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 Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

scholarly journals Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

2016 ◽  
Vol 12 ◽  
pp. 348-358 ◽  
Author(s):  
Sheng Zhang ◽  
Sien Hu ◽  
Rajita Sinha ◽  
Marc N. Potenza ◽  
Robert T. Malison ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Pattern Analysis ◽  
Cocaine Dependence ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

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 Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Brain ◽  
2012 ◽  
Vol 135 (5) ◽  
pp. 1498-1507 ◽  
Author(s):  
Ling-Li Zeng ◽  
Hui Shen ◽  
Li Liu ◽  
Lubin Wang ◽  
Baojuan Li ◽  
...  
Keyword(s):  
Major Depression ◽  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Multivariate Pattern ◽  
Brain Functional Connectivity

scholarly journals Multivariate Pattern Analysis of EEG-Based Functional Connectivity: A Study on the Identification of Depression

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 92630-92641 ◽  
Author(s):  
Hong Peng ◽  
Chen Xia ◽  
Zihan Wang ◽  
Jing Zhu ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

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.

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