diff --git a/content/intro.md b/content/intro.md
index 80635a4..f6fea22 100644
--- a/content/intro.md
+++ b/content/intro.md
@@ -1,24 +1,29 @@
 # Meta-Analyses in Python
 
-## What is meta-analysis
+```{tableofcontents}
+```
 
-Meta-analysis is the statistical aggregration of group level results.
-Meta-analysis does not require the 
-- datasets: mega, meta
-  - group: GLM, mega
-    - subject: GLM, mega
-      - session: GLM, mega
-        - run: GLM, mega
+## What is meta-analysis?
 
-## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis?
+Meta-analysis is the re-use, and often aggregation, of previously computed statistical results. Analyzing previously computed results, often across several published studies, in this way is used to find consensus and/or parse differences across studies of a similar topic or paradigm. 
+
+Meta-analysis leans on relatively light-weight data representations (e.g., published tabular data) and, thus, does not require the original source of data. This allows wide reuse without the privacy concerns or computational resources necessary for re-analysis of primary datasets. Further, published results are often more accessible than original data sources, making it possible to synthesize many independent sources of information.
 
-standard meta-analysis is aggregating single variables, whereas neuroimaging meta-analyses are
-- unique because:
-  - coordinates require special processing
-  - there is a spatial extent
 
 ## Why would you want to do a meta-analysis?
-get consensus on a scientific question
 
-```{tableofcontents}
-```
+In the face of replication crises–such as those plaguing neuroimaging,  psychology, and cancer research–being able to aggregate results in this way is particularly valuable. Researchers can use meta-analysis to identify consistency across studies and papers. Neuroimaging meta-analyses, specifically, can allow researchers to find consistency across relatively homogeneous or related groups of studies, regardless of individual study sample sizes, pipeline differences, and intrinsic differences in functional neuroanatomy between study samples (i.e., {cite}`pintos_lobo_neural_2022`). On the other hand, meta-analysis can help identify a common effect across related, but distinct, studies {cite}`bartley_meta-analytic_2018`.
+
+Further, neuroimaging meta-analyses can be used to generate hypotheses for future primary data analysis, as well. Meta-analytically generated brain networks or regions of interest (ROIs) that are significantly activated across a cognitive or behavioral paradigm can be used in subsequent analyses (e.g., for limited field-of-view, functional connectivity, or psychophysiological interaction studies). Data-driven classification of a larger set of studies into distinct categories based on similarity of brain activations can be used to investigate or propose underlying cognitive/neurobiological models of complex processing {cite}`bottenhorn_cooperating_2018, flannery_meta-analytic_2020, laird_neural_2015, riedel_dissociable_2018`. Further, meta-analyses can be useful in identifying common neural phenomena shared by multiple, seemingly distinct psychiatric diagnoses {cite}`dugre_meta-analytical_2022, janiri_shared_2020, opel_cross-disorder_2020`.
+
+## How is meta-analysis in neuroimaging different from your standard, effect-size meta-analysis?
+
+A standard meta-analysis aggregates results across a single measure. Neuroimaging meta-analyses are a special case because:
+- data from a single statistical analysis of neuroimaging often include more than one effect size (e.g., multiple significant voxels, represented by stereotaxic coordinates)
+- stereotaxic coordinates include spatial information, in addition to effect sizes and significance, and thus require additional processing
+
+There are two over-arching categories of neuroimaging meta-analyses, differentiated by the input data source. *Coordinate-based* meta-analyses are run on stereotaxic x-, y-, z-coordinate data, such as those published in neuroimaging papers, while *image-based* meta-analyses run on 3D statistical image data, such as those shared on various online repositories (e.g., NeuroVault, the Open Science Framework). There are several algorithms for both categories, but they are both distinguished from standard meta-analyses by both the volume of data per study/case (i.e., coordinates, voxels), the spatial information that is inherent to that data, and the assumptions of independence that those two features violate.
