scholarly journals Is it left or is it right? A classification approach for investigating hemispheric differences in low and high dimensionality

2021 ◽  
Author(s):  
Patrick Friedrich ◽  
Kaustubh R. Patil ◽  
Lisa N. Mochalski ◽  
Xuan Li ◽  
Julia A. Camilleri ◽  
...  
Keyword(s):  
Feature Selection ◽  
Right Hemisphere ◽  
Dimensional Space ◽  
High Dimensional ◽  
Hemispheric Differences ◽  
Dimensional Representation ◽  
Left And Right ◽  
Low Dimensional ◽  
The Right ◽  
And Function

AbstractHemispheric asymmetries, i.e., differences between the two halves of the brain, have extensively been studied with respect to both structure and function. Commonly employed pairwise comparisons between left and right are suitable for finding differences between the hemispheres, but they come with several caveats when assessing multiple asymmetries. What is more, they are not designed for identifying the characterizing features of each hemisphere. Here, we present a novel data-driven framework—based on machine learning-based classification—for identifying the characterizing features that underlie hemispheric differences. Using voxel-based morphometry data from two different samples (n = 226, n = 216), we separated the hemispheres along the midline and used two different pipelines: First, for investigating global differences, we embedded the hemispheres into a two-dimensional space and applied a classifier to assess if the hemispheres are distinguishable in their low-dimensional representation. Second, to investigate which voxels show systematic hemispheric differences, we employed two classification approaches promoting feature selection in high dimensions. The two hemispheres were accurately classifiable in both their low-dimensional (accuracies: dataset 1 = 0.838; dataset 2 = 0.850) and high-dimensional (accuracies: dataset 1 = 0.966; dataset 2 = 0.959) representations. In low dimensions, classification of the right hemisphere showed higher precision (dataset 1 = 0.862; dataset 2 = 0.894) compared to the left hemisphere (dataset 1 = 0.818; dataset 2 = 0.816). A feature selection algorithm in the high-dimensional analysis identified voxels that most contribute to accurate classification. In addition, the map of contributing voxels showed a better overlap with moderate to highly lateralized voxels, whereas conventional t test with threshold-free cluster enhancement best resembled the LQ map at lower thresholds. Both the low- and high-dimensional classifiers were capable of identifying the hemispheres in subsamples of the datasets, such as males, females, right-handed, or non-right-handed participants. Our study indicates that hemisphere classification is capable of identifying the hemisphere in their low- and high-dimensional representation as well as delineating brain asymmetries. The concept of hemisphere classifiability thus allows a change in perspective, from asking what differs between the hemispheres towards focusing on the features needed to identify the left and right hemispheres. Taking this perspective on hemispheric differences may contribute to our understanding of what makes each hemisphere special.

scholarly journals The rodent hippocampus as a bilateral structure: A review of hemispheric lateralization

2017 ◽  
Author(s):  
Jake T. Jordan
Keyword(s):  
Brain Region ◽  
Spatial Information ◽  
Right Hemisphere ◽  
Neural Substrates ◽  
Hemispheric Lateralization ◽  
Hemispheric Differences ◽  
Functional Lateralization ◽  
Left And Right ◽  
The Right ◽  
Behavior And Cognition

AbstractThe left and right rodent hippocampi exhibit striking lateralization in some of the very neural substrates considered to be critical for hippocampal cognitive function. Despite this, there is an overwhelming lack of consideration for hemispheric differences in studies of the rodent hippocampus. Asymmetries identified so far suggest that a bilateral model of the hippocampus will be essential for an understanding of this brain region, and perhaps of the brain more widely. Although hypotheses have been proposed to explain how the left and right hippocampi contribute to behavior and cognition, these hypotheses have either been refuted by more recent studies or have been limited in the scope of data they explain. Here, I will first review data on human and rodent hippocampal lateralization. The implications of these data suggest that considering the hippocampus as a bilateral structure with functional lateralization will be critical moving forward in understanding the function and mechanisms of this brain region. In exploring these implications, I will then propose a hypothesis of the hippocampus as a bilateral structure. This discrete-continuous (DC) hypothesis proposes that the left and right hippocampi contribute to spatial memory and navigation in a complementary manner. Specifically, the left hemisphere stores spatial information as discrete, salient locations and that the right hemisphere represents space continuously, contributing to route computation and flexible spatial navigation. Consideration of hippocampal lateralization in designing future studies may provide insight into the function of the hippocampus and resolve debates concerning its function.

