of a generalized non-specific task-dependent network. Frontiers in Human Neuroscience, http://dx.doi.org/10.3389/fnhum.2015.00430.

2. Fedorenko E., Duncan J., Kanwisher N. (2013). Broad domain generality in focal regions of frontal and parietal cortex. PNAS, 110, 16616–16621 doi: 10.1073/pnas.1315235110 24062451

3. Duncan J (2010) The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behaviour. Trends in Cognitive Sciences 14, 172–179. doi: 10.1016/j.tics.2010.01.004 20171926

4. Duncan J.D., & Owen A.M. (2000). Common regions of the human frontal lobe recruited by diverse cognitive demands. Trends in Neurosciences, 23, 475–483. 11006464

5. Corbetta M., & Schulman G.L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215. doi: 10.1038/nrn755 11994752

6. Dosenbach N.U.F., Fair D.D, Miezin F.M., Cohen A.L., Wenger K.K., Dosenbach R.A.T., Fox M.D., Snyder A.Z., Vincent. J.L., Raichle M.E., Schlaggar B.L., Petersen S.E. (2007). Distinct brain networks for adaptive and stable task control in humans, PNAS, 104, 11073–11078 doi: 10.1073/pnas.0704320104 17576922

7. Gratton C., Laumann T.O., Nielsen A.N., Greene D.J., Gordon E.M., Gilmore A.W., Nelson S.M., Coalson R.S., Snyder A.Z., Schlaggar B.L., et al. (2018). Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439–452 e435. doi: 10.1016/j.neuron.2018.03.035 29673485

8. Gonzalez-Castillo J., Saad Z.S., Handwerker D.A., Inati S.J., Brenowitz N., and Bandettini P.A. (2012). Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. PNAS, 109, 5487–5492. doi: 10.1073/pnas.1121049109 22431587

9. Corbetta M., Patel G., Schulman G.L. (2008). The reorienting system of the human brain: From environment to theory of mind. Neuron, 58, 306–324. doi: 10.1016/j.neuron.2008.04.017 18466742

10. Greicus M.D., Krasnow B., Reiss A.L., Menon V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. PNAS, 100, 253–258. doi: 10.1073/pnas.0135058100 12506194

11. Downar J., Crawley A.P., Mikulis D.J., eDavis K.D. (2002). A cortical network sensitive to stimulus salience in a neutral behavioral context across multiple sensory modalities. Journal of Neurophysiology, 87, 615–20. doi: 10.1152/jn.00636.2001 11784775

12. Raichle M. (2010). Two views on brain function. Trends in Cognitive Sciences, 14, 180–190. doi: 10.1016/j.tics.2010.01.008 20206576

13. Bressler S.L, & Menon V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290. doi: 10.1016/j.tics.2010.04.004 20493761

14. Power J. D, & Petersen, S.E. (2013). Control-related systems in the human brain. Current Opinion in Neurobiology, 2, 223–228.

15. Lee M.H., Hacker C.D., Snyder A.Z., Corbetta M., Zhang D., Leuthardt E.C., Shimony J.S. (2012). Clustering of resting state networks, PlosOne, 7, doi: 10.1371/journal.pone.0040370 22792291

16. Fox M.D., Snyder A.Z., Vincent J.L., Corbetta M., Van Essen D.C., Raichle M.E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. PNAS, 102, 9673–9678. doi: 10.1073/pnas.0504136102 15976020

17. Lee M.H. Lee, Smyser C.D., Shimony J.S. (2013). Resting-State fMRI: A Review of Methods and Clinical Applications. American Journal of Neuroradiology, 34, 1866–1872 doi: 10.3174/ajnr.A3263 22936095

18. Cabeza R., and Nyberg L. (2000). Imaging cognition II: An empirical review of 275 PET and fMRI studies. Journal of Cognitive Neuroscience, 12, 1–47.

19. Fransson P (2006) How default is the default mode of brain function? Further evidence from intrinsic BOLD signal fluctuations. Neuropsychologia 44:2836–2845. doi: 10.1016/j.neuropsychologia.2006.06.017 16879844

20. Spreng RN (2012) The fallacy of a "task-negative" network. Frontiers in Psychology, 3:145. doi: 10.3389/fpsyg.2012.00145 22593750

21. Krieger-Redwood K, Jefferies E, Karapanagiotidis T, Seymour R, Nunes A, Ang JW, Majernikova V, Mollo G, Smallwood J (2016) Down but not out in posterior cingulate cortex: Deactivation yet functional coupling with prefrontal cortex during demanding semantic cognition. Neuroimage 141:366–377 doi: 10.1016/j.neuroimage.2016.07.060 27485753

