Kamran Afzali’s research while affiliated with Université de Montréal and other places

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Publications (1)

Fig. 1 Fifteen regions of interest (ROIs) selected from the Human Brainnetome Atlas (Fan et al., 2016) based on their documented involvement in affective processes. (a) ROIs were chosen based on their highest scores associated with the "Emotion" behavioral domain or subdomain (anger, anxiety, disgust, fear, happiness, sadness) as defined in the BrainMap taxonomy (Fox & Lancaster, 2002; Fox et al., 2005). (b) Visualization of all ROIs in the MNI space, excluding R_A10
Fig. 2 Distribution of Positive and Negative Affect scores. Fluctuations in positive affectivity (PA) (a) and negative affectivity (NA) (b) scores are presented across 40 to 50 longitudinal sessions for each subject, distributed over 6 to 8 months (left). Boxplots and swarm-
Fig. 3 Detailed loadings of PCA coupling components. Respective loadings of the 105 raw connectivity measures to the 10 components retained by LASSO regularization (Table 4). (a) Components associ-
Fig. 4 Summarized loadings of PCA coupling components. Averaged absolute PCA loadings are depicted across the coupling components retained by LASSO regularization, separately for positive (a) and negative (b) affectivity models, highlighting the higher contribution of the connectivity of the amygdala, hippocampus, and insula in explaining within-person variation in day-to-day affectivity
PANAS. Compilation of the 20 PANAS items categorized into the positive or negative affect scale according to the classification by Watson et al. (1988)

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Functional brain connectivity changes associated with day-to-day fluctuations in affective states
  • Article
  • Full-text available

September 2024

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Cognitive Affective & Behavioral Neuroscience

Jeanne Racicot

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Salima Smine

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Kamran Afzali

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Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models’ generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months.

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