Semantic Networks in the Divided Cerebral Hemispheres

1995 ◽  
Vol 6 (4) ◽  
pp. 212-218 ◽  
Author(s):  
Alice Cronin-Golomb
Keyword(s):  
Right Hemisphere ◽  
Semantic Networks ◽  
Size Composition ◽  
Strongly Correlated ◽  
Hemispheric Differences ◽  
Semantic Organization ◽  
Left And Right ◽  
The Right ◽  
Or Organization ◽  
Visual Hemifields

Hemispheric differences in the recognition and manipulation of meaning may be based on distinctions in size, composition, or organization of the right and left semantic networks The present study describes these features of pictorially based semantic networks in 3 subjects with complete forebrain commissurotomy Stimuli were presented for prolonged viewing to the left and right visual hemifields For each trial, the subjects chose from a 20-choice array all pictures that were associated with a target, then indicated the member of each pair of chosen associates that was more closely related to the target The hemispheres' networks were found to be of similar size and composition, but were organized differently The right hemisphere more often produced linear rankings of semantic associates to a target than did the left, and rankings by the two hemispheres were not strongly correlated Hemispheric differences in semantic organization mirror differences in perceptual organization, with the right hemisphere specialized for conventional meaning and the left hemisphere specialized for detecting and processing deviations from standard meaning

Selective Attention to Faces in a Rapid Visual Stream: Hemispheric Differences in Enhancement and Suppression of Category-selective Neural Activity

2018 ◽  
Vol 30 (3) ◽  
pp. 393-410 ◽  
Author(s):  
Genevieve Quek ◽  
Dan Nemrodov ◽  
Bruno Rossion ◽  
Joan Liu-Shuang
Keyword(s):  
Selective Attention ◽  
Left Hemisphere ◽  
Right Hemisphere ◽  
Hemispheric Differences ◽  
Face Categorization ◽  
The Face ◽  
Left And Right ◽  
The Right ◽  
The Impact ◽  
Selective Activity

In daily life, efficient perceptual categorization of faces occurs in dynamic and highly complex visual environments. Yet the role of selective attention in guiding face categorization has predominantly been studied under sparse and static viewing conditions, with little focus on disentangling the impact of attentional enhancement and suppression. Here we show that attentional enhancement and suppression exert a differential impact on face categorization supported by the left and right hemispheres. We recorded 128-channel EEG while participants viewed a 6-Hz stream of object images (buildings, animals, objects, etc.) with a face image embedded as every fifth image (i.e., OOOOFOOOOFOOOOF…). We isolated face-selective activity by measuring the response at the face presentation frequency (i.e., 6 Hz/5 = 1.2 Hz) under three conditions: Attend Faces, in which participants monitored the sequence for instances of female faces; Attend Objects, in which they responded to instances of guitars; and Baseline, in which they performed an orthogonal task on the central fixation cross. During the orthogonal task, face-specific activity was predominantly centered over the right occipitotemporal region. Actively attending to faces enhanced face-selective activity much more evidently in the left hemisphere than in the right, whereas attending to objects suppressed the face-selective response in both hemispheres to a comparable extent. In addition, the time courses of attentional enhancement and suppression did not overlap. These results suggest the left and right hemispheres support face-selective processing in distinct ways—where the right hemisphere is mandatorily engaged by faces and the left hemisphere is more flexibly recruited to serve current tasks demands.

