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Müller VI, Cieslik EC, Ficco L, Tyralla S, Sepehry AA, Aziz-Safaie T, Feng C, Eickhoff SB, Langner R. Not All Stroop-Type Tasks Are Alike: Assessing the Impact of Stimulus Material, Task Design, and Cognitive Demand via Meta-analyses Across Neuroimaging Studies. Neuropsychol Rev 2024:10.1007/s11065-024-09647-1. [PMID: 39264479 DOI: 10.1007/s11065-024-09647-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/29/2024] [Indexed: 09/13/2024]
Abstract
The Stroop effect is one of the most often studied examples of cognitive conflict processing. Over time, many variants of the classic Stroop task were used, including versions with different stimulus material, control conditions, presentation design, and combinations with additional cognitive demands. The neural and behavioral impact of this experimental variety, however, has never been systematically assessed. We used activation likelihood meta-analysis to summarize neuroimaging findings with Stroop-type tasks and to investigate whether involvement of the multiple-demand network (anterior insula, lateral frontal cortex, intraparietal sulcus, superior/inferior parietal lobules, midcingulate cortex, and pre-supplementary motor area) can be attributed to resolving some higher-order conflict that all of the tasks have in common, or if aspects that vary between task versions lead to specialization within this network. Across 133 neuroimaging experiments, incongruence processing in the color-word Stroop variant consistently recruited regions of the multiple-demand network, with modulation of spatial convergence by task variants. In addition, the neural patterns related to solving Stroop-like interference differed between versions of the task that use different stimulus material, with the only overlap between color-word, emotional picture-word, and other types of stimulus material in the posterior medial frontal cortex and right anterior insula. Follow-up analyses on behavior reported in these studies (in total 164 effect sizes) revealed only little impact of task variations on the mean effect size of reaction time. These results suggest qualitative processing differences among the family of Stroop variants, despite similar task difficulty levels, and should carefully be considered when planning or interpreting Stroop-type neuroimaging experiments.
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Aziz-Safaie T, Müller VI, Langner R, Eickhoff SB, Cieslik EC. The effect of task complexity on the neural network for response inhibition: An ALE meta-analysis. Neurosci Biobehav Rev 2024; 158:105544. [PMID: 38220034 PMCID: PMC11130604 DOI: 10.1016/j.neubiorev.2024.105544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 12/18/2023] [Accepted: 01/09/2024] [Indexed: 01/16/2024]
Abstract
Response inhibition is classically investigated using the go/no-go (GNGT) and stop-signal task (SST), which conceptually measure different subprocesses of inhibition. Further, different task versions with varying levels of additional executive control demands exist, making it difficult to identify the core neural correlates of response inhibition independent of variations in task complexity. Using neuroimaging meta-analyses, we show that a divergent pattern of regions is consistently involved in the GNGT versus SST, arguing for different mechanisms involved when performing the two tasks. Further, for the GNGT a strong effect of task complexity was found, with regions of the multiple demand network (MDN) consistently involved particularly in the complex GNGT. In contrast, both standard and complex SST recruited the MDN to a similar degree. These results complement behavioral evidence suggesting that inhibitory control becomes automatic after some practice and is performed without input of higher control regions in the classic, standard GNGT, but continues to be implemented in a top-down controlled fashion in the SST.
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Cieslik EC, Ullsperger M, Gell M, Eickhoff SB, Langner R. Success versus failure in cognitive control: Meta-analytic evidence from neuroimaging studies on error processing. Neurosci Biobehav Rev 2024; 156:105468. [PMID: 37979735 PMCID: PMC10976187 DOI: 10.1016/j.neubiorev.2023.105468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/23/2023] [Accepted: 11/13/2023] [Indexed: 11/20/2023]
Abstract
Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.
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Heckner MK, Cieslik EC, Paas Oliveros LK, Eickhoff SB, Patil KR, Langner R. Predicting executive functioning from brain networks: modality specificity and age effects. Cereb Cortex 2023; 33:10997-11009. [PMID: 37782935 PMCID: PMC10646699 DOI: 10.1093/cercor/bhad338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/25/2023] [Accepted: 08/26/2023] [Indexed: 10/04/2023] Open
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Boeken OJ, Cieslik EC, Langner R, Markett S. Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding. Brain Struct Funct 2023; 228:1811-1834. [PMID: 36547707 PMCID: PMC10516793 DOI: 10.1007/s00429-022-02603-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.
