scholarly journals More is less: increased processing of unwanted memories facilitates forgetting

2018 ◽  
Author(s):  
Tracy H. Wang ◽  
Katerina Placek ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prefrontal Cortex ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Temporal Cortex ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Alternative Account ◽  
Ventral Temporal Cortex

ABSTRACTThe intention to forget can produce long-lasting effects. This ability has been linked to suppression of both rehearsal and retrieval of unwanted memories – processes that are mediated by prefrontal cortex and hippocampus. Here, we describe an alternative account of deliberate forgetting in which the intention to forget is associated with increased engagement with the unwanted information. We used pattern classifiers to decode functional magnetic resonance imaging (fMRI) data from a task in which participants viewed a series of pictures and were instructed to remember or forget each one. Pictures followed by a forget instruction elicited higher levels of processing in ventral temporal cortex compared to those followed by a remember instruction. This boost in processing led to more forgetting, particularly for items that showed moderate (vs. weak or strong) activation. This result is consistent with the non-monotonic plasticity hypothesis, which predicts weakening and forgetting of memories that are moderately activated.

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.

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.

Prefrontal Cortex Involvement in Selective Letter Generation: A Functional Magnetic Resonance Imaging Study

Cortex ◽  
1998 ◽  
Vol 34 (3) ◽  
pp. 389-401 ◽  
Author(s):  
Greig I. de Zubicaray ◽  
Steven C.R. Williams ◽  
Stephen J. Wilson ◽  
Stephen E. Rose ◽  
Michael J. Brammer ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prefrontal Cortex ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Imaging Study ◽  
Magnetic Resonance Imaging Study ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

Language Functional Magnetic Resonance Imaging in Preoperative Assessment of Language Areas: Correlation with Direct Cortical Stimulation

Neurosurgery ◽  
2003 ◽  
Vol 52 (6) ◽  
pp. 1335-1347 ◽  
Author(s):  
Franck-Emmanuel Roux ◽  
Kader Boulanouar ◽  
Jean-Albert Lotterie ◽  
Mehdi Mejdoubi ◽  
James P. LeSage ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Left Hemisphere ◽  
Brain Mapping ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Verb Generation ◽  
Language Areas

Abstract OBJECTIVE The aim of this study was to analyze the usefulness of preoperative language functional magnetic resonance imaging (fMRI), by correlating fMRI data with intraoperative cortical stimulation results for patients with brain tumors. METHODS Naming and verb generation tasks were used, separately or in combination, for 14 right-handed patients with tumors in the left hemisphere. fMRI data obtained were analyzed with SPM software, with two standard analysis thresholds (P < 0.005 and then P < 0.05). The fMRI data were then registered in a frameless stereotactic neuronavigational device and correlated with direct brain mapping results. We used a statistical model with the fMRI information as a predictor, spatially correlating each intraoperatively mapped cortical site with fMRI data integrated in the neuronavigational system (site-by-site correlation). Eight patients were also studied with language fMRI postoperatively, with the same acquisition protocol. RESULTS We observed high variability in signal extents and locations among patients with both tasks. The activated areas were located mainly in the left hemisphere in the middle and inferior frontal gyri (F2 and F3), the superior and middle temporal gyri (T1 and T2), and the supramarginal and angular gyri. A total of 426 cortical sites were tested for each task among the 14 patients. In frontal and temporoparietal areas, poor sensitivity of the fMRI technique was observed for the naming and verb generation tasks (22 and 36%, respectively) with P < 0.005 as the analysis threshold. Although not perfect, the specificity of the fMRI technique was good in all conditions (97% for the naming task and 98% for the verb generation task). Better correlation (sensitivity, 59%; specificity, 97%) was achieved by combining the two fMRI tasks. Variation of the analysis threshold to P < 0.05 increased the sensitivity to 66% while decreasing the specificity to 91%. Postoperative fMRI data (for the cortical brain areas studied intraoperatively) were in accordance with brain mapping results for six of eight patients. Complete agreement between pre- and postoperative fMRI studies and direct brain mapping results was observed for only three of eight patients. CONCLUSION With the paradigms and analysis thresholds used in this study, language fMRI data obtained with naming or verb generation tasks, before and after surgery, were imperfectly correlated with intraoperative brain mapping results. A better correlation could be obtained by combining the fMRI tasks. The overall results of this study demonstrated that language fMRI could not be used to make critical surgical decisions in the absence of direct brain mapping. Other acquisition protocols are required for evaluation of the potential role of language fMRI in the accurate detection of essential cortical language areas.

scholarly journals Understanding multivariate brain activity: evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging

