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.

Statistical Analysis of Functional Magnetic Resonance Imaging Data

2017 ◽  
pp. 103-114
Author(s):  
Nicole A. Lazar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Temporal Correlation ◽  
Fmri Data ◽  
Imaging Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

The analysis of functional magnetic resonance imaging (fMRI) data poses many statistical challenges. The data are massive, noisy, and have a complicated spatial and temporal correlation structure. This chapter introduces the basics of fMRI data collection and surveys common approaches for data analysis.

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.

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).

scholarly journals Globally conditioned Granger causality in brain–brain and brain–heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

2016 ◽  
Vol 374 (2067) ◽  
pp. 20150185 ◽  
Author(s):  
Andrea Duggento ◽  
Marta Bianciardi ◽  
Luca Passamonti ◽  
Lawrence L. Wald ◽  
Maria Guerrisi ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Resting State ◽  
High Field ◽  
Brain Activity ◽  
Brain Regions ◽  
Sympathovagal Balance ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Ultra High Field ◽  
Conditioned Approach

The causal, directed interactions between brain regions at rest (brain–brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain–heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain–brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain–brain and brain–heart interactions reflecting central modulation of ANS outflow.

Hedonic-Specific Activity in Piriform Cortex During Odor Imagery Mimics That During Odor Perception

2007 ◽  
Vol 98 (6) ◽  
pp. 3254-3262 ◽  
Author(s):  
Moustafa Bensafi ◽  
Noam Sobel ◽  
Rehan M. Khan
Keyword(s):  
Specific Activity ◽  
Piriform Cortex ◽  
Brain Regions ◽  
Specific Pattern ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Odor Perception ◽  
Cortical Areas ◽  
Visual Cortical ◽  
Left Insula

Although it is known that visual imagery is accompanied by activity in visual cortical areas, including primary visual cortex, whether olfactory imagery exists remains controversial. Here we asked whether cue-dependent olfactory imagery was similarly accompanied by activity in olfactory cortex, and in particular whether hedonic-specific patterns of activity evident in olfactory perception would also be present during olfactory imagery. We used functional magnetic resonance imaging to measure activity in subjects who alternated between smelling and imagining pleasant and unpleasant odors. Activity induced by imagining odors mimicked that induced by perceiving real odorants, not only in the particular brain regions activated, but also in its hedonic-specific pattern. For both real and imagined odors, unpleasant stimuli induced greater activity than pleasant stimuli in the left frontal portion of piriform cortex and left insula. These findings combine with findings from other modalities to suggest activation of primary sensory cortical structures during mental imagery of sensory events.

scholarly journals Increased sensitivity to strong perturbations in a whole-brain model of LSD

2021 ◽  
Author(s):  
Beatrice M. Jobst ◽  
Selen Atasoy ◽  
Adrián Ponce-Alvarez ◽  
Ana Sanjuán ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
External Perturbation ◽  
Brain Regions ◽  
Lysergic Acid ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Whole Brain ◽  
Response Diversity ◽  
Brain States ◽  
The Brain

AbstractLysergic acid diethylamide (LSD) is a potent psychedelic drug, which has seen a revival in clinical and pharmacological research within recent years. Human neuroimaging studies have shown fundamental changes in brain-wide functional connectivity and an expansion of dynamical brain states, thus raising the question about a mechanistic explanation of the dynamics underlying these alterations. Here, we applied a novel perturbational approach based on a whole-brain computational model, which opens up the possibility to externally perturb different brain regions in silico and investigate differences in dynamical stability of different brain states, i.e. the dynamical response of a certain brain region to an external perturbation. After adjusting the whole-brain model parameters to reflect the dynamics of functional magnetic resonance imaging (fMRI) BOLD signals recorded under the influence of LSD or placebo, perturbations of different brain areas were simulated by either promoting or disrupting synchronization in the regarding brain region. After perturbation offset, we quantified the recovery characteristics of the brain area to its basal dynamical state with the Perturbational Integration Latency Index (PILI) and used this measure to distinguish between the two brain states. We found significant changes in dynamical complexity with consistently higher PILI values after LSD intake on a global level, which indicates a shift of the brain’s global working point further away from a stable equilibrium as compared to normal conditions. On a local level, we found that the largest differences were measured within the limbic network, the visual network and the default mode network. Additionally, we found a higher variability of PILI values across different brain regions after LSD intake, indicating higher response diversity under LSD after an external perturbation. Our results provide important new insights into the brain-wide dynamical changes underlying the psychedelic state - here provoked by LSD intake - and underline possible future clinical applications of psychedelic drugs in particular psychiatric disorders.HighlightsNovel offline perturbational method applied on functional magnetic resonance imaging (fMRI) data under the effect of lysergic acid diethylamide (LSD)Shift of brain’s global working point to more complex dynamics after LSD intakeConsistently longer recovery time after model perturbation under LSD influenceStrongest effects in resting state networks relevant for psychedelic experienceHigher response diversity across brain regions under LSD influence after an external in silico perturbation

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