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Paas Oliveros LK, Cieslik EC, Pieczykolan A, Pläschke RN, Eickhoff SB, Langner R. Brain functional characterization of response-code conflict in dual-tasking and its modulation by age. Cereb Cortex 2023; 33:10155-10180. [PMID: 37540164 PMCID: PMC10502578 DOI: 10.1093/cercor/bhad273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 08/05/2023] Open
Abstract
Crosstalk between conflicting response codes contributes to interference in dual-tasking, an effect exacerbated in advanced age. Here, we investigated (i) brain activity correlates of such response-code conflicts, (ii) activity modulations by individual dual-task performance and related cognitive abilities, (iii) task-modulated connectivity within the task network, and (iv) age-related differences in all these aspects. Young and older adults underwent fMRI while responding to the pitch of tones through spatially mapped speeded button presses with one or two hands concurrently. Using opposing stimulus-response mappings between hands, we induced conflict between simultaneously activated response codes. These response-code conflicts elicited activation in key regions of the multiple-demand network. While thalamic and parietal areas of the conflict-related network were modulated by attentional, working-memory and task-switching abilities, efficient conflict resolution in dual-tasking mainly relied on increasing supplementary motor activity. Older adults showed non-compensatory hyperactivity in left superior frontal gyrus, and higher right premotor activity was modulated by working-memory capacity. Finally, connectivity between premotor or parietal seed regions and the conflict-sensitive network was neither conflict-specific nor age-sensitive. Overall, resolving dual-task response-code conflict recruited substantial parts of the multiple-demand network, whose activity and coupling, however, were only little affected by individual differences in task performance or age.
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Heckner MK, Cieslik EC, Oliveros LKP, Eickhoff SB, Patil KR, Langner R. Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.29.547036. [PMID: 37425780 PMCID: PMC10327061 DOI: 10.1101/2023.06.29.547036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.
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Heckner MK, Cieslik EC, Patil KR, Gell M, Eickhoff SB, Hoffstädter F, Langner R. Predicting executive functioning from functional brain connectivity: network specificity and age effects. Cereb Cortex 2023; 33:6495-6507. [PMID: 36635227 PMCID: PMC10233269 DOI: 10.1093/cercor/bhac520] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 01/14/2023] Open
Abstract
Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.
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Cieslik EC, Ullsperger M, Gell M, Eickhoff SB, Langner R. Success versus failure in cognitive control: meta-analytic evidence from neuroimaging studies on error processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.10.540136. [PMID: 37214978 PMCID: PMC10197606 DOI: 10.1101/2023.05.10.540136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain mechanisms of error processing have often been investigated using response interference tasks and focusing on the posterior medial frontal cortex, which is also implicated in resolving response conflict in general. Thereby, the role other brain regions may play has remained undervalued. Here, activation likelihood estimation meta-analyses were used to synthesize the neuroimaging literature on brain activity related to committing errors versus responding successfully in interference tasks and to test for commonalities and differences. The salience network and the temporoparietal junction were commonly recruited irrespective of whether responses were correct or incorrect, pointing towards a general involvement in coping with situations that call for increased cognitive control. The dorsal posterior cingulate cortex, posterior thalamus, and left superior frontal gyrus showed error-specific convergence, which underscores their consistent involvement when performance goals are not met. In contrast, successful responding revealed stronger convergence in the dorsal attention network and lateral prefrontal regions. Underrecruiting these regions in error trials may reflect failures in activating the task-appropriate stimulus-response contingencies necessary for successful response execution.
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Frahm L, Cieslik EC, Hoffstaedter F, Satterthwaite TD, Fox PT, Langner R, Eickhoff SB. Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations. Hum Brain Mapp 2022; 43:3987-3997. [PMID: 35535616 PMCID: PMC9374884 DOI: 10.1002/hbm.25898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/14/2022] [Accepted: 04/19/2022] [Indexed: 11/17/2022] Open
Abstract
In recent neuroimaging studies, threshold‐free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster‐level family‐wise error (cFWE) and it does not require setting a cluster‐forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate‐based neuroimaging meta‐analysis, Activation Likelihood Estimation (ALE), by means of large‐scale simulations. We created over 200,000 artificial meta‐analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel‐level and cluster‐level FWE correction approaches. All three multiple‐comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel‐level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.