22. Shulman G.L., Fiez J.A., Corbetta M., Buckner R.L., Miezin F.M., Raichle M.E., et al.(1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of Cognitive Neuroscience, 9, 648–663. doi: 10.1162/jocn.1997.9.5.648 23965122

23. Raichle M.E., Macleod A.M., Snyder A.Z., Powers W.J., Gusnard D.A., Shulman G.L. (2001). A default mode of brain function. PNAS, 98, 676–682. doi: 10.1073/pnas.98.2.676 11209064

24. Buckner, Randy L, Andrews‐ Hanna, Jessica R, & Schacter, Daniel L. (2008). The brain's default network. Annals of the New York Academy of Sciences, 1124, 1–38. doi: 10.1196/annals.1440.011 18400922

25. Buckner R.L. (2012). The serendipitous discovery of the brain's default network. Neuroimage, 62, 1137–1145. doi: 10.1016/j.neuroimage.2011.10.035 22037421

26. Mitra A., Raichle M.E. (2018). Principles of cross-network communication in human resting state fMRI. Scandinavian Journal of Psychology, 59, 83–90. doi: 10.1111/sjop.12422 29356003

27. Mak L. E., Minuzzi L., MacQueen G., Hall G., Kennedy S.H., Milev R. (2017). The default mode network in healthy individuals: A systematic review and meta-analysis. Brain Connect, 7, https://doi.org/10.1089/brain.2016.0438

28. Gao W., & Lin W. (2012). Frontal parietal control network regulates the anti-correlated default and dorsal attention networks. Human Brain Mapping, 33, 192–202. doi: 10.1002/hbm.21204 21391263

29. Mitra A. & Raichle ME., (2016). How networks communicate: Propagation patterns in spontaneous brain activity. Philosophical Transactions of the Royal Society, London B, 371, doi: 10.1098/rstb.2015.0546 27574315

30. Lustig C., Snyder A.Z., Bhakta M., O’Brien K.C., McAvoy M., Raichle M.E. (2003). Functional deactivations: Change with age and dementia of the Alzheimer type, PNAS, 100, 14504–14509. doi: 10.1073/pnas.2235925100 14608034

31. Ramot M., Fisch L., Harel M., Kipervasser S., Andelman F., Neufeld M.Y., Kramer U., Fried I., and Malach R. (2012). A widely distributed spectral signature of task-negative electrocorticography responses revealed during a visuomotor task in the human cortex. Journal of Neuroscience, 32, 10458–10469. doi: 10.1523/JNEUROSCI.0877-12.2012 22855795

32. Sridharan D., Levitin D.J., and Menon V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. PNAS, 105, 12569–12574. doi: 10.1073/pnas.0800005105 18723676

33. Posner M. I., & Raichle M. E. (1997). Images of mind. New York: Freeman & co.

34. Mittner M., Boekel W., Tucker A.M., Turner B.M., Heathcote A., Forstmann B.U. (2014). When the brain takes a break: A model-based analysis of mind wandering, The Journal of Neuroscience, 34, 16286–16295. doi: 10.1523/JNEUROSCI.2062-14.2014 25471568

35. Kompus K, Specht K, Ersland L, Juvodden HT, van Wageningen H, Hugdahl K, Westerhausen R. (2012). A forced-attention dichotic listening fMRI study on 113 subjects. Brain and Language, 121, 240–247. doi: 10.1016/j.bandl.2012.03.004 22494771

36. Hugdahl K., & Andersson L. (l986). The "forced-attention paradigm" in dichotic listening to CV-syllables: A comparison between adults and children. Cortex, 22, 4l7–432.

37. Asbjørnsen A., & Hugdahl K. (1995). Attentional effects in dichotic listening. Brain and Language, 49, 189–201. doi: 10.1006/brln.1995.1029 7640962

38. Hugdahl K., Westerhausen R., Alho K., Medvedev S., Laine M, & Hämäläinen. H. (2009). Attention and cognitive control: Unfolding the dichotic listening story. Scandinavian Journal of Psychology, 50, 11–22. doi: 10.1111/j.1467-9450.2008.00676.x 18705670

39. Obrzut J.E., Mondor T.A., Uecker A. (1993).The influence of attention on the dichotic REA with normal and learning disabled children. Neuropsychologia, 31, 1411–1416. doi: 10.1016/0028-3932(93)90107-b 8127436

40. Gadea M., Gomez C., Espert R. (2000). Test-retest performance for the consonant-vowel dichotic listening test with and without attentional manipulations. Journal of Clinical and Experimental Neuropsychology, 22, 793–803. doi: 10.1076/jcen.22.6.793.959 11320437

41. Helland T., & Asbjørnsen A. (2000). Executive functions in dyslexia. Child Neuropsychology, 6, 37–46. doi: 10.1076/0929-7049(200003)6:1;1-B;FT037 10980667

