scholarly journals Large-scale automated synthesis of human functional neuroimaging data

2011 ◽  
Vol 8 (8) ◽  
pp. 665-670 ◽  
Author(s):  
Tal Yarkoni ◽  
Russell A Poldrack ◽  
Thomas E Nichols ◽  
David C Van Essen ◽  
Tor D Wager
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Automated Synthesis ◽  
Neuroimaging Data

scholarly journals Toward a Global and Reproducible Science for Brain Imaging in Neurotrauma: The ENIGMA Adult Moderate/Severe Traumatic Brain Injury Working Group

2019 ◽  
Author(s):  
Alexander Olsen ◽  
Talin Babikian ◽  
Erin D. Bigler ◽  
Karen Caeyenberghs ◽  
Virginia Conde ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Data Analysis ◽  
Brain Injury ◽  
Working Group ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Small Sample ◽  
Mortality And Morbidity ◽  
Neuroimaging Data

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with nonimaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for largescale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals.

scholarly journals Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data

2014 ◽  
Author(s):  
Andrew S Fox ◽  
Luke J Chang ◽  
Krzysztof J Gorgolewski ◽  
Tal Yarkoni
Keyword(s):  
Biological Sciences ◽  
Cognitive Processes ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Psychological Phenomena ◽  
Allen Brain ◽  
Neuroimaging Data ◽  
Meta Analyses ◽  
Or Genes ◽  
Novel Associations

Understanding how microscopic molecules give rise to complex cognitive processes is a major goal of the biological sciences. The countless hypothetical molecule-cognition relationships necessitate discovery-based techniques to guide scientists toward the most productive lines of investigation. To this end, we present a novel discovery tool that uses spatial patterns of neural gene expression from the Allen Brain Institute (ABI) and large-scale functional neuroimaging meta-analyses from the Neurosynth framework to bridge neurogenetic and neuroimaging data. We quantified the spatial similarity between over 20,000 genes from the ABI and 48 psychological topics derived from lexical analysis of neuroimaging articles, producing a comprehensive set of gene/cognition mappings that we term the Neurosynth-gene atlas. We demonstrate the ability to independently replicate known gene/cognition associations (e.g., between dopamine and reward), and subsequently use it to identify a range of novel associations between individual molecules or genes and complex psychological phenomena such as reward, memory and emotion. Our results complement existing discovery-based methods such as GWAS, and provide a novel means of generating hypotheses about the neurogenetic substrates of complex cognitive functions.

scholarly journals Cerebellum and Emotion in Morality

2021 ◽  
Author(s):  
Hyemin Han
Keyword(s):  
Large Scale ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Moral Functioning ◽  
Neuroimaging Data ◽  
Crus I ◽  
The Right ◽  
Brain Image Analysis ◽  
The Relationship

In the current chapter, I examined the relationship between the cerebellum, emotion, and morality with evidence from large-scale neuroimaging data analysis. Although the aforementioned relationship has not been well studied in neuroscience, recent studies have shown that the cerebellum is closely associated with emotional and social processes at the neural level. Also, debates in the field of moral philosophy, psychology, and neuroscience have supported the importance of emotion in moral functioning. Thus, I explored the potentially important but less-studies topic with NeuroSynth, a tool for large-scale brain image analysis, while addressing issues associated with reverse inference. The result from analysis demonstrated that brain regions in the cerebellum, the right Crus I and Crus II in particular, were specifically associated with morality in general. I discussed the potential implications of the finding based on clinical and functional neuroimaging studies of the cerebellum, emotional functioning, and neural networks for diverse psychological processes.

What Has Direct Cortical and Subcortical Electrostimulation Taught Us about Neurolinguistics?

2019 ◽  
pp. 185-211
Author(s):  
Hugues Duffau
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Functional Neuroimaging ◽  
Great Accuracy ◽  
Subcortical White Matter ◽  
Cerebral Lesions ◽  
Physiological Basis ◽  
Postoperative Mri ◽  
The Brain

Investigating the neural and physiological basis of language is one of the most important challenges in neurosciences. Direct electrical stimulation (DES), usually performed in awake patients during surgery for cerebral lesions, is a reliable tool for detecting both cortical and subcortical (white matter and deep grey nuclei) regions crucial for cognitive functions, especially language. DES transiently interacts locally with a small cortical or axonal site, but also nonlocally, as the focal perturbation will disrupt the entire subnetwork sustaining a given function. Thus, in contrast to functional neuroimaging, DES represents a unique opportunity to identify with great accuracy and reproducibility, in vivo in humans, the structures that are actually indispensable to the function, by inducing a transient virtual lesion based on the inhibition of a subcircuit lasting a few seconds. Currently, this is the sole technique that is able to directly investigate the functional role of white matter tracts in humans. Thus, combining transient disturbances elicited by DES with the anatomical data provided by pre- and postoperative MRI enables to achieve reliable anatomo-functional correlations, supporting a network organization of the brain, and leading to the reappraisal of models of language representation. Finally, combining serial peri-operative functional neuroimaging and online intraoperative DES allows the study of mechanisms underlying neuroplasticity. This chapter critically reviews the basic principles of DES, its advantages and limitations, and what DES can reveal about the neural foundations of language, that is, the large-scale distribution of language areas in the brain, their connectivity, and their ability to reorganize.

