scholarly journals An Application of Univariate and Multivariate Approaches in fMRI to Quantifying the Hemispheric Lateralization of Acoustic and Linguistic Processes

2012 ◽  
Vol 24 (3) ◽  
pp. 636-652 ◽  
Author(s):  
Carolyn McGettigan ◽  
Samuel Evans ◽  
Stuart Rosen ◽  
Zarinah K. Agnew ◽  
Poonam Shah ◽  
...  
Keyword(s):  
Speech Intelligibility ◽  
Pattern Analysis ◽  
Temporal Cortex ◽  
Hemispheric Lateralization ◽  
Multivariate Pattern Analysis ◽  
Hemispheric Asymmetries ◽  
Intelligible Speech ◽  
Hemisphere Dominance ◽  
The Right ◽  
Multivariate Pattern

The question of hemispheric lateralization of neural processes is one that is pertinent to a range of subdisciplines of cognitive neuroscience. Language is often assumed to be left-lateralized in the human brain, but there has been a long running debate about the underlying reasons for this. We addressed this problem with fMRI by identifying the neural responses to amplitude and spectral modulations in speech and how these interact with speech intelligibility to test previous claims for hemispheric asymmetries in acoustic and linguistic processes in speech perception. We used both univariate and multivariate analyses of the data, which enabled us to both identify the networks involved in processing these acoustic and linguistic factors and to test the significance of any apparent hemispheric asymmetries. We demonstrate bilateral activation of superior temporal cortex in response to speech-derived acoustic modulations in the absence of intelligibility. However, in a contrast of amplitude-modulated and spectrally modulated conditions that differed only in their intelligibility (where one was partially intelligible and the other unintelligible), we show a left dominant pattern of activation in STS, inferior frontal cortex, and insula. Crucially, multivariate pattern analysis showed that there were significant differences between the left and the right hemispheres only in the processing of intelligible speech. This result shows that the left hemisphere dominance in linguistic processing does not arise because of low-level, speech-derived acoustic factors and that multivariate pattern analysis provides a method for unbiased testing of hemispheric asymmetries in processing.

scholarly journals Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler
Keyword(s):  
Breast Cancer ◽  
Functional Connectivity ◽  
Breast Cancer Survivors ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Survival Rates ◽  
Connectivity Pattern ◽  
Multivariate Pattern Analysis ◽  
The Right ◽  
Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

scholarly journals Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hanxiaoran Li ◽  
Sutao Song ◽  
Donglin Wang ◽  
Zhonglin Tan ◽  
Zhenzhen Lian ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Pattern Analysis ◽  
Low Frequency ◽  
Multivariate Pattern Analysis ◽  
Major Depressive ◽  
Individual Level ◽  
Hamd Score ◽  
The Individual ◽  
The Right ◽  
Multivariate Pattern

Abstract Background Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). Methods Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. Results The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification. Conclusions The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

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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