+
+```{bibliography}
+:filter: docname in docnames
+```
\ No newline at end of file
diff --git a/content/references.bib b/content/references.bib
index 783ec6a..ee57a61 100644
--- a/content/references.bib
+++ b/content/references.bib
@@ -54,3 +54,181 @@ @book{ruby
   year      = {2008},
   publisher = {O'Reilly Media}
 }
+
+
+@article{bartley_meta-analytic_2018,
+	title = {Meta-analytic evidence for a core problem solving network across multiple representational domains},
+	volume = {92},
+	issn = {0149-7634},
+	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425494/},
+	doi = {10.1016/j.neubiorev.2018.06.009},
+	abstract = {Problem solving is a complex skill engaging multi-stepped reasoning processes to find unknown solutions. The breadth of real-world contexts requiring problem solving is mirrored by a similarly broad, yet unfocused neuroimaging literature, and the domain-general or context-specific brain networks associated with problem solving are not well understood. To more fully characterize those brain networks, we performed activation likelihood estimation meta-analysis on 280 neuroimaging problem solving experiments reporting 3166 foci from 1919 individuals across 131 papers. The general map of problem solving revealed broad fronto-cingulo-parietal convergence, regions similarly identified when considering separate mathematical, verbal, and visuospatial problem solving domain-specific analyses. Conjunction analysis revealed a common network supporting problem solving across diverse contexts, and difference maps distinguished functionally-selective sub-networks specific to task type. Our results suggest cooperation between representationally specialized sub-network and whole-brain systems provide a neural basis for problem solving, with the core network contributing general purpose resources to perform cognitive operations and manage problem demand. Further characterization of cross-network dynamics could inform neuroeducational studies on problem solving skill development.},
+	urldate = {2020-10-01},
+	journal = {Neuroscience and biobehavioral reviews},
+	author = {Bartley, Jessica E. and Boeving, Emily R. and Riedel, Michael C. and Bottenhorn, Katherine L. and Salo, Taylor and Eickhoff, Simon B. and Brewe, Eric and Sutherland, Matthew T. and Laird, Angela R.},
+	month = sep,
+	year = {2018},
+	pmid = {29944961},
+	pmcid = {PMC6425494},
+	pages = {318--337},
+	file = {PubMed Central Full Text PDF:/Users/katherine.b/Zotero/storage/7WG2J7G3/Bartley et al. - 2018 - Meta-analytic evidence for a core problem solving .pdf:application/pdf},
+}
+
+
+@article{riedel_dissociable_2018,
+	title = {Dissociable meta-analytic brain networks contribute to coordinated emotional processing},
+	volume = {39},
+	issn = {1097-0193},
+	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.24018},
+	doi = {10.1002/hbm.24018},
+	abstract = {Meta-analytic techniques for mining the neuroimaging literature continue to exert an impact on our conceptualization of functional brain networks contributing to human emotion and cognition. Traditional theories regarding the neurobiological substrates contributing to affective processing are shifting from regional- towards more network-based heuristic frameworks. To elucidate differential brain network involvement linked to distinct aspects of emotion processing, we applied an emergent meta-analytic clustering approach to the extensive body of affective neuroimaging results archived in the BrainMap database. Specifically, we performed hierarchical clustering on the modeled activation maps from 1,747 experiments in the affective processing domain, resulting in five meta-analytic groupings of experiments demonstrating whole-brain recruitment. Behavioral inference analyses conducted for each of these groupings suggested dissociable networks supporting: (1) visual perception within primary and associative visual cortices, (2) auditory perception within primary auditory cortices, (3) attention to emotionally salient information within insular, anterior cingulate, and subcortical regions, (4) appraisal and prediction of emotional events within medial prefrontal and posterior cingulate cortices, and (5) induction of emotional responses within amygdala and fusiform gyri. These meta-analytic outcomes are consistent with a contemporary psychological model of affective processing in which emotionally salient information from perceived stimuli are integrated with previous experiences to engender a subjective affective response. This study highlights the utility of using emergent meta-analytic methods to inform and extend psychological theories and suggests that emotions are manifest as the eventual consequence of interactions between large-scale brain networks.},