Database of MNI stereotactic coordinates for deep brain stimulation targets in neuropsychiatric disorders

2011 ◽  
Vol 26 (S2) ◽  
pp. 1149-1149
Author(s):  
U. Moser ◽  
M. Savli ◽  
R. Lanzenberger ◽  
S. Kasper
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Functional Neuroimaging ◽  
Case Reports ◽  
Three Dimensional ◽  
Compulsive Disorder ◽  
Neuroimaging Data ◽  
Specific Disorders ◽  
Image Position ◽  
Deep Brain

IntroductionDeep brain stimulation (DBS) is a promising therapy option for otherwise treatment-resistant neuropsychiatrie disorders, especially in obsessive-compulsive disorder (OCD), major depression (TRD) and Tourette's Syndrome (TS).ObjectiveThe brain coordinates of the DBS targets are mainly reported using measurements in original, unnormalized brains. In the neuroimaging community stereotactic data are mainly indicated in the standardized Montreal Neurological Institute (MNI) space, i.e. a three-dimensional proportional grid system.AimsImproved comparability between targets in DBS studies and molecular and functional neuroimaging data from PET, SPECT, MRI, fMRI, mostly published with stereotactic data.MethodsA comprehensive and systematic literature search for published DBS case reports or studies in TRD, OCD and TS was performed. We extracted the tip positions of electrode leads as provided in the publications or by the authors, and transferred individual coordinates to the standard brain in the MNI space.Results46 publications fulfilled the inclusion criteria. The main targets for the specific disorders and one or two examples of their calculated MNI coordinates are indicated in the table:[MNI coordinates of the main DBS targets]ConclusionsWe provide DBS data of neuropsychiatrie disorders in the MNI space, improving the comparability to molecular, functional and structural neuroimaging data.

scholarly journals Functional Neuroimaging and Psychology: What Have You Done for Me Lately?

2013 ◽  
Vol 25 (6) ◽  
pp. 834-842 ◽  
Author(s):  
Joseph M. Moran ◽  
Jamil Zaki
Keyword(s):  
Functional Imaging ◽  
Brain Mapping ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Psychological Theory ◽  
Quarter Century ◽  
Psychological Phenomena ◽  
Neuroimaging Data ◽  
Subsequent Behavior ◽  
Reverse Inference

Functional imaging has become a primary tool in the study of human psychology but is not without its detractors. Although cognitive neuroscientists have made great strides in understanding the neural instantiation of countless cognitive processes, commentators have sometimes argued that functional imaging provides little or no utility for psychologists. And indeed, myriad studies over the last quarter century have employed the technique of brain mapping—identifying the neural correlates of various psychological phenomena—in ways that bear minimally on psychological theory. How can brain mapping be made more relevant to behavioral scientists broadly? Here, we describe three trends that increase precisely this relevance: (i) the use of neuroimaging data to adjudicate between competing psychological theories through forward inference, (ii) isolating neural markers of information processing steps to better understand complex tasks and psychological phenomena through probabilistic reverse inference, and (iii) using brain activity to predict subsequent behavior. Critically, these new approaches build on the extensive tradition of brain mapping, suggesting that efforts in this area—although not initially maximally relevant to psychology—can indeed be used in ways that constrain and advance psychological theory.

scholarly journals Social structure learning in human anterior insula

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Tatiana Lau ◽  
Samuel J Gershman ◽  
Mina Cikara
Keyword(s):  
Social Group ◽  
Structure Learning ◽  
Functional Neuroimaging ◽  
Group Structure ◽  
Anterior Insula ◽  
Anterior Cingulate ◽  
Latent Structure ◽  
Group Representations ◽  
Neuroimaging Data ◽  
Category Labels

Humans form social coalitions in every society, yet we know little about how we learn and represent social group boundaries. Here we derive predictions from a computational model of latent structure learning to move beyond explicit category labels and interpersonal, or dyadic, similarity as the sole inputs to social group representations. Using a model-based analysis of functional neuroimaging data, we find that separate areas correlate with dyadic similarity and latent structure learning. Trial-by-trial estimates of ‘allyship’ based on dyadic similarity between participants and each agent recruited medial prefrontal cortex/pregenual anterior cingulate (pgACC). Latent social group structure-based allyship estimates, in contrast, recruited right anterior insula (rAI). Variability in the brain signal from rAI improved prediction of variability in ally-choice behavior, whereas variability from the pgACC did not. These results provide novel insights into the psychological and neural mechanisms by which people learn to distinguish ‘us’ from ‘them.’

scholarly journals Advances in studying brain morphology: The benefits of open-access data

2017 ◽  
Author(s):  
Christopher R Madan
Keyword(s):  
Open Access ◽  
Brain Structure ◽  
Large Scale ◽  
Open Data ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Open Access Data ◽  
Age Related ◽  
Neuroimaging Data ◽  
Access Data

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., 2017; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), the current paper is focused specifically on the implications of open data to brain morphology research.

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