scholarly journals Atlas-based classification algorithms for identification of informative brain regions in fMRI data

2018 ◽  
Author(s):  
Juan E. Arco ◽  
Paloma Díaz-Gutiérrez ◽  
Javier Ramírez ◽  
María Ruz
Keyword(s):  
Averaging Method ◽  
Computational Cost ◽  
Brain Regions ◽  
Specific Method ◽  
Experimental Conditions ◽  
Large Sphere ◽  
Local Averaging ◽  
Functional Value ◽  
Neuroimaging Data ◽  
The Brain

AbstractMulti-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employedIn the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al., 2013a) and a Multi-Kernel Learning (MKL, Rakotomamonjy et al., 2008) approach, in a scenario where the goal is to find the informative brain regions that support certain mental operations. These methods employ weights to measure the informativeness of a brain region and highly reduce the large computational cost that Searchlight entails. We evaluated their performance in two different scenarios where the differential BOLD activation between experimental conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations.Results show that both methods were able to localize informative regions when differences between conditions were large, demonstrating a large sensitivity and stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provided the directionality of univariate approaches. However, when differences were small, only ABLA localized informative regions. Thus, our results show that atlas-based methods are useful alternatives to Searchlight, but that the nature of the classification to perform should be taken into account when choosing the specific method to implement.

Structural magnetic resonance imaging markers of susceptibility and transition to schizophrenia: A review of familial and clinical high risk population studies

2014 ◽  
Vol 29 (2) ◽  
pp. 144-154 ◽  
Author(s):  
C Bois ◽  
HC Whalley ◽  
AM McIntosh ◽  
SM Lawrie
Keyword(s):  
High Risk ◽  
Grey Matter ◽  
Brain Regions ◽  
High Risk Population ◽  
Clinical High Risk ◽  
Cross Sectional ◽  
Structural Abnormalities ◽  
Neuroimaging Data ◽  
Imaging Markers ◽  
The Brain

There is a growing consensus that a symptomatology as complex and heterogeneous as schizophrenia is likely to be produced by widespread perturbations of brain structure, as opposed to isolated deficits in specific brain regions. Structural brain-imaging studies have shown that several features of the brain, such as grey matter, white matter integrity and the morphology of the cortex differ in individuals at high risk of the disorder compared to controls, but to a lesser extent than in patients, suggesting that structural abnormalities may form markers of vulnerability to the disorder. Research has had some success in delineating abnormalities specific to those individuals that transition to psychosis, compared to those at high risk that do not, suggesting that a general risk for the disorder can be distinguished from alterations specific to frank psychosis. In this paper, we review cross-sectional and longitudinal studies of individuals at familial or clinical high risk of the disorder. We conclude that the search for reliable markers of schizophrenia is likely to be enhanced by methods which amalgamate structural neuroimaging data into a coherent framework that takes into account the widespread distribution of brain alterations, and relates this to leading hypotheses of schizophrenia.

scholarly journals Same Brain, Different Look?—The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

2021 ◽  
Vol 10 (21) ◽  
pp. 4987
Author(s):  
Ronja Thieleking ◽  
Rui Zhang ◽  
Maria Paerisch ◽  
Kerstin Wirkner ◽  
Alfred Anwander ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Diffusion Imaging ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Clinical Diagnostics ◽  
Neuroimaging Data ◽  
Age Range ◽  
The Impact ◽  
The Brain ◽  
Healthy Participants

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

scholarly journals Genetic, Cellular, and Connectomic Characterization of the Adult Human Brain Regions Commonly Plagued by Glioma

2020 ◽  
Author(s):  
Ayan S. Mandal ◽  
Rafael Romero-Garcia ◽  
Michael G. Hart ◽  
John Suckling
Keyword(s):  
Brain Regions ◽  
Association Cortex ◽  
Low Grade ◽  
Expression Of Genes ◽  
Neuroimaging Data ◽  
Frequency Map ◽  
Multiple Graph ◽  
Anatomic Localization ◽  
The Brain

AbstractA better understanding of the nonrandom localization patterns of gliomas across the brain could lend clues to the origins of these types of tumors. Following hypotheses derived from prior research into neuropsychiatric disease and cancer, gliomas may be expected to localize to brain regions characterized by hubness, stem-like cells, and transcription of genetic drivers of gliomagenesis. We combined neuroimaging data from 335 adult patients with high- and low-grade glioma to form a replicable tumor frequency map. Using this map, we demonstrated that glioma frequency is elevated in association cortex and correlated with multiple graph-theoretical metrics of high functional connectedness. Brain regions populated with stem-like cells also exhibited a high glioma frequency. Furthermore, gliomas were localized to brain regions enriched with the expression of genes associated with chromatin organization and synaptic signaling. Finally, a regression model incorporating connectomic, cellular, and genetic factors explained 58% of the variance in glioma frequency. Our findings illustrate how factors of diverse scale, from genetic to connectomic, can independently influence the anatomic localization of oncogenesis.

scholarly journals Finding decodable information that is read out in behaviour

2018 ◽  
Author(s):  
Tijl Grootswagers ◽  
Radoslaw M. Cichy ◽  
Thomas A. Carlson
Keyword(s):  
Temporal Cortex ◽  
Brain Activation ◽  
Object Perception ◽  
Neural Activation ◽  
Experimental Conditions ◽  
Activation Patterns ◽  
Visual Objects ◽  
Neuroimaging Data ◽  
Ventral Temporal Cortex ◽  
The Brain

AbstractMultivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

What can Functional Neuroimaging Tell the Experimental Psychologist?

