Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures

Abstract

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting‐state fMRI (rs‐fMRI) sample from the PREDICT‐HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole‐brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs‐fMRI data to obtain whole‐brain resting state networks. FNC was defined as the correlation between RSN time‐courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k‐means clustering algorithm. HDgmc individuals spent significantly more time in State‐1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State‐4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State‐1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations

“…Recent studies applying the dynamic FNC method (dFNC) have demonstrated that temporal functional network connectivity (FNC) analysis (i.e., co-activation between covarying networks estimated via independent component analysis) can uncover reoccurring connectivity patterns at resting state or during task performances. Their results also indicate that brain connectivity patterns iterate through time and show smooth variations of connectivity (Allen et al, 2014; Calhoun et al, 2014; Damaraju et al, 2014; Rashid et al, 2014; Espinoza et al, 2019). The dFNC method provides a way to explore temporally transient changes in the functional connectivity among brain networks using sliding windows to compute FNC across time (Sakoğlu et al, 2010; Allen et al, 2014).…”

“…Then, in each time-windowed domain a FNC vector is calculated. This procedure generates a discrete sequence of windowed FNC (wFNC) vectors that are then represented by wFNC matrices (Figure 1B) describing connectivity behavior across time (Sakoğlu et al, 2010; Allen et al, 2014; Damaraju et al, 2014; Rashid et al, 2014; Espinoza et al, 2019). Subjects’ dFNC data is formed by all wFNC vectors, and is referred to as the zero order derivatives of the sliding window correlations.…”

Predicting intelligence from brain gray matter volume

“…On the other hand, we also assessed whether intelligence can be predicted from regional gray matter volumes when not correcting for total brain size (i.e., from absolute gray matter volumes), to test the influence of total brain size on the prediction of intelligence from regional gray matter differences. As there exists no general consensus on how to best construct meaningful features from the very high-dimensional voxel-wise neuroimaging data, we implemented two different approaches of feature construction and compared the respective results: We started with a well-established and purely data-driven method, i.e., principal component analyses (PCA, see e.g., Abreu et al 2019 ; Espinoza et al 2019 ; Wasmuht et al 2018 ). In addition, we implemented a more theoretically informed, domain knowledge-based approach, which combines voxel-specific gray matter values in regions of interest in accordance with a well-established functional brain atlas (Schaefer et al 2018 ).…”

The Role of Hypothalamic Pathology for Non-Motor Features of Huntington’s Disease

“…Functional MRI (fMRI) studies have reinforced our understanding of limbic system dysfunction in HD. Reduced functional connectivity, network integrity and activity was found in the HD hippocampus, amygdala, ventral striatum, cingulate cortex and prefrontal cortex using different fMRI paradigms assessing verbal working memory, emotional processing, interference/conflict resolution and attention/alertness [89][90][91][92][93][94][95][96][97][98][99]. Such alterations in activity patterns produce multidimensional maps that to some extent reflect internal states of brain processing in response to a task.…”