2019 ◽  
Author(s):  
Ru-Yuan Zhang ◽  
Xue-Xin Wei ◽  
Kendrick Kay
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Brain Activity ◽  
Neuronal Population ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Correlated Activity ◽  
Population Codes

ABSTRACTPrevious studies have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We first replicate the classical finding that TCNCs impair population codes in a standard neuronal population. We then extend our analysis to fMRI data, and show that voxelwise TCNCs do not impair and can even improve MVPA performance when TCNCs are strong or the number of voxels is large. We also confirm these results using standard information-theoretic analyses in computational neuroscience. Further computational analyses demonstrate that the discrepancy between the effect of TCNCs in neuronal and voxel populations can be explained by tuning heterogeneity and pool sizes. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

scholarly journals Estimates of locus coeruleus function with functional magnetic resonance imaging are influenced by localization approaches and the use of multi-echo data

2019 ◽  
Author(s):  
Hamid B. Turker ◽  
Elizabeth Riley ◽  
Wen-Ming Luh ◽  
Stan J. Colcombe ◽  
Khena M. Swallow
Keyword(s):  
Magnetic Resonance Imaging ◽  
Time Series ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Locus Coeruleus ◽  
Signal To Noise Ratio ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Signal To Noise

AbstractThe locus coeruleus (LC) plays a central role in regulating human cognition, arousal, and autonomic states. Efforts to characterize the LC’s function in humans using functional magnetic resonance imaging have been hampered by its small size and location near a large source of noise, the fourth ventricle. We tested whether the ability to characterize LC function is improved by employing neuromelanin-T1 weighted images (nmT1) for LC localization and multi-echo functional magnetic resonance imaging (ME-fMRI) for estimating intrinsic functional connectivity (iFC). Analyses indicated that, relative to a probabilistic atlas, utilizing nmT1 images to individually localize the LC increases the specificity of seed time series and clusters in the iFC maps. When combined with independent components analysis (ME-ICA), ME-fMRI data provided significant gains in the temporal signal to noise ratio relative to denoised single-echo (1E) data. The effects of acquiring nmT1 images and ME-fMRI data did not appear to only reflect increases in power: iFC maps for each approach only moderately overlapped. This is consistent with findings that ME-fMRI offers substantial advantages over 1E data acquisition and denoising. It also suggests that individually identifying LC with nmT1 scans is likely to reduce the influence of other nearby brainstem regions on estimates of LC function.HighlightsManual tracing of locus coeruleus increased specificity of seed time seriesManual tracing of locus coeruleus increased specificity of intrinsic connectivityMulti-echo fMRI increased temporal signal-to-noise ratio compared to single-echo fMRIConnectivity maps across methodologies overlapped only moderatelyMeasurement of LC function benefits from multi-echo fMRI and tracing ROIs

scholarly journals Multiscale Entropy of Resting-State Functional Magnetic Resonance Imaging Differentiates Progressive Supranuclear Palsy and Multiple System Atrophy

Life ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1411
Author(s):  
Katsuhiko Kadota ◽  
Keiichi Onoda ◽  
Satoshi Abe ◽  
Chizuko Hamada ◽  
Shingo Mitaki ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prefrontal Cortex ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Multiple System Atrophy ◽  
Resting State ◽  
Multiscale Entropy ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Reduced Complexity

Distinguishing progressive supranuclear palsy (PSP) from multiple system atrophy (MSA) in the early clinical stages is challenging; few sensitive and specific biomarkers are available for their differential diagnosis. Resting-state functional magnetic resonance imaging (rs-fMRI) is used to study the fluctuations in blood oxygen level-dependent (BOLD) signals at rest, which provides evidence for aberrant brain functional networks in neurodegenerative diseases. We aimed to examine whether rs-fMRI data could differentiate between PSP and MSA via a multiscale entropy (MSE) analysis of BOLD signals, which estimates the complexity of temporal fluctuations in brain activity. We recruited 14 and 18 patients with PSP and MSA, respectively, who underwent neuropsychological tests and rs-fMRI. PSP patients demonstrated greater cognitive function impairments, particularly in the frontal executive function. The bilateral prefrontal cortex revealed lower entropy BOLD signal values in multiple time scales for PSP, compared to the values observed in MSA patients; however, the functional connectivity of the representative brain networks was comparable between the diseases. The reduced complexity of BOLD signals in the prefrontal cortex was associated with frontal dysfunction. Thus, an MSE analysis of rs-fMRI could differentiate between PSP and MSA, and the reduced complexity of BOLD signals could be associated with cognitive impairment.

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