Introduction

Sex-biased conditions have been shown to associate with the sexual dimorphism of the brain structural connections¹. Diffusion MRI (dMRI) is the only non-invasive method that can map the living human brain’s connections and has been used to explore structural differences between the brains of the human sexes^2–6.

In this work, we utilize dMRI to test for structural connectivity differences between females and males in frontostriatal white matter connections. Previous studies have identified significant sex differences of the white matter in the frontal lobe, by analyzing dMRI-based quantitative microstructure measures such as fractional anisotropy and mean diffusivity^5,6. These measures are useful for the assessment of differences in the underlying tissue microstructure between different populations. However, they do not provide topographical information of the WM connections, which is important for assessing the brain wiring pattern⁷.

Therefore, we propose a novel method, using fiber clustering of whole-brain dMRI tractography to assess the topographical organization of frontostriatal structural connectivity, which allows us to quantify the degree of deviation from a topographic, parallel, arrangement. The proposed method provides important information to assess the segregated and integrative brain wiring patterns in the human brain⁸. We applied the proposed method to study sex differences in a cohort of 100 healthy subjects.

Methods

Study population and dMRI data: We studied 100 healthy adult subjects from the Human Connectome Project (HCP)⁹ (46 females and 54 males; age-matched with p=0.91). dMRI data (b=0/3000s/mm², 90 directions, 18 b=0, TE/TR=89.5/5520ms, and 1.25x1.25x1.25mm³) was used. Whole-brain tractography was computed using an Unscented Kalman Filter (UKF) method¹⁰ implemented in SlicerDMRI¹¹.

A novel topographical measure of the frontostriatal WM connections: 1) We identified WM connections from the prefrontal cortex (PFC) and the striatum (S). We leveraged a data-driven fiber clustering atlas¹² that allows for a whole-brain tractography parcellation into 2000 fiber clusters for each HCP subject under study. We identified fiber clusters of interest (i.e., from PFC to S) according to their connected anatomical brain regions, in particular, the Freesurfer¹³ regions including orbital, lateral and medial PFC regions and the caudate. 2) We quantified the topographical relationship of fiber clusters of interest. For each pair of clusters, we measured the mean fiber endpoint distance within PFC (i.e., cortical distance D_PFC) and the mean fiber endpoint distances terminating in the caudate (i.e., striatal distance D_S)(Figure-1). A high D_PFC and a low D_S describe a convergent (integrative) pattern of frontostriatal WM connection, while a low D_PFC and a high D_S describe a divergent (segregated) pattern. For each pair of clusters, we measured the ratio of the fiber endpoint distances: R_D=(D_PFC-D_S)/(D_PFC+D_S) for an integrated measure including both D_PFC and D_S.

Statistical analysis: 1) We first compared sex differences for all pairs of clusters using R_D. For each subject, we put the R_D values of all pairs of clusters together as a feature vector. Across all subjects, we performed a principal component analysis (PCA)¹⁴ for feature dimension reduction to focus on the cluster pairs that are the most discriminative between the two groups. Then, we performed a Hotelling T-Squared test¹⁵ for computing group differences using the features after PCA reduction. We tested for multiple dimension reduction strategies (i.e. different dimensions after PCA), where a p-value was computed per strategy (FDR corrected for multiple comparisons across all strategies). 2) We also compared sex differences for each cluster uring R_D. For each cluster and each subject, we put the R_D values with respect to the remaining clusters together as a feature vector. Then, we performed a Hotelling T-Squared test to computing group differences of each cluster. FDR was performed for multiple comparison corrections across all clusters.

Results

We identified 17 WM fiber clusters that connect PFC and S in both the left and right hemispheres. These resulted in a total of 136 pairs of fiber clusters per hemisphere, yielding 136 R_D values. The statistical comparison across all pairs of clusters shows that when the dimension was higher or equal to 23 and 34 in the left and right hemispheres, respectively, there were significant group differences (Figure-2). For the comparison on each individual cluster, we found that clusters #10 (connecting the left rostral middle frontal gyrus) and #16 (connecting the left lateral orbito frontal gyrus) in the left hemisphere were significantly different, and cluster #3 (connecting the right rostral middle frontal gyrus) in the right hemisphere was significantly different (Figure-3).

Discussion

We found significant sex differences of the WM connections in the frontostriatal circuits, which are important in modulating human cognitive control and reward processing¹⁶. The identified significant group differences were potentially related to the different brain wiring patterns in the frontal lobe between females and males. In particular, we found three individual clusters that connect the associative dorsolateral prefrontal cortex that is associated with executive functions and the limbic orbitolateral prefrontal cortex that is associated with functions including reward processing¹⁷.

Conclusion

We proposed a novel method to investigate the topographical structural connectivity in frontostriatal white matter connections. We applied this method to study white matter sexual differences in a cohort of 100 healthy subjects. In the future, we plan to use this methodology to assess diverse groups including comparing healthy subjects versus those with psychosis.

Acknowledgements

The authors gratefully acknowledge the support of the following NIH grants: P41EB015898, R01MH108574, P41EB015902, R01MH119222, U01CA199459.

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