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Heckner MK, Cieslik EC, Eickhoff SB, Camilleri JA, Hoffstaedter F, Langner R. The Aging Brain and Executive Functions Revisited: Implications from Meta-analytic and Functional-Connectivity Evidence. J Cogn Neurosci 2021; 33:1716-1752. [PMID: 32762523 DOI: 10.1162/jocn_a_01616] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Healthy aging is associated with changes in cognitive performance, including executive functions (EFs) and their associated brain activation patterns. However, it has remained unclear which EF-related brain regions are affected consistently, because the results of pertinent neuroimaging studies and earlier meta-analyses vary considerably. We, therefore, conducted new rigorous meta-analyses of published age differences in EF-related brain activity. Out of a larger set of regions associated with EFs, only left inferior frontal junction and left anterior cuneus/precuneus were found to show consistent age differences. To further characterize these two age-sensitive regions, we performed seed-based resting-state functional connectivity (RS-FC) analyses using fMRI data from a large adult sample with a wide age range. We also assessed associations of the two regions' whole-brain RS-FC patterns with age and EF performance. Although our results largely point toward a domain-general role of left inferior frontal junction in EFs, the pattern of individual study contributions to the meta-analytic results suggests process-specific modulations by age. Our analyses further indicate that the left anterior cuneus/precuneus is recruited differently by older (compared with younger) adults during EF tasks, potentially reflecting inefficiencies in switching the attentional focus. Overall, our findings question earlier meta-analytic results and suggest a larger heterogeneity of age-related differences in brain activity associated with EFs. Hence, they encourage future research that pays greater attention to replicability, investigates age-related differences in deactivation, and focuses on more narrowly defined EF subprocesses, combining multiple behavioral assessments with multimodal imaging.
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Heckner MK, Cieslik EC, Küppers V, Fox PT, Eickhoff SB, Langner R. Delineating visual, auditory and motor regions in the human brain with functional neuroimaging: a BrainMap-based meta-analytic synthesis. Sci Rep 2021; 11:9942. [PMID: 33976234 PMCID: PMC8113600 DOI: 10.1038/s41598-021-88773-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 04/13/2021] [Indexed: 02/03/2023] Open
Abstract
Most everyday behaviors and laboratory tasks rely on visual, auditory and/or motor-related processes. Yet, to date, there has been no large-scale quantitative synthesis of functional neuroimaging studies mapping the brain regions consistently recruited during such perceptuo-motor processing. We therefore performed three coordinate-based meta-analyses, sampling the results of neuroimaging experiments on visual (n = 114), auditory (n = 122), or motor-related (n = 251) processing, respectively, from the BrainMap database. Our analyses yielded both regions known to be recruited for basic perceptual or motor processes and additional regions in posterior frontal cortex. Comparing our results with data-driven network definitions based on resting-state functional connectivity revealed good overlap in expected regions but also showed that perceptual and motor task-related activations consistently involve additional frontal, cerebellar, and subcortical areas associated with "higher-order" cognitive functions, extending beyond what is captured when the brain is at "rest." Our resulting sets of domain-typical brain regions can be used by the neuroimaging community as robust functional definitions or masks of regions of interest when investigating brain correlates of perceptual or motor processes and their interplay with other mental functions such as cognitive control or affective processing. The maps are made publicly available via the ANIMA database.
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Chen J, Wensing T, Hoffstaedter F, Cieslik EC, Müller VI, Patil KR, Aleman A, Derntl B, Gruber O, Jardri R, Kogler L, Sommer IE, Eickhoff SB, Nickl-Jockschat T. Neurobiological substrates of the positive formal thought disorder in schizophrenia revealed by seed connectome-based predictive modeling. NEUROIMAGE-CLINICAL 2021; 30:102666. [PMID: 34215141 PMCID: PMC8105296 DOI: 10.1016/j.nicl.2021.102666] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 04/01/2021] [Accepted: 04/03/2021] [Indexed: 12/14/2022]
Abstract
Formal thought disorder (FTD) is a core symptom of schizophrenia, but its neurobiological substrates remain elusive. Resting-state functional connectivity (rsFC) of three meta-analytically defined seeds were correlated to positive and negative symptom dimensions of FTD. RsFC patterns allowed individual prediction of positive FTD symptom severity. These findings generalized to an independent data set. Our study has identified robust neurobiological correlates of positive FTD in schizophrenia. Formal thought disorder (FTD) is a core symptom cluster of schizophrenia, but its neurobiological substrates remain poorly understood. Here we collected resting-state fMRI data from 276 subjects at seven sites and employed machine-learning to investigate the neurobiological correlates of FTD along positive and negative symptom dimensions in schizophrenia. Three a priori, meta-analytically defined FTD-related brain regions were used as seeds to generate whole-brain resting-state functional connectivity (rsFC) maps, which were then compared between schizophrenia patients and controls. A repeated cross-validation procedure was realized within the patient group to identify clusters whose rsFC patterns to the seeds were repeatedly observed as significantly associated with specific FTD dimensions. These repeatedly identified clusters (i.e., robust clusters) were functionally characterized and the rsFC patterns were used for predictive modeling to investigate predictive capacities for individual FTD dimensional-scores. Compared with controls, differential rsFC was found in patients in fronto-temporo-thalamic regions. Our cross-validation procedure revealed significant clusters only when assessing the seed-to-whole-brain rsFC patterns associated with positive-FTD. RsFC patterns of three fronto-temporal clusters, associated with higher-order cognitive processes (e.g., executive functions), specifically predicted individual positive-FTD scores (p = 0.005), but not other positive symptoms, and the PANSS general psychopathology subscale (p > 0.05). The prediction of positive-FTD was moreover generalized to an independent dataset (p = 0.013). Our study has identified neurobiological correlates of positive FTD in schizophrenia in a network associated with higher-order cognitive functions, suggesting a dysexecutive contribution to FTD in schizophrenia. We regard our findings as robust, as they allow a prediction of individual-level symptom severity.