+	language = {en},
+	number = {6},
+	urldate = {2022-08-08},
+	journal = {Human Brain Mapping},
+	author = {Riedel, Michael C. and Yanes, Julio A. and Ray, Kimberly L. and Eickhoff, Simon B. and Fox, Peter T. and Sutherland, Matthew T. and Laird, Angela R.},
+	year = {2018},
+	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.24018},
+	keywords = {affective processing, BrainMap, co-activations, data mining, emotion, functional connectivity, functional magnetic resonance imaging, meta-analysis, neuroinformatics},
+	pages = {2514--2531},
+	file = {Full Text PDF:/Users/katherine.b/Zotero/storage/W3RGZZU4/Riedel et al. - 2018 - Dissociable meta-analytic brain networks contribut.pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/BNSH2ZAA/hbm.html:text/html},
+}
+
+@misc{pintos_lobo_neural_2022,
+	title = {Neural systems underlying {RDoC} social constructs: {An} activation likelihood estimation meta-analysis},
+	copyright = {© 2022, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
+	shorttitle = {Neural systems underlying {RDoC} social constructs},
+	url = {https://www.biorxiv.org/content/10.1101/2022.04.04.487016v2},
+	doi = {10.1101/2022.04.04.487016},
+	abstract = {Neuroscientists have sought to identify the underlying neural systems supporting social processing that allows interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMH’s Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the “social brain”. We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.},
+	language = {en},
+	urldate = {2022-08-08},
+	publisher = {bioRxiv},
+	author = {Pintos Lobo, Rosario and Bottenhorn, Katherine L. and Riedel, Michael C. and Toma, Afra I. and Hare, Megan M. and Smith, Donisha D. and Moor, Alexandra C. and Cowan, Isis K. and Valdes, Javier A. and Bartley, Jessica E. and Salo, Taylor and Boeving, Emily R. and Pankey, Brianna and Sutherland, Matthew T. and Musser, Erica D. and Laird, Angela R.},
+	month = jul,
+	year = {2022},
+	note = {Pages: 2022.04.04.487016
+Section: New Results},
+	file = {Full Text PDF:/Users/katherine.b/Zotero/storage/8I8JQGTT/Lobo et al. - 2022 - Neural systems underlying RDoC social constructs .pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/MBZD6236/2022.04.04.html:text/html},
+}
+
+@article{laird_neural_2015,
+	title = {Neural architecture underlying classification of face perception paradigms.},
+	volume = {119},
+	issn = {1095-9572},
+	doi = {10.1016/j.neuroimage.2015.06.044},
+	abstract = {We present a novel strategy for deriving a classification system of functional neuroimaging paradigms that relies on hierarchical clustering of experiments archived in the BrainMap database. The goal of our proof-of-concept application was to examine the underlying neural architecture of the face perception literature from a meta-analytic perspective, as these studies include a wide range of tasks. Task-based results exhibiting similar activation patterns were grouped as similar, while tasks activating different brain networks were classified as functionally distinct. We identified four sub-classes of face tasks: (1) Visuospatial Attention and Visuomotor Coordination to Faces, (2) Perception and Recognition of Faces, (3) Social Processing and Episodic Recall of Faces, and (4) Face Naming and Lexical Retrieval. Interpretation of these sub-classes supports an extension of a well-known model of face perception to include a core system for visual analysis and extended systems for personal information, emotion, and salience processing. Overall, these results demonstrate that a large-scale data mining approach can inform the evolution of theoretical cognitive models by probing the range of behavioral manipulations across experimental tasks.},
+	journal = {NeuroImage},
+	author = {Laird, Angela R and Riedel, Michael C and Sutherland, Matthew T and Eickhoff, Simon B and Ray, Kimberly L and Uecker, Angela M and Fox, P Mickle and Turner, Jessica A and Fox, Peter T},
+	year = {2015},
+	pages = {70--80},
+}
+
+
+@article{flannery_meta-analytic_2020,
+	title = {Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms},
+	volume = {20},
