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 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 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.

Sign in / Sign up

Export Citation Format

Share Document