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Pläschke RN, Patil KR, Cieslik EC, Nostro AD, Varikuti DP, Plachti A, Lösche P, Hoffstaedter F, Kalenscher T, Langner R, Eickhoff SB. Age differences in predicting working memory performance from network-based functional connectivity. Cortex 2020; 132:441-459. [PMID: 33065515 PMCID: PMC7778730 DOI: 10.1016/j.cortex.2020.08.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 06/27/2020] [Accepted: 08/23/2020] [Indexed: 01/14/2023]
Abstract
Deterioration in working memory capacity (WMC) has been associated with normal aging, but it remains unknown how age affects the relationship between WMC and connectivity within functional brain networks. We therefore examined the predictability of WMC from fMRI-based resting-state functional connectivity (RSFC) within eight meta-analytically defined functional brain networks and the connectome in young and old adults using relevance vector machine in a robust cross-validation scheme. Particular brain networks have been associated with mental functions linked to WMC to a varying degree and are associated with age-related differences in performance. Comparing prediction performance between the young and old sample revealed age-specific effects: In young adults, we found a general unpredictability of WMC from RSFC in networks subserving WM, cognitive action control, vigilant attention, theory-of-mind cognition, and semantic memory, whereas in older adults each network significantly predicted WMC. Moreover, both WM-related and WM-unrelated networks were differently predictive in older adults with low versus high WMC. These results indicate that the within-network functional coupling during task-free states is specifically related to individual task performance in advanced age, suggesting neural-level reorganization. In particular, our findings support the notion of a decreased segregation of functional brain networks, deterioration of network integrity within different networks and/or compensation by reorganization as factors driving associations between individual WMC and within-network RSFC in older adults. Thus, using multivariate pattern regression provided novel insights into age-related brain reorganization by linking cognitive capacity to brain network integrity.
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Botvinik-Nezer R, Holzmeister F, Camerer CF, Dreber A, Huber J, Johannesson M, Kirchler M, Iwanir R, Mumford JA, Adcock RA, Avesani P, Baczkowski BM, Bajracharya A, Bakst L, Ball S, Barilari M, Bault N, Beaton D, Beitner J, Benoit RG, Berkers RMWJ, Bhanji JP, Biswal BB, Bobadilla-Suarez S, Bortolini T, Bottenhorn KL, Bowring A, Braem S, Brooks HR, Brudner EG, Calderon CB, Camilleri JA, Castrellon JJ, Cecchetti L, Cieslik EC, Cole ZJ, Collignon O, Cox RW, Cunningham WA, Czoschke S, Dadi K, Davis CP, Luca AD, Delgado MR, Demetriou L, Dennison JB, Di X, Dickie EW, Dobryakova E, Donnat CL, Dukart J, Duncan NW, Durnez J, Eed A, Eickhoff SB, Erhart A, Fontanesi L, Fricke GM, Fu S, Galván A, Gau R, Genon S, Glatard T, Glerean E, Goeman JJ, Golowin SAE, González-García C, Gorgolewski KJ, Grady CL, Green MA, Guassi Moreira JF, Guest O, Hakimi S, Hamilton JP, Hancock R, Handjaras G, Harry BB, Hawco C, Herholz P, Herman G, Heunis S, Hoffstaedter F, Hogeveen J, Holmes S, Hu CP, Huettel SA, Hughes ME, Iacovella V, Iordan AD, Isager PM, Isik AI, Jahn A, Johnson MR, Johnstone T, Joseph MJE, Juliano AC, Kable JW, Kassinopoulos M, Koba C, Kong XZ, Koscik TR, Kucukboyaci NE, Kuhl BA, Kupek S, Laird AR, Lamm C, Langner R, Lauharatanahirun N, Lee H, Lee S, Leemans A, Leo A, Lesage E, Li F, Li MYC, Lim PC, Lintz EN, Liphardt SW, Losecaat Vermeer AB, Love BC, Mack ML, Malpica N, Marins