+	issn = {1530-7026},
+	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7117996/},
+	doi = {10.3758/s13415-019-00763-7},
+	abstract = {Reward learning is a ubiquitous cognitive mechanism guiding adaptive choices and behaviors, and when impaired, can lead to considerable mental health consequences. Reward-related functional neuroimaging studies have begun to implicate networks of brain regions essential for processing various peripheral influences (e.g., risk, subjective preference, delay, social context) involved in the multifaceted reward processing construct. To provide a more complete neurocognitive perspective on reward processing that synthesizes findings across the literature while also appreciating these peripheral influences, we used emerging meta-analytic techniques to elucidate brain regions, and in turn networks, consistently engaged in distinct aspects of reward processing. Using a data-driven, meta-analytic, k-means clustering approach, we dissociated seven meta-analytic groupings (MAGs) of neuroimaging results (i.e., brain activity maps) from 749 experimental contrasts across 176 reward processing studies involving 13,358 healthy participants. We then performed an exploratory functional decoding approach to gain insight into the putative functions associated with each MAG. We identified a seven-MAG clustering solution that represented dissociable patterns of convergent brain activity across reward processing tasks. Additionally, our functional decoding analyses revealed that each of these MAGs mapped onto discrete behavior profiles that suggested specialized roles in predicting value (MAG-1 \& MAG-2) and processing a variety of emotional (MAG-3), external (MAG-4 \& MAG-5), and internal (MAG-6 \& MAG-7) influences across reward processing paradigms. These findings support and extend aspects of well-accepted reward learning theories and highlight large-scale brain network activity associated with distinct aspects of reward processing.},
+	number = {2},
+	urldate = {2022-08-08},
+	journal = {Cognitive, affective \& behavioral neuroscience},
+	author = {Flannery, Jessica S. and Riedel, Michael C. and Bottenhorn, Katherine L. and Poudel, Ranjita and Salo, Taylor and Hill-Bowen, Lauren D. and Laird, Angela R. and Sutherland, Matthew T.},
+	month = apr,
+	year = {2020},
+	pmid = {31872334},
+	pmcid = {PMC7117996},
+	pages = {215--235},
+	file = {Full Text:/Users/katherine.b/Zotero/storage/QKW5JYK8/Flannery et al. - 2020 - Meta-analytic clustering dissociates brain activit.pdf:application/pdf},
+}
+
+@article{dugre_meta-analytical_2022,
+	title = {Meta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders},
+	volume = {12},
+	copyright = {2022 The Author(s)},
+	issn = {2045-2322},
+	url = {https://www.nature.com/articles/s41598-022-08909-3},
+	doi = {10.1038/s41598-022-08909-3},
+	abstract = {In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments’ topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.},
+	language = {en},
+	number = {1},
+	urldate = {2022-08-08},
+	journal = {Scientific Reports},
+	author = {Dugré, Jules R. and Eickhoff, Simon B. and Potvin, Stéphane},
+	month = mar,
+	year = {2022},
+	note = {Number: 1
+Publisher: Nature Publishing Group},
+	keywords = {Human behaviour, Social neuroscience},
+	pages = {4909},
+	file = {Full Text PDF:/Users/katherine.b/Zotero/storage/GWUM4EAX/Dugré et al. - 2022 - Meta-analytical transdiagnostic neural correlates .pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/FF3MI2LD/s41598-022-08909-3.html:text/html},
+}
+
+@article{opel_cross-disorder_2020,
+	series = {New {Mechanisms} of {Psychosis}: {Clinical} {Implications}},
+	title = {Cross-{Disorder} {Analysis} of {Brain} {Structural} {Abnormalities} in {Six} {Major} {Psychiatric} {Disorders}: {A} {Secondary} {Analysis} of {Mega}- and {Meta}-analytical {Findings} {From} the {ENIGMA} {Consortium}},
+	volume = {88},
+	issn = {0006-3223},
+	shorttitle = {Cross-{Disorder} {Analysis} of {Brain} {Structural} {Abnormalities} in {Six} {Major} {Psychiatric} {Disorders}},
+	url = {https://www.sciencedirect.com/science/article/pii/S0006322320315857},
+	doi = {10.1016/j.biopsych.2020.04.027},
+	abstract = {Background
+Neuroimaging studies have consistently reported similar brain structural abnormalities across different psychiatric disorders. Yet, the extent and regional distribution of shared morphometric abnormalities between disorders remains unknown.
