scholarly journals Examining Similarity Structure: Multidimensional Scaling and Related Approaches in Neuroimaging

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Svetlana V. Shinkareva ◽  
Jing Wang ◽  
Douglas H. Wedell
Keyword(s):  
Multidimensional Scaling ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Multivariate Pattern Analysis ◽  
Imaging Data ◽  
Magnetic Resonance Imaging Data ◽  
Analysis Techniques ◽  
Similarity Structure ◽  
Brain Data ◽  
Multivariate Pattern

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.

scholarly journals A heuristic feature cluster search algorithm for precise functional brain mapping

2019 ◽  
Author(s):  
Nima Asadi ◽  
Yin Wang ◽  
Ingrid Olson ◽  
Zoran Obradovic
Keyword(s):  
Pattern Analysis ◽  
Search Algorithm ◽  
Search Space ◽  
Brain Regions ◽  
Information Value ◽  
Multivariate Pattern Analysis ◽  
Functional Brain Mapping ◽  
Optimization Parameters ◽  
Synthetic Datasets ◽  
Multivariate Pattern

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.

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

2016 ◽  
Vol 39 ◽  
Author(s):  
Carolyn Parkinson ◽  
Thalia Wheatley
Keyword(s):  
Cognitive Neuroscience ◽  
Brain Function ◽  
Pattern Analysis ◽  
Brain Regions ◽  
Neural Population ◽  
Multivariate Pattern Analysis ◽  
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.

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

2012 ◽  
Vol 24 (9) ◽  
pp. 1896-1907 ◽  
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 ◽  
Multivariate Pattern Analysis ◽  
Acoustic Feature ◽  
Complex Sounds ◽  
Human Auditory Cortex ◽  
Multivariate Pattern

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.

scholarly journals Multivariate Pattern Analysis Techniques for Electroencephalography Data to Study Flanker Interference Effects

2020 ◽  
Vol 30 (07) ◽  
pp. 2050024
Author(s):  
David López-García ◽  
Alberto Sobrado ◽  
José M. G. Peñalver ◽  
Juan Manuel Górriz ◽  
María Ruz
Keyword(s):  
Pattern Analysis ◽  
Initial Approximation ◽  
Neural Mechanisms ◽  
Multivariate Pattern Analysis ◽  
Time Frequency ◽  
Analysis Techniques ◽  
New Information ◽  
Analytical Strategies ◽  
Underlying Mechanisms ◽  
Multivariate Pattern

A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task (DST) to a format that allows measuring concurrent high-density electroencephalography (EEG). We used multivariate pattern analysis (MVPA) to decode conflict-related electrophysiological markers associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not have been obtained with classic analytical strategies, open novel avenues of research.

scholarly journals Multivariate pattern analysis techniques for electroencephalography data to study interference effects

2019 ◽  
Author(s):  
David López-García ◽  
Alberto Sobrado ◽  
José M. G. Peñalver ◽  
Juan Manuel Górriz ◽  
María Ruz
Keyword(s):  
Pattern Analysis ◽  
Initial Approximation ◽  
Neural Mechanisms ◽  
Multivariate Pattern Analysis ◽  
Time Frequency ◽  
Analysis Techniques ◽  
New Information ◽  
Analytical Strategies ◽  
Underlying Mechanisms ◽  
Multivariate Pattern

AbstractA central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task to a format that allows measuring concurrent high-density electroencephalography. We used multivariate pattern analysis (MVPA) to decode conflictrelated neural processes associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not had been obtained with classic analytical strategies, open novel avenues of research.

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