T, Maumet C, McDonald K, McGuire JT, Melero H, Méndez Leal AS, Meyer B, Meyer KN, Mihai G, Mitsis GD, Moll J, Nielson DM, Nilsonne G, Notter MP, Olivetti E, Onicas AI, Papale P, Patil KR, Peelle JE, Pérez A, Pischedda D, Poline JB, Prystauka Y, Ray S, Reuter-Lorenz PA, Reynolds RC, Ricciardi E, Rieck JR, Rodriguez-Thompson AM, Romyn A, Salo T, Samanez-Larkin GR, Sanz-Morales E, Schlichting ML, Schultz DH, Shen Q, Sheridan MA, Silvers JA, Skagerlund K, Smith A, Smith DV, Sokol-Hessner P, Steinkamp SR, Tashjian SM, Thirion B, Thorp JN, Tinghög G, Tisdall L, Tompson SH, Toro-Serey C, Torre Tresols JJ, Tozzi L, Truong V, Turella L, van 't Veer AE, Verguts T, Vettel JM, Vijayarajah S, Vo K, Wall MB, Weeda WD, Weis S, White DJ, Wisniewski D, Xifra-Porxas A, Yearling EA, Yoon S, Yuan R, Yuen KSL, Zhang L, Zhang X, Zosky JE, Nichols TE, Poldrack RA, Schonberg T. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 2020; 582:84-88. [PMID: 32483374 PMCID: PMC7771346 DOI: 10.1038/s41586-020-2314-9] [Citation(s) in RCA: 503] [Impact Index Per Article: 100.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 04/07/2020] [Indexed: 01/13/2023]
Abstract
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset2-5. Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.
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Pläschke RN, Cieslik EC, Müller VI, Hoffstaedter F, Plachti A, Varikuti DP, Goosses M, Latz A, Caspers S, Jockwitz C, Moebus S, Gruber O, Eickhoff CR, Reetz K, Heller J, Südmeyer M, Mathys C, Caspers J, Grefkes C, Kalenscher T, Langner R, Eickhoff SB. Corrigendum to "On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.". Hum Brain Mapp 2018; 39:4633-4635. [PMID: 30304584 DOI: 10.1002/hbm.24406] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Chechko N, Cieslik EC, Müller VI, Nickl-Jockschat T, Derntl B, Kogler L, Aleman A, Jardri R, Sommer IE, Gruber O, Eickhoff SB. Differential Resting-State Connectivity Patterns of the Right Anterior and Posterior Dorsolateral Prefrontal Cortices (DLPFC) in Schizophrenia. Front Psychiatry 2018; 9:211. [PMID: 29892234 PMCID: PMC5985714 DOI: 10.3389/fpsyt.2018.00211] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 05/03/2018] [Indexed: 01/24/2023] Open
Abstract
In schizophrenia (SCZ), dysfunction of the dorsolateral prefrontal cortex (DLPFC) has been linked to the deficits in executive functions and attention. It has been suggested that, instead of considering the right DLPFC as a cohesive functional entity, it can be divided into two parts (anterior and posterior) based on its whole-brain connectivity patterns. Given these two subregions' differential association with cognitive processes, we investigated the functional connectivity (FC) profile of both subregions through resting-state data to determine whether they are differentially affected in SCZ. Resting-state magnetic resonance imaging (MRI) scans were obtained from 120 patients and 172 healthy controls (HC) at 6 different MRI sites. The results showed differential FC patterns for the anterior and posterior parts of the right executive control-related DLPFC in SCZ with the parietal, the temporal and the cerebellar regions, along with a convergent reduction of connectivity with the striatum and the occipital cortex. An increased psychopathology level was linked to a higher difference in posterior vs. anterior FC for the left IFG/anterior insula, regions involved in higher-order cognitive processes. In sum, the current analysis demonstrated that even between two neighboring clusters connectivity could be differentially disrupted in SCZ. Lacking the necessary anatomical specificity, such notions may in fact be detrimental to a proper understanding of SCZ pathophysiology.
