Reason for optimism: How a shifting focus on neural population codes is moving cognitive neuroscience beyond phrenology

Population Responses ◽

Population Codes ◽

Multivariate Pattern ◽

Insight Into

AbstractMultivariate pattern analysis can address many of the challenges for cognitive neuroscience highlighted in After Phrenology (Anderson 2014) by illuminating the information content of brain regions and by providing insight into whether functional overlap reflects the recruitment of common or distinct computational mechanisms. Further, failing to consider submaximal but reliable population responses can lead to an overly modular account of brain function.

A heuristic feature cluster search algorithm for precise functional brain mapping

10.1101/518480 ◽

2019 ◽

Author(s):

Nima Asadi ◽

Yin Wang ◽

Ingrid Olson ◽

Zoran Obradovic

Keyword(s):

Pattern Analysis ◽

Search Algorithm ◽

Search Space ◽

Brain Regions ◽

Information Value ◽

Functional Brain Mapping ◽

Optimization Parameters ◽

Synthetic Datasets ◽

ABSTRACTDetecting the most relevant brain regions for explaining the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis (MVPA) which is commonly conducted through the searchlight procedure as well as a number of other approaches. This is due to advantages of such methods which include being intuitive and flexible with regards to size of the search space. However, these approaches suffer from a number of limitations that lead to misidentification of truly informative voxels or clusters of voxels which in turn results in imprecise information maps. The limitations of such procedures mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape and other optimization parameters, overlooking the structure and interactions within the regions, and the drawbacks of using regularization methods in analysis of datasets with characteristics of common fMRI data. In this paper, we propose a fully data-driven maximum relevance minimum redundancy search algorithm for detecting precise information value of voxel-level clusters within brain regions while alleviating the above mentioned limitations. In order to make the algorithm efficient, we propose an implementation based on principles of dynamic programming. We evaluate and compare the proposed algorithm with the searchlight procedure using both real and synthetic datasets.

Decoding the Neural Representation of Self and Person Knowledge with Multivariate Pattern Analysis and Data Driven Approaches

10.31234/osf.io/wr6gq ◽

2018 ◽

Author(s):

Dylan D. Wagner ◽

Robert Chavez ◽

Timothy W. Broom

Keyword(s):

Cognitive Neuroscience ◽

Conceptual Knowledge ◽

Person Perception ◽

Pattern Analysis ◽

Social Groups ◽

The Self ◽

Neural Representation ◽

Data Driven ◽

Multivariate pattern analysis and data driven approaches to understanding how the human brain encodes sensory information and higher level conceptual knowledge have become increasingly dominant in visual and cognitive neuroscience, however it is only in recent years that these methods have been applied to the domain of social information processing. This review examines recent research in the field of social cognitive neuroscience focusing on how multivariate pattern analysis (e.g., pattern classification, representational similarity analysis) and data-driven methods (e.g, reverse correlation, intersubject correlation) have been used to decode and characterize high-level information about the self, other persons and social groups. We begin with a review of what is known about how self-referential processing and person perception are represented in the medial prefrontal cortex based on conventional activation-based neuroimaging approaches. This is followed by a non-technical overview of current multivariate pattern-based and data-driven neuroimaging methods designed to characterize and/or decode neural representations. The remainder of the review focuses on examining how these methods have been applied to the topic of self, person perception and the perception of social groups. Throughout, we highlight recent trends (e.g., analysis of social networks, decoding race and social groups, the use of naturalistic stimuli) and discuss several theoretical challenges that arise from the application of these new methods to the question of how the brain represents knowledge about the self and others.

FM-selective Networks in Human Auditory Cortex Revealed Using fMRI and Multivariate Pattern Classification

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00254 ◽

2012 ◽

Vol 24 (9) ◽

pp. 1896-1907 ◽

Cited By ~ 9

Author(s):

I-Hui Hsieh ◽

Paul Fillmore ◽

Feng Rong ◽

Gregory Hickok ◽

Kourosh Saberi

Keyword(s):

Auditory Cortex ◽

Pattern Analysis ◽

Primary Auditory Cortex ◽

Brain Regions ◽

Direction Selectivity ◽

Acoustic Feature ◽

Complex Sounds ◽

Human Auditory Cortex ◽

Frequency modulation (FM) is an acoustic feature of nearly all complex sounds. Directional FM sweeps are especially pervasive in speech, music, animal vocalizations, and other natural sounds. Although the existence of FM-selective cells in the auditory cortex of animals has been documented, evidence in humans remains equivocal. Here we used multivariate pattern analysis to identify cortical selectivity for direction of a multitone FM sweep. This method distinguishes one pattern of neural activity from another within the same ROI, even when overall level of activity is similar, allowing for direct identification of FM-specialized networks. Standard contrast analysis showed that despite robust activity in auditory cortex, no clusters of activity were associated with up versus down sweeps. Multivariate pattern analysis classification, however, identified two brain regions as selective for FM direction, the right primary auditory cortex on the supratemporal plane and the left anterior region of the superior temporal gyrus. These findings are the first to directly demonstrate existence of FM direction selectivity in the human auditory cortex.

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

The British Journal for the Philosophy of Science ◽

10.1093/bjps/axx023 ◽

2019 ◽

Vol 70 (2) ◽

pp. 581-607 ◽

Cited By ~ 25

Author(s):

J. Brendan Ritchie ◽

David Michael Kaplan ◽

Colin Klein

Keyword(s):

Cognitive Neuroscience ◽

Pattern Analysis ◽

Neural Representation ◽

Multivariate Pattern ◽

The Brain

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

10.1101/127233 ◽

2017 ◽

Cited By ~ 3

Author(s):

J. Brendan Ritchie ◽

David Michael Kaplan ◽

Colin Klein

Keyword(s):

Cognitive Neuroscience ◽

Strong Evidence ◽

Neural Activity ◽

Pattern Analysis ◽

Neural Representation ◽

Neural Decoding ◽

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.

Examining Similarity Structure: Multidimensional Scaling and Related Approaches in Neuroimaging

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/796183 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Svetlana V. Shinkareva ◽

Jing Wang ◽

Douglas H. Wedell

Keyword(s):

Multidimensional Scaling ◽

Pattern Analysis ◽

Brain Regions ◽

Imaging Data ◽

Magnetic Resonance Imaging Data ◽

Analysis Techniques ◽

Similarity Structure ◽

Brain Data ◽

This paper covers similarity analyses, a subset of multivariate pattern analysis techniques that are based on similarity spaces defined by multivariate patterns. These techniques offer several advantages and complement other methods for brain data analyses, as they allow for comparison of representational structure across individuals, brain regions, and data acquisition methods. Particular attention is paid to multidimensional scaling and related approaches that yield spatial representations or provide methods for characterizing individual differences. We highlight unique contributions of these methods by reviewing recent applications to functional magnetic resonance imaging data and emphasize areas of caution in applying and interpreting similarity analysis methods.

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01068 ◽

2017 ◽

Vol 29 (4) ◽

pp. 677-697 ◽

Cited By ~ 176

Author(s):

Tijl Grootswagers ◽

Susan G. Wardle ◽

Thomas A. Carlson

Keyword(s):

Time Series ◽

Cognitive Neuroscience ◽

Pattern Analysis ◽

Activation Patterns ◽

Cognitive States ◽

Classifier Selection ◽

Computer Interfaces ◽

Neuroimaging Data ◽

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.