+Methods
+Here, we conducted a cross-disorder analysis of brain structural abnormalities in 6 psychiatric disorders based on effect size estimates for cortical thickness and subcortical volume differences between healthy control subjects and psychiatric patients from 11 mega- and meta-analyses from the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta Analysis) consortium. Correlational and exploratory factor analyses were used to quantify the relative overlap in brain structural effect sizes between disorders and to identify brain regions with disorder-specific abnormalities.
+Results
+Brain structural abnormalities in major depressive disorder, bipolar disorder, schizophrenia, and obsessive-compulsive disorder were highly correlated (r = .443 to r = .782), and one shared latent underlying factor explained between 42.3\% and 88.7\% of the brain structural variance of each disorder. The observed shared morphometric signature of these disorders showed little similarity with brain structural patterns related to physiological aging. In contrast, patterns of brain structural abnormalities independent of all other disorders were observed in both attention-deficit/hyperactivity disorder and autism spectrum disorder. Brain regions showing high proportions of independent variance were identified for each disorder to locate disorder-specific morphometric abnormalities.
+Conclusions
+Taken together, these results offer novel insights into transdiagnostic as well as disorder-specific brain structural abnormalities across 6 major psychiatric disorders. Limitations comprise the uncertain contribution of risk factors, comorbidities, and medication effects to the observed pattern of results that should be clarified by future research.},
+	language = {en},
+	number = {9},
+	urldate = {2022-08-08},
+	journal = {Biological Psychiatry},
+	author = {Opel, Nils and Goltermann, Janik and Hermesdorf, Marco and Berger, Klaus and Baune, Bernhard T. and Dannlowski, Udo},
+	month = nov,
+	year = {2020},
+	keywords = {Cross-disorder, ENIGMA, Neuroimaging, Psychiatric disorders, Structural MRI, Transdiagnostic},
+	pages = {678--686},
+	file = {ScienceDirect Snapshot:/Users/katherine.b/Zotero/storage/FMEKC5W7/S0006322320315857.html:text/html},
+}
+
+@article{janiri_shared_2020,
+	title = {Shared {Neural} {Phenotypes} for {Mood} and {Anxiety} {Disorders}: {A} {Meta}-analysis of 226 {Task}-{Related} {Functional} {Imaging} {Studies}},
+	volume = {77},
+	issn = {2168-622X},
+	shorttitle = {Shared {Neural} {Phenotypes} for {Mood} and {Anxiety} {Disorders}},
+	url = {https://doi.org/10.1001/jamapsychiatry.2019.3351},
+	doi = {10.1001/jamapsychiatry.2019.3351},
+	abstract = {Major depressive disorder, bipolar disorder, posttraumatic stress disorder, and anxiety disorders are highly comorbid and have shared clinical features. It is not yet known whether their clinical overlap is reflected at the neurobiological level.To detect transdiagnostic convergence in abnormalities in task-related brain activation.Task-related functional magnetic resonance imaging articles published in PubMed, Web of Science, and Google Scholar during the last decade comparing control individuals with patients with mood, posttraumatic stress, and anxiety disorders were examined.Following Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guidelines, articles were selected if they reported stereotactic coordinates of whole-brain–based activation differences between adult patients and control individuals.Coordinates of case-control differences coded by diagnosis and by cognitive domain based on the research domain criteria were analyzed using activation likelihood estimation.Identification of transdiagnostic clusters of aberrant activation and quantification of the contribution of diagnosis and cognitive domain to each cluster.A total of 367 experiments (major depressive disorder, 149; bipolar disorder, 103; posttraumatic stress disorder, 55; and anxiety disorders, 60) were included comprising observations from 4507 patients and 4755 control individuals. Three right-sided clusters of hypoactivation were identified centered in the inferior prefrontal cortex/insula (volume, 2120 mm3), the inferior parietal lobule (volume, 1224 mm3), and the putamen (volume, 888 mm3); diagnostic differences were noted only in the putamen (χ23 = 8.66; P = .03), where hypoactivation was more likely in bipolar disorder (percentage contribution = 72.17\%). Tasks associated with cognitive systems made the largest contribution to each cluster (percentage contributions \>29\%). Clusters of hyperactivation could only be detected using a less stringent