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Müller VI, Cieslik EC, Laird AR, Fox PT, Radua J, Mataix-Cols D, Tench CR, Yarkoni T, Nichols TE, Turkeltaub PE, Wager TD, Eickhoff SB. Ten simple rules for neuroimaging meta-analysis. Neurosci Biobehav Rev 2018; 84:151-161. [PMID: 29180258 PMCID: PMC5918306 DOI: 10.1016/j.neubiorev.2017.11.012] [Citation(s) in RCA: 521] [Impact Index Per Article: 74.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Revised: 11/18/2017] [Accepted: 11/20/2017] [Indexed: 01/15/2023]
Abstract
Neuroimaging has evolved into a widely used method to investigate the functional neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders, yielding a literature of more than 30,000 papers. With such an explosion of data, it is increasingly difficult to sift through the literature and distinguish spurious from replicable findings. Furthermore, due to the large number of studies, it is challenging to keep track of the wealth of findings. A variety of meta-analytical methods (coordinate-based and image-based) have been developed to help summarise and integrate the vast amount of data arising from neuroimaging studies. However, the field lacks specific guidelines for the conduct of such meta-analyses. Based on our combined experience, we propose best-practice recommendations that researchers from multiple disciplines may find helpful. In addition, we provide specific guidelines and a checklist that will hopefully improve the transparency, traceability, replicability and reporting of meta-analytical results of neuroimaging data.
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Pläschke RN, Cieslik EC, Müller VI, Hoffstaedter F, Plachti A, Varikuti DP, Goosses M, Latz A, Caspers S, Jockwitz C, Moebus S, Gruber O, Eickhoff CR, Reetz K, Heller J, Südmeyer M, Mathys C, Caspers J, Grefkes C, Kalenscher T, Langner R, Eickhoff SB. On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification. Hum Brain Mapp 2017; 38:5845-5858. [PMID: 28876500 DOI: 10.1002/hbm.23763] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 07/06/2017] [Accepted: 07/30/2017] [Indexed: 01/10/2023] Open
Abstract
Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.
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Wensing T, Cieslik EC, Müller VI, Hoffstaedter F, Eickhoff SB, Nickl-Jockschat T. Neural correlates of formal thought disorder: An activation likelihood estimation meta-analysis. Hum Brain Mapp 2017; 38:4946-4965. [PMID: 28653797 DOI: 10.1002/hbm.23706] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 05/24/2017] [Accepted: 06/16/2017] [Indexed: 11/10/2022] Open
Abstract
Formal thought disorder (FTD) refers to a psychopathological dimension characterized by disorganized and incoherent speech. Whether symptoms of FTD arise from aberrant processing in language-related regions or more general cognitive networks, however, remains debated. Here, we addressed this question by a quantitative meta-analysis of published functional neuroimaging studies on FTD. The revised Activation Likelihood Estimation (ALE) algorithm was used to test for convergent aberrant activation changes in 18 studies (30 experiments) investigating FTD, of which 17 studies comprised schizophrenia patients and one study healthy subjects administered to S-ketamine. Additionally, we analyzed task-dependent and task-independent (resting-state) functional connectivity (FC) of brain regions showing convergence in activation changes. Subsequent functional characterization was performed for the initial clusters and the delineated connectivity networks by reference to the BrainMap database. Consistent activation changes were found in the left superior temporal gyrus (STG) and two regions within the left posterior middle temporal gyrus (p-MTG), ventrally (vp-MTG) and dorsally (dp-MTG). Functional characterization revealed a prominent functional association of ensuing clusters from our ALE meta-analysis with language and speech processing, as well as auditory perception in STG and with social cognition in dp-MTG. FC analysis identified task-dependent and task-independent networks for all three seed regions, which were mainly related to language and speech processing, but showed additional involvement in higher order cognitive functions. Our findings suggest that FTD is mainly characterized by abnormal activation in brain regions of the left hemisphere that are associated with language and speech processing, but also extend to higher order cognitive functions. Hum Brain Mapp 38:4946-4965, 2017. © 2017 Wiley Periodicals, Inc.