2005 ◽  
Vol 58 (2) ◽  
pp. 193-233 ◽  
Author(s):  
Richard Henson
Keyword(s):  
Short Term Memory ◽  
Functional Neuroimaging ◽  
A Priori ◽  
Qualitative Difference ◽  
Imaging Data ◽  
Structure Mapping ◽  
Function Structure ◽  
Experimental Conditions ◽  
Neuroimaging Data ◽  
The Brain

I argue here that functional neuroimaging data—which I restrict to the haemodynamic techniques of fMRI and PET—can inform psychological theorizing, provided one assumes a “systematic” function–structure mapping in the brain. In this case, imaging data simply comprise another dependent variable, along with behavioural data, that can be used to test competing theories. In particular, I distinguish two types of inference: function-to-structure deduction and structure-to-function induction. With the former inference, a qualitatively different pattern of activity over the brain under two experimental conditions implies at least one different function associated with changes in the independent variable. With the second type of inference, activity of the same brain region(s) under two conditions implies a common function, possibly not predicted a priori. I illustrate these inferences with imaging studies of recognition memory, short-term memory, and repetition priming. I then consider in greater detail what is meant by a “systematic” function–structure mapping and argue that, particularly for structure-to-function induction, this entails a one-to-one mapping between functional and structural units, although the structural unit may be a network of interacting regions and care must be taken over the appropriate level of functional/structural abstraction. Nonetheless, the assumption of a systematic function–structure mapping is a “working hypothesis” that, in common with other scientific fields, cannot be proved on independent grounds and is probably best evaluated by the success of the enterprise as a whole. I also consider statistical issues such as the definition of a qualitative difference and methodological issues such as the relationship between imaging and behavioural data. I finish by reviewing various objections to neuroimaging, including neophrenology, functionalism, and equipotentiality, and by observing some criticisms of current practice in the imaging literature.

scholarly journals Same Brain, Different Look? – the Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

2021 ◽  
Author(s):  
Ronja Thieleking ◽  
Rui Zhang ◽  
Maria Paerisch ◽  
Kerstin Wirkner ◽  
Alfred Anwander ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Diffusion Imaging ◽  
Mean Diffusivity ◽  
Brain Regions ◽  
Clinical Diagnostics ◽  
Neuroimaging Data ◽  
Age Range ◽  
The Impact ◽  
The Brain ◽  
Healthy Participants

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps,obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

scholarly journals A Practical Introduction to Network Neuroscience for Communication Researchers

2020 ◽  
Author(s):  
Jacob T. Fisher ◽  
Frederic R. Hopp ◽  
René Weber
Keyword(s):  
Statistical Tests ◽  
Brain Regions ◽  
Psychology Research ◽  
Media Psychology ◽  
Evolving Networks ◽  
Communication Processes ◽  
Neuroimaging Data ◽  
Neural Underpinnings ◽  
The Brain ◽  
Network Neuroscience

The increasing adoption of brain imaging methods has greatly augmented our understanding of the neural underpinnings of communication processes. Enabled by recent advancements in mathematics and computational infrastructure, researchers have begun to move beyond traditional univariate analytic techniques in favor of methods that consider the brain in terms of evolving networks of interactions between brain regions. This network neuroscience approach is a potential boon to communication and media psychology research but also requires a careful look at the complications inherent in adopting a novel (and complex) methodological tool. In this manuscript, we provide an overview of network neuroscience in view of the needs of communication neuroscientists, discussing considerations that must be considered when constructing networks from neuroimaging data and conducting statistical tests on these networks. Throughout the manuscript, we highlight research domains in which network neuroscience is likely to be particularly useful for increasing theoretical clarity in communication and media psychology research.

scholarly journals Multiclass Sparse Bayesian Regression for fMRI-Based Prediction

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Vincent Michel ◽  
Evelyn Eger ◽  
Christine Keribin ◽  
Bertrand Thirion
Keyword(s):  
Brain Regions ◽  
Bayesian Regression ◽  
Data Sets ◽  
Brain Images ◽  
Popular Approach ◽  
Prediction Function ◽  
Neuroimaging Data ◽  
Behavioral Parameters ◽  
The Brain ◽  
Better Than

Inverse inferencehas recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, calledMulticlass Sparse Bayesian Regression(MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.

scholarly journals Same Brain, Different Look? – the Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

2021 ◽  
Author(s):  
Ronja Thieleking ◽  
Rui Zhang ◽  
Maria Paerisch ◽  
Kerstin Wirkner ◽  
Alfred Anwander ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Diffusion Imaging ◽  
Brain Regions ◽  
Clinical Diagnostics ◽  
Future Studies ◽  
Neuroimaging Data ◽  
Age Range ◽  
The Impact ◽  
The Brain ◽  
Healthy Participants

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or masking pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from e.g. diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19-54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After differing preprocessing steps, 3D-fractional anisotropy (FA) maps obtained by tensor fitting were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Preprocessing including unringing reduced the Gibbs ringing artefact, and head motion estimates were significantly lower at Skyra. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

scholarly journals Revisiting the global workspace orchestrating the hierarchical organization of the human brain

2021 ◽  
Author(s):  
Gustavo Deco ◽  
Diego Vidaurre ◽  
Morten L. Kringelbach
Keyword(s):  
Human Brain ◽  
Brain Regions ◽  
Hierarchical Organization ◽  
Long Term Memory ◽  
Whole Brain ◽  
Rich Club ◽  
Global Workspace ◽  
Neuroimaging Data ◽  
The Brain

AbstractA central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a ‘global workspace’, but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common ‘functional rich club’. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.

Sign in / Sign up

Export Citation Format

Share Document