Multivariate decoding of fMRI data

e-Neuroforum ◽  
2012 ◽  
Vol 18 (1) ◽  
pp. 1-16 ◽  
Author(s):  
J. Heinzle ◽  
S. Anders ◽  
S. Bode ◽  
C. Bogler ◽  
Y. Chen ◽  
...  
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Cognitive Task ◽  
Brain Regions ◽  
Medical Diagnostics ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Analysis Techniques ◽  
Flow Surface

AbstractThe advent of functional magnetic resonance imaging (fMRI) of brain function 20 years ago has provided a new methodology for non-in­vasive measurement of brain function that is now widely used in cognitive neurosci­ence. Traditionally, fMRI data has been an­alyzed looking for overall activity chang­es in brain regions in response to a stimu­lus or a cognitive task. Now, recent develop­ments have introduced more elaborate, con­tent-based analysis techniques. When mul­tivariate decoding is applied to the detailed patterning of regionally-specific fMRI signals, it can be used to assess the amount of infor­mation these encode about specific task-vari­ables. Here we provide an overview of sev­eral developments, spanning from applica­tions in cognitive neuroscience (perception, attention, reward, decision making, emotion­al communication) to methodology (informa­tion flow, surface-based searchlight decod­ing) and medical diagnostics.

New FMRI Methods for the Study of Language

2018 ◽  
pp. 974-991
Author(s):  
Roel M. Willems ◽  
Marcel A. J. van Gerven
Keyword(s):  
Magnetic Resonance Imaging ◽  
Cognitive Neuroscience ◽  
State Of The Art ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Analysis Techniques ◽  
Important Message ◽  
Language Research ◽  
The Brain

The use of various techniques for measuring brain activation has led to a dramatic increase in knowledge about how the brain is involved in language. One of these techniques is functional magnetic resonance imaging (fMRI). This chapter describes ways of analyzing data that take away some of the classical limitations of fMRI. One important message from the chapter is that improved analysis techniques allow for the use of more naturalistic and continuously presented stimuli like spoken narratives or movies, than was considered possible before. Part 1 describes how some traditional limitations of fMRI for language research can relatively easily be overcome. In part 2, state-of-the-art approaches for the analysis of fMRI data are examined. It is intended that the description of these techniques will be inspirational for those who want to perform cognitive neuroscience studies of language, most notably at the level of discourse.

scholarly journals Further results on why a point process is effective for estimating correlation between brain regions

2020 ◽  
Vol 12 ◽  
pp. 120003
Author(s):  
I. Cifre ◽  
M. Zarepour ◽  
S. G. Horovitz ◽  
S. A. Cannas ◽  
D. R. Chialvo
Keyword(s):  
Point Process ◽  
Large Fraction ◽  
Temporal Correlation ◽  
Brain Regions ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Processing Scheme ◽  
Inflection Points ◽  
Correlation Properties

Signals from brain functional magnetic resonance imaging (fMRI) can be efficiently represented by a sparse spatiotemporal point process, according to a recently introduced heuristic signal processing scheme. This approach has already been validated for relevant conditions, demonstrating that it preserves and compresses a surprisingly large fraction of the signal information. Here we investigated the conditions necessary for such an approach to succeed, as well as the underlying reasons, using real fMRI data and a simulated dataset. The results show that the key lies in the temporal correlation properties of the time series under consideration. It was found that signals with slowly decaying autocorrelations are particularly suitable for this type of compression, where inflection points contain most of the information.

Pharmacological fMRI: A New Tool for Drug Development in Humans

2001 ◽  
Vol 14 (5) ◽  
pp. 368-375 ◽  
Author(s):  
Irene Tracey ◽  
Richard G. Wise
Keyword(s):  
Magnetic Resonance Imaging ◽  
Drug Development ◽  
Brain Function ◽  
Brain Regions ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
New Era ◽  
Human Pharmacology ◽  
Site Of Action ◽  
Over Time

Until now, our understanding of human brain pharmacology has depended on indirect assessments or animal models. The advent of pharmacological functional magnetic resonance imaging (phMRI) has enabled researchers to focus directly on human pharmacology and brain function. Functional MRI, with its increased spatial and temporal resolution, has a further advantage over other neuroimaging methods in that being totally noninvasive, it allows serial, longitudinal studies to be performed on the same subject. This opens the door to a new era of phMRI, as the effects of drugs can be readily monitored in one subject (control or patient) over time. In addition, sophisticated paradigms can be developed that can isolate specific brain regions of activation. These regions can then be subsequently targeted and challenged with appropriate drugs. This allows for a “battery” of paradigms aimed at determining a drug’s mechanism and site of action, which would be valuable for drug development and discovery.

Cerebral plasticity in multiple sclerosis: insights from fMRI

2002 ◽  
Vol 8 (3) ◽  
pp. 193-199 ◽  
Author(s):  
A Cifelli ◽  
P M Matthews
Keyword(s):  
Multiple Sclerosis ◽  
Cognitive Neuroscience ◽  
Brain Function ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Clinical Expression ◽  
Cerebral Activation ◽  
Cerebral Plasticity ◽  
Neural Substrate ◽  
The Relationship

Functional magnetic resonance imaging (fMRI) allows noninvasive localization of cerebral activation with relatively high spatial and temporal resolution. The considerable potential for the elucidation of the mechanisms of brain function has made it a useful tool to investigate the neural substrate of motor, sensory and cognitive functions. Understanding derived from these basic cognitive neuroscience investigations is beginning to be applied to clinically relevant problems. In this article, applications to multiple sclerosis (MS) are reviewed, which address the challenging notion that adaptive cerebral plasticity may have an important influence on the relationship between MS pathology and its clinical expression.