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Caspers J, Mathys C, Hoffstaedter F, Südmeyer M, Cieslik EC, Rubbert C, Hartmann CJ, Eickhoff CR, Reetz K, Grefkes C, Michely J, Turowski B, Schnitzler A, Eickhoff SB. Differential Functional Connectivity Alterations of Two Subdivisions within the Right dlPFC in Parkinson's Disease. Front Hum Neurosci 2017; 11:288. [PMID: 28611616 PMCID: PMC5447710 DOI: 10.3389/fnhum.2017.00288] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Accepted: 05/16/2017] [Indexed: 02/02/2023] Open
Abstract
Patients suffering from Parkinson's disease (PD) often show impairments in executive function (EF) like decision-making and action control. The right dorsolateral prefrontal cortex (dlPFC) has been strongly implicated in EF in healthy subjects and has repeatedly been reported to show alterations related to EF impairment in PD. Recently, two key regions for cognitive action control have been identified within the right dlPFC by co-activation based parcellation. While the posterior region is engaged in rather basal EF like stimulus integration and working memory, the anterior region has a more abstract, supervisory function. To investigate whether these functionally distinct subdivisions of right dlPFC are differentially affected in PD, we analyzed resting-state functional connectivity (FC) in 39 PD patients and 44 age- and gender-matched healthy controls. Patients were examined both after at least 12 h withdrawal of dopaminergic drugs (OFF) and under their regular dopaminergic medication (ON). We found that only the posterior right dlPFC subdivision shows FC alterations in PD, while the anterior part remains unaffected. PD-related decreased FC with posterior right dlPFC was found in the bilateral medial posterior parietal cortex (mPPC) and left dorsal premotor region (PMd) in the OFF state. In the medical ON, FC with left PMd normalized, while decoupling with bilateral mPPC remained. Furthermore, we observed increased FC between posterior right dlPFC and the bilateral dorsomedial prefrontal cortex (dmPFC) in PD in the ON state. Our findings point to differential disturbances of right dlPFC connectivity in PD, which relate to its hierarchical organization of EF processing by stronger affecting the functionally basal posterior aspect than the hierarchically higher anterior part.
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Genon S, Li H, Fan L, Müller VI, Cieslik EC, Hoffstaedter F, Reid AT, Langner R, Grefkes C, Fox PT, Moebus S, Caspers S, Amunts K, Jiang T, Eickhoff SB. The Right Dorsal Premotor Mosaic: Organization, Functions, and Connectivity. Cereb Cortex 2017; 27:2095-2110. [PMID: 26965906 DOI: 10.1093/cercor/bhw065] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The right dorsal premotor cortex (PMd) of humans has been reported to be involved in a broad range of motor and cognitive functions. We explored the basis of this behavioral heterogeneity by performing a connectivity-based parcellation using meta-analytic approach applied to PMd coactivations. We compared our connectivity-based parcellation results with parcellations obtained through resting-state functional connectivity and probabilistic diffusion tractography. Functional connectivity profiles and behavioral decoding of the resulting PMd subregions allowed characterizing their respective behavior profile. These procedures divided the right PMd into 5 distinct subregions that formed a cognitive-motor gradient along a rostro-caudal axis. In particular, we found 1) a rostral subregion functionally connected with prefrontal cortex, which likely supports high-level cognitive processes, such as working memory, 2) a central subregion showing a mixed behavioral profile and functional connectivity to parietal regions of the dorsal attention network, and 3) a caudal subregion closely integrated with the motor system. Additionally, we found 4) a dorsal subregion, preferentially related to hand movements and connected to both cognitive and motor regions, and 5) a ventral subregion, whose functional profile fits the concept of an eye movement-related field. In conclusion, right PMd may be considered as a functional mosaic formed by 5 subregions.
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Müller VI, Cieslik EC, Serbanescu I, Laird AR, Fox PT, Eickhoff SB. Altered Brain Activity in Unipolar Depression Revisited: Meta-analyses of Neuroimaging Studies. JAMA Psychiatry 2017; 74:47-55. [PMID: 27829086 PMCID: PMC5293141 DOI: 10.1001/jamapsychiatry.2016.2783] [Citation(s) in RCA: 192] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
IMPORTANCE During the past 20 years, numerous neuroimaging experiments have investigated aberrant brain activation during cognitive and emotional processing in patients with unipolar depression (UD). The results of those investigations, however, vary considerably; moreover, previous meta-analyses also yielded inconsistent findings. OBJECTIVE To readdress aberrant brain activation in UD as evidenced by neuroimaging experiments on cognitive and/or emotional processing. DATA SOURCES Neuroimaging experiments published from January 1, 1997, to October 1, 2015, were identified by a literature search of PubMed, Web of Science, and Google Scholar using different combinations of the terms fMRI (functional magnetic resonance imaging), PET (positron emission tomography), neural, major depression, depression, major depressive disorder, unipolar depression, dysthymia, emotion, emotional, affective, cognitive, task, memory, working memory, inhibition, control, n-back, and Stroop. STUDY SELECTION Neuroimaging experiments (using fMRI or PET) reporting whole-brain results of group comparisons between adults with UD and healthy control