Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

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)

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

10.1101/866939 ◽

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.

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