threshold. These were centered in the perigenual/dorsal anterior cingulate cortex (volume, 2208 mm3), the left amygdala/parahippocampal gyrus (volume, 2008 mm3), and the left thalamus (volume, 1904 mm3). No diagnostic differences were observed (χ23 \< 3.06; P \> .38), while tasks associated with negative valence systems made the largest contribution to each cluster (percentage contributions \>49\%). All findings were robust to the moderator effects of age, sex, and magnetic field strength of the scanner and medication.In mood disorders, posttraumatic stress disorder, and anxiety disorders, the most consistent transdiagnostic abnormalities in task-related brain activity converge in regions that are primarily associated with inhibitory control and salience processing. Targeting these shared neural phenotypes could potentially mitigate the risk of affective morbidity in the general population and improve outcomes in clinical populations.},
+	number = {2},
+	urldate = {2022-08-08},
+	journal = {JAMA Psychiatry},
+	author = {Janiri, Delfina and Moser, Dominik A. and Doucet, Gaelle E. and Luber, Maxwell J. and Rasgon, Alexander and Lee, Won Hee and Murrough, James W. and Sani, Gabriele and Eickhoff, Simon B. and Frangou, Sophia},
+	month = feb,
+	year = {2020},
+	pages = {172--179},
+	file = {Full Text:/Users/katherine.b/Zotero/storage/76QG447G/Janiri et al. - 2020 - Shared Neural Phenotypes for Mood and Anxiety Diso.pdf:application/pdf;Snapshot:/Users/katherine.b/Zotero/storage/UNZZABNU/2753513.html:text/html},
+}
+
+
+@article{bottenhorn_cooperating_2018,
+	title = {Cooperating yet distinct brain networks engaged during naturalistic paradigms: {A} meta-analysis of functional {MRI} results},
+	volume = {3},
+	issn = {2472-1751},
+	shorttitle = {Cooperating yet distinct brain networks engaged during naturalistic paradigms},
+	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6326731/},
+	doi = {10.1162/netn_a_00050},
+	abstract = {Cognitive processes do not occur by pure insertion and instead depend on the full complement of co-occurring mental processes, including perceptual and motor functions. As such, there is limited ecological validity to human neuroimaging experiments that use highly controlled tasks to isolate mental processes of interest. However, a growing literature shows how dynamic, interactive tasks have allowed researchers to study cognition as it more naturally occurs. Collective analysis across such neuroimaging experiments may answer broader questions regarding how naturalistic cognition is biologically distributed throughout the brain. We applied an unbiased, data-driven, meta-analytic approach that uses k-means clustering to identify core brain networks engaged across the naturalistic functional neuroimaging literature. Functional decoding allowed us to, then, delineate how information is distributed between these networks throughout the execution of dynamical cognition in realistic settings. This analysis revealed six recurrent patterns of brain activation, representing sensory, domain-specific, and attentional neural networks that support the cognitive demands of naturalistic paradigms. Although gaps in the literature remain, these results suggest that naturalistic fMRI paradigms recruit a common set of networks that allow both separate processing of different streams of information and integration of relevant information to enable flexible cognition and complex behavior., Naturalistic fMRI paradigms offer increased ecological validity over traditional paradigms, addressing the gap left by studying highly interactive cognitive processes as isolated neural phenomena. This study identifies the connectional architecture supporting dynamic cognition in naturalistic fMRI paradigms, the first meta-analysis of a wide range of more realistic neuroimaging experiments. Here we identify and characterize six core patterns of neural activity that support functional segregation and integration in large-scale brain networks. This study provides a unique investigation of the cooperating neural systems that enable complex behavior.},
+	number = {1},
+	urldate = {2020-10-01},
+	journal = {Network Neuroscience},
+	author = {Bottenhorn, Katherine L. and Flannery, Jessica S. and Boeving, Emily R. and Riedel, Michael C. and Eickhoff, Simon B. and Sutherland, Matthew T. and Laird, Angela R.},
+	month = oct,
+	year = {2018},
+	pmid = {30793072},
+	pmcid = {PMC6326731},
+	pages = {27--48},
+	file = {PubMed Central Full Text PDF:/Users/katherine.b/Zotero/storage/GDS86K7E/Bottenhorn et al. - 2018 - Cooperating yet distinct brain networks engaged du.pdf:application/pdf},
+}