individuals as coordinates in a standard anatomic reference space and using an emotional or/and cognitive challenging task were selected. DATA EXTRACTION AND SYNTHESIS Coordinates reported to show significant activation differences between UD and healthy controls during emotional or cognitive processing were extracted. By using the revised activation likelihood estimation algorithm, different meta-analyses were calculated. MAIN OUTCOMES AND MEASURES Meta-analyses tested for brain regions consistently found to show aberrant brain activation in UD compared with controls. Analyses were calculated across all emotional processing experiments, all cognitive processing experiments, positive emotion processing, negative emotion processing, experiments using emotional face stimuli, experiments with a sex discrimination task, and memory processing. All meta-analyses were calculated across experiments independent of reporting an increase or decrease of activity in major depressive disorder. For meta-analyses with a minimum of 17 experiments available, separate analyses were performed for increases and decreases. RESULTS In total, 57 studies with 99 individual neuroimaging experiments comprising in total 1058 patients were included; 34 of them tested cognitive and 65 emotional processing. Overall analyses across cognitive processing experiments (P > .29) and across emotional processing experiments (P > .47) revealed no significant results. Similarly, no convergence was found in analyses investigating positive (all P > .15), negative (all P > .76), or memory (all P > .48) processes. Analyses that restricted inclusion of confounds (eg, medication, comorbidity, age) did not change the results. CONCLUSIONS AND RELEVANCE Inconsistencies exist across individual experiments investigating aberrant brain activity in UD and replication problems across previous neuroimaging meta-analyses. For individual experiments, these inconsistencies may relate to use of uncorrected inference procedures, differences in experimental design and contrasts, or heterogeneous clinical populations; meta-analytically, differences may be attributable to varying inclusion and exclusion criteria or rather liberal statistical inference approaches.
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Schilbach L, Derntl B, Aleman A, Caspers S, Clos M, Diederen KMJ, Gruber O, Kogler L, Liemburg EJ, Sommer IE, Müller VI, Cieslik EC, Eickhoff SB. Differential Patterns of Dysconnectivity in Mirror Neuron and Mentalizing Networks in Schizophrenia. Schizophr Bull 2016; 42:1135-48. [PMID: 26940699 PMCID: PMC4988733 DOI: 10.1093/schbul/sbw015] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Impairments of social cognition are well documented in patients with schizophrenia (SCZ), but the neural basis remains poorly understood. In light of evidence that suggests that the "mirror neuron system" (MNS) and the "mentalizing network" (MENT) are key substrates of intersubjectivity and joint action, it has been suggested that dysfunction of these neural networks may underlie social difficulties in SCZ patients. Additionally, MNS and MENT might be associated differently with positive vs negative symptoms, given prior social cognitive and symptom associations. We assessed resting state functional connectivity (RSFC) in meta-analytically defined MNS and MENT networks in this patient group. Magnetic resonance imaging (MRI) scans were obtained from 116 patients and 133 age-, gender- and movement-matched healthy controls (HC) at 5 different MRI sites. Network connectivity was analyzed for group differences and correlations with clinical symptoms. Results demonstrated decreased connectivity within the MNS and also the MENT in patients compared to controls. Notably, dysconnectivity of the MNS was related to symptom severity, while no such relationship was observed for the MENT. In sum, these findings demonstrate that differential patterns of dysconnectivity exist in SCZ patients, which may contribute differently to the interpersonal difficulties commonly observed in the disorder.
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Cieslik EC, Seidler I, Laird AR, Fox PT, Eickhoff SB. Different involvement of subregions within dorsal premotor and medial frontal cortex for pro- and antisaccades. Neurosci Biobehav Rev 2016; 68:256-269. [PMID: 27211526 DOI: 10.1016/j.neubiorev.2016.05.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 04/01/2016] [Accepted: 05/17/2016] [Indexed: 01/22/2023]
Abstract
The antisaccade task has been widely used to investigate cognitive action control. While the general network for saccadic eye movements is well defined, the exact location of eye fields within the frontal cortex strongly varies between studies. It is unknown whether this inconsistency reflects spatial uncertainty or is the result of different involvement of subregions for specific aspects of eye movement control. The aim of the present study was to examine functional differentiations within the frontal cortex by integrating results from neuroimaging studies analyzing pro- and antisaccade behavior using meta-analyses. The results provide evidence for a differential functional specialization of neighboring oculomotor frontal regions, with lateral frontal eye fields (FEF) and supplementary eye field (SEF) more often involved in prosaccades while medial FEF and anterior midcingulate cortex (aMCC) revealed consistent stronger involvement for antisaccades. This dissociation was furthermore mirrored by functional connectivity analyses showing that the lateral FEF and SEF are embedded in a motor output network, while medial FEF and aMCC are integrated in a multiple demand network.
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