42. Tallus J., Soveri A., Hämäläinen H., Tuomainen J., Laine M. (2015). Effects of auditory attention training with the dichotic listening task: Behavioural and neurophysiological evidence, PlosOne, 6, 10, e0139318. doi: 10.1371/journal.pone.0139318 eCollection. 26439112

43. Kershner J.R. (2016). Forced-attention dichotic listening with university students with dyslexia: Search for a core deficit. Journal of Learning Disabilities 2016, Vol. 49(3) 282–292

44. O'Leary D., Andreasen N. C., Hurtig R. R., Hichwa R. D., Watkins L., Boles Ponto L. L., Rogers M., & Kirchner P. T. (1997). A positron emission tomography study of binaurally and dichotically presented stimuli: Effects of level of language and directed attention. Brain and Language, 53, 20–39.

45. Hugdahl K., Law I., Kyllingsbæk S., Brønnick K., Gade A., & Paulson O. B. (2000). Effects of attention on dichotic listening: An 15O-PET study. Human Brain Mapping, 10, 87–97. 10864233

46. Westerhausen R., Moosmann M., Alho K., Belsby S.O., Hämäläinen H., Medvedev S., Specht K., & Hugdahl K. (2010). Identification of attention and cognitive control networks in a parametric auditory fMRI study. Neuropsychologia, 48, 2075–2081 doi: 10.1016/j.neuropsychologia.2010.03.028 20363236

47. Shankweiler D., & Studdert-Kennedy M. (1967). Identification of consonants and vowels presented to left and right ears. Quarterly Journal of Experimental Psychology, 19, 59–63. doi: 10.1080/14640746708400069 6041684

48. Hugdahl K. (2011). Fifty years of dichotic listening research—Still going and going and going…. Brain and Cognition, 76, 211–214. doi: 10.1016/j.bandc.2011.03.006 21470754

49. Van den Noort M., Specht K., Rimol LM, Ersland L., & Hugdahl K. (2008) A new verbal reports fMRI dichotic listening paradigm for studies of hemispheric asymmetry. Neuroimage, 40, 902–911. doi: 10.1016/j.neuroimage.2007.11.051 18234509

50. Bryden M.P., Munhall K., & Allard F. (1983). Attentional biases and the right-ear effect in dichotic listening. Brain and Language, 18, 236–248. 6839141

51. Hommet C, Mondon K., Berrut G, Goyer Y., Isingrini M., Constans T, Beizung C. (2011). Central auditory processing in aging: the dichotic listening paradigm. Journal of Nutrition in Health and Aging, 14, 751–756.

52. Gadea M., Alino M., Garijo E., Espert R, Salvador A, (2016). Testing the benefits of neurofeedback on selective attention measured through dichotic lustening, Applied Psychophysiology and Biofeedback, 41, 157–164. doi: 10.1007/s10484-015-9323-8 26683198

53. Logothetis N.L., Pauls J., Augath M., Trinath T., Oeltermann A. (2001). A default mode of brain function. Nature, 412, 150–157 doi: 10.1038/35084005 11449264

54. Popa D., Popescu A.T., Pare D. (2009). Contrasting activity profile of two distributed cortical networks as a function of attentional demands. Journal of Neuroscience 29, 1191–1201. doi: 10.1523/JNEUROSCI.4867-08.2009 19176827

55. Sormaz M., Murphy C., Wang H-t., Hymers M., Karapanagiotidis T., Poerio G., Margulies D.S., Jefferies E., Smallwood J. (2018). Default mode network can support the level of detail in experience during active task states, PNAS, 115, 9318–9323. doi: 10.1073/pnas.1721259115 30150393

56. Fedorenko E., Behr M. K., Kanwisher N. (2011). Functional specificity for high-level linguistic processing in the human brain. PNAS, 108, 16428–16433. doi: 10.1073/pnas.1112937108 21885736

57. Bush G., Shin L.M. (2006). The multi-source interference task: An fMRI task that reliably activates the cingulo-frontal-parietal cognitive/attention network. nature Protocols, 1, 308–313. doi: 10.1038/nprot.2006.48 17406250

58. Posner M.I. & Rothbart M.K. (2007). Research on attention networks as a model for the integration of psychological science. Annual Review Psychology, 58, 1–23

59. Sauseng P., Klimesch W., Stadler W., Schabus M., Doppelmayr M., Hanslmayr S., Gruber W.R., Birbaumer N. (2005). A shift of visual spatial attention is selectively associated with human EEG alpha activity. European Journal of Neuroscience, 22, 2917–2926. doi: 10.1111/j.1460-9568.2005.04482.x 16324126

60. Sauseng P., Klimesch W.e, Freunberger R, Pecherstorfer T.e, Hanslmayr S., Doppelmayre M. (2006). Relevance of EEG alpha and theta oscillations during task switching. Experimental Brain Research, 170, 295–301. doi: 10.1007/s00221-005-0211-y 16317574

61. Bandettini P. A. (1999). The temporal resolution of Functional MRI. In C. T. W. Moonen & P. A. Bandettini (Eds.), Functional MRI. Berlin: Springer Verlag.