scholarly journals High-order interdependencies in the aging brain

2020 ◽  
Author(s):  
Marilyn Gatica ◽  
Rodrigo Cofré ◽  
Pedro A.M. Mediano ◽  
Fernando E. Rosas ◽  
Patricio Orio ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Brain Regions ◽  
High Order ◽  
Fmri Data ◽  
Aging Brain ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Promising Tool ◽  
Older Subjects ◽  
Healthy Participants

AbstractBrain interdependencies can be studied either from a structural/anatomical perspective (“structural connectivity”, SC) or by considering statistical interdependencies (“functional connectivity”, FC). Interestingly, while SC is typically pairwise (white-matter fibers start in a certain region and arrive at another), FC is not; however, most FC analyses focus only on pairwise statistics and neglect high-order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and redundancy in groups of three or more interacting variables. In this paper we used the O-Information to investigate how high-order statistical interdependencies are affected by age. For this, we analised functional magnetic resonance imaging (fMRI) data at rest obtained from 164 healthy participants, ranging from 10 to 80 years old. Our results show that older subjects (age ranging from 60 to 80 years) exhibit a higher predominance of redundant dependencies than younger subjects; moreover, this effect seems to be pervasive, taking place at all interaction orders. Additionally, we found that these effects are highly heterogeneous across brain regions, and suggest the existence of a “redundancy core” formed by the prefrontal and motor cortices – thus involving functions such as working memory, executive and motor functions. Our methodology to assess high-order interdependencies in fMRI data has unlimited applications. The code to calculate these metrics is freely available.

scholarly journals Dissociating tinnitus patients from healthy controls using resting-state cyclicity analysis and clustering

2019 ◽  
Vol 3 (1) ◽  
pp. 67-89 ◽  
Author(s):  
Benjamin J. Zimmerman ◽  
Ivan Abraham ◽  
Sara A. Schmidt ◽  
Yuliy Baryshnikov ◽  
Fatima T. Husain
Keyword(s):  
Resting State ◽  
Classification Accuracy ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Physiological Models ◽  
Analysis Stability

Chronic tinnitus is a common and sometimes debilitating condition that lacks scientific consensus on physiological models of how the condition arises as well as any known cure. In this study, we applied a novel cyclicity analysis, which studies patterns of leader-follower relationships between two signals, to resting-state functional magnetic resonance imaging (rs-fMRI) data of brain regions acquired from subjects with and without tinnitus. Using the output from the cyclicity analysis, we were able to differentiate between these two groups with 58–67% accuracy by using a partial least squares discriminant analysis. Stability testing yielded a 70% classification accuracy for identifying individual subjects’ data across sessions 1 week apart. Additional analysis revealed that the pairs of brain regions that contributed most to the dissociation between tinnitus and controls were those connected to the amygdala. In the controls, there were consistent temporal patterns across frontal, parietal, and limbic regions and amygdalar activity, whereas in tinnitus subjects, this pattern was much more variable. Our findings demonstrate a proof-of-principle for the use of cyclicity analysis of rs-fMRI data to better understand functional brain connectivity and to use it as a tool for the differentiation of patients and controls who may differ on specific traits.

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

scholarly journals Neural substrates of propranolol-induced impairments in the reconsolidation of nicotine-associated memories in smokers

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiao Lin ◽  
Jiahui Deng ◽  
Kai Yuan ◽  
Qiandong Wang ◽  
Lin Liu ◽  
...  
Keyword(s):  
Neural Activity ◽  
Neural Substrates ◽  
Brain Regions ◽  
Reward Processing ◽  
Memory Reconsolidation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Postcentral Gyrus ◽  
Propranolol Group ◽  
Propranolol Administration

AbstractThe majority of smokers relapse even after successfully quitting because of the craving to smoking after unexpectedly re-exposed to smoking-related cues. This conditioned craving is mediated by reward memories that are frequently experienced and stubbornly resistant to treatment. Reconsolidation theory posits that well-consolidated memories are destabilized after retrieval, and this process renders memories labile and vulnerable to amnestic intervention. This study tests the retrieval reconsolidation procedure to decrease nicotine craving among people who smoke. In this study, 52 male smokers received a single dose of propranolol (n = 27) or placebo (n = 25) before the reactivation of nicotine-associated memories to impair the reconsolidation process. Craving for smoking and neural activity in response to smoking-related cues served as primary outcomes. Functional magnetic resonance imaging was performed during the memory reconsolidation process. The disruption of reconsolidation by propranolol decreased craving for smoking. Reactivity of the postcentral gyrus in response to smoking-related cues also decreased in the propranolol group after the reconsolidation manipulation. Functional connectivity between the hippocampus and striatum was higher during memory reconsolidation in the propranolol group. Furthermore, the increase in coupling between the hippocampus and striatum positively correlated with the decrease in craving after the reconsolidation manipulation in the propranolol group. Propranolol administration before memory reactivation disrupted the reconsolidation of smoking-related memories in smokers by mediating brain regions that are involved in memory and reward processing. These findings demonstrate the noradrenergic regulation of memory reconsolidation in humans and suggest that adjunct propranolol administration can facilitate the treatment of nicotine dependence. The present study was pre-registered at ClinicalTrials.gov (registration no. ChiCTR1900024412).

Sign in / Sign up

Export Citation Format

Share Document