Face Image Feature Extraction and Feature Selection

2013 ◽  
Vol 432 ◽  
pp. 587-591 ◽  
Author(s):  
Yang Meng Tian ◽  
Yu Duo Zheng ◽  
Wei Jin ◽  
Gai Hong Du
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Feature Vector ◽  
Dimensional Space ◽  
Face Image ◽  
Image Feature ◽  
High Dimensional ◽  
Gabor Feature ◽  
Low Dimensional

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

scholarly journals Structural and functional brain asymmetries in the early phases of life: a scoping review

2021 ◽  
Author(s):  
Patrizia Bisiacchi ◽  
Elisa Cainelli
Keyword(s):  
Scoping Review ◽  
Right Hemisphere ◽  
Final Analysis ◽  
Brain Structures ◽  
Large Variability ◽  
Speech Stimuli ◽  
Morphological Asymmetry ◽  
Cerebral Asymmetries ◽  
The Right ◽  
And Function

AbstractAsymmetry characterizes the brain in both structure and function. Anatomical asymmetries explain only a fraction of functional variability in lateralization, with structural and functional asymmetries developing at different periods of life and in different ways. In this work, we perform a scoping review of the cerebral asymmetries in the first brain development phases. We included all English-written studies providing direct evidence of hemispheric asymmetries in full-term neonates, foetuses, and premature infants, both at term post-conception and before. The final analysis included 57 studies. The reviewed literature shows large variability in the used techniques and methodological procedures. Most structural studies investigated the temporal lobe, showing a temporal planum more pronounced on the left than on the right (although not all data agree), a morphological asymmetry already present from the 29th week of gestation. Other brain structures have been poorly investigated, and the results are even more discordant. Unlike data on structural asymmetries, functional data agree with each other, identifying a leftward dominance for speech stimuli and an overall dominance of the right hemisphere in all other functional conditions. This generalized dominance of the right hemisphere for all conditions (except linguistic stimuli) is in line with theories stating that the right hemisphere develops earlier and that its development is less subject to external influences because it sustains functions necessary to survive.

Bihemispheric Language: How the Two Hemispheres Collaborate in the Processing of Language

2004 ◽  
Author(s):  
Norman D. Cook
Keyword(s):  
Language Processing ◽  
Right Hemisphere ◽  
Central Question ◽  
Linguistic Processing ◽  
Figures Of Speech ◽  
Language Functions ◽  
Left And Right ◽  
Level Of Understanding ◽  
The Right

Speech production in most people is strongly lateralized to the left hemisphere (LH), but language understanding is generally a bilateral activity. At every level of linguistic processing that has been investigated experimentally, the right hemisphere (RH) has been found to make characteristic contributions, from the processing of the affective aspects of intonation, through the appreciation of word connotations, the decoding of the meaning of metaphors and figures of speech, to the understanding of the overall coherency of verbal humour, paragraphs and short stories. If both hemispheres are indeed engaged in linguistic decoding and both processes are required to achieve a normal level of understanding, a central question concerns how the separate language functions on the left and right are integrated. This chapter reviews relevant studies on the hemispheric contributions to language processing and the role of interhemispheric communications in cognition.

scholarly journals An Incremental Version of L-MVU for the Feature Extraction of MI-EEG

2019 ◽  
Vol 2019 ◽  
pp. 1-19
Author(s):  
Mingai Li ◽  
Hongwei Xi ◽  
Xiaoqing Zhu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Average Energy ◽  
High Dimensional ◽  
Dimensional Representation ◽  
Feature Extraction Method ◽  
Sample Data ◽  
Out Of Sample ◽  
Nonlinear Features ◽  
Low Dimensional

Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy. As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG. However, L-MVU still requires considerable computation costs for out-of-sample data. An incremental version of L-MVU (denoted as IL-MVU) is proposed in this paper. The low-dimensional representation of the training data is generated by L-MVU. For each out-of-sample data, its nearest neighbors will be found in the high-dimensional training samples and the corresponding reconstruction weight matrix be calculated to generate its low-dimensional representation as well. IL-MVU is further combined with the dual-tree complex wavelet transform (DTCWT), which develops a hybrid feature extraction method (named as IL-MD). IL-MVU is applied to extract the nonlinear features of the specific subband signals, which are reconstructed by DTCWT and have the obvious event-related synchronization/event-related desynchronization phenomenon. The average energy features of α and β waves are calculated simultaneously. The two types of features are fused and are evaluated by a linear discriminant analysis classifier. Based on the two public datasets with 12 subjects, extensive experiments were conducted. The average recognition accuracies of 10-fold cross-validation are 92.50% on Dataset 3b and 88.13% on Dataset 2b, and they gain at least 1.43% and 3.45% improvement, respectively, compared to existing methods. The experimental results show that IL-MD can extract more accurate features with relatively lower consumption cost, and it also has better feature visualization and self-adaptive characteristics to subjects. The t-test results and Kappa values suggest the proposed feature extraction method reaches statistical significance and has high consistency in classification.

scholarly journals Discovering a sparse set of pairwise discriminating features in high-dimensional data

2020 ◽  
Author(s):  
Samuel Melton ◽  
Sharad Ramanathan
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Dimensional Subspace ◽  
Supplementary Information ◽  
High Dimensional ◽  
Technological Advances ◽  
Data Points ◽  
Low Dimensional ◽  
Sparse Set

Abstract Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.

Categorizing and Individuating Others: The Neural Substrates of Person Perception

2004 ◽  
Vol 16 (10) ◽  
pp. 1785-1795 ◽  
Author(s):  
Malia F. Mason ◽  
C. Neil Macrae
Keyword(s):  
Neural Activity ◽  
Person Perception ◽  
Social Cognitive ◽  
Right Hemisphere ◽  
Neural Substrates ◽  
Hemispheric Differences ◽  
Cognitive Operations ◽  
Processing Abilities ◽  
Group Members ◽  
The Right

People are remarkably adroit at understanding other social agents. Quite how these information-processing abilities are realized, however, remains open to debate and empirical scrutiny. In particular, little is known about basic aspects of person perception, such as the operations that support people's ability to categorize (i.e., assign persons to groups) and individuate (i.e., discriminate among group members) others. In an attempt to rectify this situation, the current research focused on the initial perceptual stages of person construal and considered: (i) hemispheric differences in the efficiency of categorization and individuation; and (ii) the neural activity that supports these social-cognitive operations. Noting the greater role played by configural processing in individuation than categorization, it was expected that performance on the former task would be enhanced when stimuli (i.e., faces) were presented to the right rather than to the left cerebral hemisphere. The results of two experiments (Experiment 1—healthy individuals; Experiment 2—split-brain patient) confirmed this prediction. Extending these findings, a final neuroimaging investigation revealed that individuation is accompanied by neural activity in regions of the temporal and prefrontal cortices, especially in the right hemisphere. We consider the implications of these findings for contemporary treatments of person perception.

Right hemisphere speech perception revealed by amobarbital injection and electrical interference

Neurology ◽  
1998 ◽  
Vol 51 (2) ◽  
pp. 458-464 ◽  
Author(s):  
D. Boatman ◽  
J. Hart ◽  
R. P. Lesser ◽  
N. Honeycutt ◽  
N. B. Anderson ◽  
...  
Keyword(s):  
Speech Perception ◽  
Left Hemisphere ◽  
Temporal Lobe ◽  
Right Hemisphere ◽  
Adult Brain ◽  
Left Posterior ◽  
Left And Right ◽  
Sodium Amobarbital ◽  
The Right ◽  
Electrical Interference

Objective: To investigate the right hemispheric speech perception capabilities of an adult right-handed patient with seizures.Methods: Consecutive, unilateral, intracarotid sodium amobarbital injections and left hemispheric electrical interference mapping were used to determine lateralization and localization of speech perception, measured as syllable discrimination.Results: Syllable discrimination remained intact after left and right intracarotid sodium amobarbital injections. Language otherwise strongly lateralized to the left hemisphere. Despite evidence of bilateral speech perception capabilities, electrical interference testing in the left posterior temporal lobe impaired syllable discrimination.Conclusions: The results suggest a functionally symmetric, parallel system in the adult brain with preferential use of left hemispheric pathways for speech perception.

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