scholarly journals Multivariate pattern analysis and the search for neural representations

Synthese ◽  
2021 ◽  
Author(s):  
Bryce Gessell ◽  
Benjamin Geib ◽  
Felipe De Brigard
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

scholarly journals Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Temporal Information ◽  
Single Subject ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
Over Time

AbstractBackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.ConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

scholarly journals Attention Biases Competition for Visual Representation via Dissociable Influences from Frontal and Parietal Cortex

2021 ◽  
pp. 1-17
Author(s):  
Andrew D. Sheldon ◽  
Elyana Saad ◽  
Muhammet I. Sahan ◽  
Emma E. Meyering ◽  
Michael J. Starrett ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Attentional Control ◽  
Pattern Analysis ◽  
Relevant Information ◽  
Time Varying ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Change State ◽  
Multivariate Pattern ◽  
Central Fixation

What mechanisms underlie the prioritization of neural representations of visually perceived information to guide behavior? We assessed the dynamics whereby attention biases competition for representation of visual stimuli by enhancing representations of relevant information and suppressing the irrelevant. Multivariate pattern analysis (MVPA) classifiers were trained to discriminate patterns of fMRI activity associated with each of three stimuli, within several predefined ROIs. Participants performed a change-detection task wherein two of three presented items flashed at 1 Hz, one to each side of central fixation. Both flashing stimuli would unpredictably change state, but participants covertly counted the number of changes only for the cued item. In the ventral occipito-temporal ROI, MVPA evidence (a proxy for representational fidelity) was dynamically enhanced for attended stimuli and suppressed for unattended stimuli, consistent with a mechanism of biased competition between stimulus representations. Frontal and parietal ROIs displayed a qualitatively distinct, more “source-like” profile, wherein MVPA evidence for only the attended stimulus could be observed above baseline levels. To assess how attentional modulation of ventral occipito-temporal representations might relate to signals originating in the frontal and/or parietal ROIs, we analyzed informational connectivity (IC), which indexes time-varying covariation between regional levels of MVPA evidence. Parietal-posterior IC was elevated during the task, but did not differ for cued versus uncued items. Frontal-posterior IC, in contrast, was sensitive to an item's priority status. Thus, although regions of frontal and parietal cortex act as sources of top–down attentional control, their precise functions likely differ.

scholarly journals A Multivariate Pattern Analysis of Metabolic Profile in Neurologically Impaired Children and Adolescents

Children ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 186
Author(s):  
Valeria Calcaterra ◽  
Giacomo Biganzoli ◽  
Gloria Pelizzo ◽  
Hellas Cena ◽  
Alessandra Rizzuto ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Glucose Metabolism ◽  
Children And Adolescents ◽  
Metabolic Profile ◽  
Children With Disabilities ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neurologically Impaired ◽  
Multivariate Pattern ◽  
Impaired Children

Background: The prevalence of pediatric metabolic syndrome is usually closely linked to overweight and obesity; however, this condition has also been described in children with disabilities. We performed a multivariate pattern analysis of metabolic profiles in neurologically impaired children and adolescents in order to reveal patterns and crucial biomarkers among highly interrelated variables. Patients and methods: We retrospectively reviewed 44 cases of patients (25M/19F, mean age 12.9 ± 8.0) with severe disabilities. Clinical and anthropometric parameters, body composition, blood pressure, and metabolic and endocrinological assessment (fasting blood glucose, insulin, total cholesterol, high-density lipoprotein cholesterol, triglycerides, glutamic oxaloacetic transaminase, glutamate pyruvate transaminase, gamma-glutamyl transpeptidase) were recorded in all patients. As a control group, we evaluated 120 healthy children and adolescents (61M/59F, mean age 12.9 ± 2.7). Results: In the univariate analysis, the children-with-disabilities group showed a more dispersed distribution, thus with higher variability of the features related to glucose metabolism and insulin resistance (IR) compared to the healthy controls. The principal component (PC1), which emerged from the PC analysis conducted on the merged dataset and characterized by these variables, was crucial in describing the differences between the children-with-disabilities group and controls. Conclusion: Children and adolescents with disabilities displayed a different metabolic profile compared to controls. Metabolic syndrome (MetS), particularly glucose metabolism and IR, is a crucial point to consider in the treatment and care of this fragile pediatric population. Early detection of the interrelated variables and intervention on these modifiable risk factors for metabolic disturbances play a central role in pediatric health and life expectancy in patients with a severe disability.

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