62. Callard F., Smallwood J., and Margulies D.S. (2012). Default positions: How neuroscience's historical legacy has hampered investigation of the resting Mind. Frontiers in Psychology 3, 321. doi: 10.3389/fpsyg.2012.00321 22973252

63. Margulies D.S. Ghosh S.S., Goulas A., Falkiewicz F., Huntenburg J.M., Langs G., Bezgin G., Eickhoff S.B., Castellanos F.X., Petrides M., Jefferies E., Smallwood J.(2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. PNAS, 113, 12574–12579 doi: 10.1073/pnas.1608282113 27791099

64. Margulies D.S., and Smallwood J. (2017). Converging evidence for the role of transmodal cortex in cognition. PNAS, 114, 12641–12643. doi: 10.1073/pnas.1717374114 29142008

65. Vatansever D, Menon DK, Stamatakis EA (2017a) Default mode contributions to automated information processing. PNAS, 114,12821–12826 doi: 10.1073/pnas.1710521114 29078345

66. Provost JS, Monchi O (2015). Exploration of the dynamics between brain regions associated with the default-mode network and frontostriatal pathway with regards to task familiarity. The European Journal of Neuroscience, 41, 835–844. doi: 10.1111/ejn.12821 25620606

67. Sorg C., Riedl V., Mühlau M., Calhoun V.D., Eichele T., Drzezga A., Förstl H., Kurz A., Zimmer C., Wohlschläger A.M. (2007). Selective changes of resting-state networks in indivisduals at risk for Alzheimer's disease. PNAS, 104, 18760–18765. doi: 10.1073/pnas.0708803104 18003904

68. Northoff G., & Qin P. (2011). How can the brain's resting state activity generate hallucinations? A 'resting state hypothesis' of auditory verbal hallucinations. Schizophrenia Research, 127, 202–214. doi: 10.1016/j.schres.2010.11.009 21146961

69. Nygård M., Eichele T, Løberg E-M., Jørgensen H, Johnsen E, Kroken R, Berle Ø, Hugdahl K. (2012). Patients with schizophrenia fail to up-regulate task-positive and down-regulate task-negative brain networks: An fMRI study using an ICA analysis approach. Frontiers in Human Neuroscience, doi: 103389.fnhum.2012.00149

70. Allen P., Sommer I.E., Jardri R., Eysenck M.W., Hugdahl K. (2019). Extrinsic and default mode networks in psychiatric conditions: Relationship to excitatory-inhibitory transmitter balance and early trauma. Neuroscience and Biobehavioral Reviews, 99, 90–100. doi: 10.1016/j.neubiorev.2019.02.004 30769024

71. Van Dijk K. R., Hedden T., Venkataraman A., Evans K. C., Lazar S. W., & Buckner R. L. (2009). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology, 103, 297–321. doi: 10.1152/jn.00783.2009 19889849

72. Cole D. M., Smith S. M., & Beckmann C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state fMRI data. Frontiers in Systems Neuroscience, 4,8.

73. Di X & Biswal B.B. (2014). Modulatory interactions between the default mode network and task positive networks in resting state. PeerJ, doi: 10.7717/peerj.367 24860698


Článek vyšel v časopise

PLOS One


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Léčba bolesti v ordinaci praktického lékaře
nový kurz
Autoři: MUDr. PhDr. Zdeňka Nováková, Ph.D.

Revmatoidní artritida: včas a k cíli
Autoři: MUDr. Heřman Mann

Jistoty a nástrahy antikoagulační léčby aneb kardiolog - neurolog - farmakolog - nefrolog - právník diskutují
Autoři: doc. MUDr. Štěpán Havránek, Ph.D., prof. MUDr. Roman Herzig, Ph.D., doc. MUDr. Karel Urbánek, Ph.D., prim. MUDr. Jan Vachek, MUDr. et Mgr. Jolana Těšínová, Ph.D.

Léčba akutní pooperační bolesti
Autoři: doc. MUDr. Jiří Málek, CSc.

Nové antipsychotikum kariprazin v léčbě schizofrenie
Autoři: prof. MUDr. Cyril Höschl, DrSc., FRCPsych.

Všechny kurzy