Data_Sheet_1_Relationship among number of close friends, subclinical geriatric depression, and subjective cognitive decline based on regional homogeneity of functional magnetic resonance imaging data.docx

The relationship between geriatric depression and dementia has been widely debated, and the neurological mechanisms underlying subjective cognitive decline (SCD) associated with social relationships remain elusive. Subclinical geriatric depression (SGD) is common in patients with SCD, and close friends (CFs) have a great influence on a person’s social life. Studies have proven that communication or leisure activities with CFs can improve the cognitive performance of elderly. However, it remains unclear whether the engagement of specific brain regions mediates having CFs, SGD, and SCD. In this study, we aimed to assess the association between social relationships (that is, CFs), SGD, and SCD from the perspective of brain function. We examined the data of 66 patients with SCD and 63 normal controls (NC). Compared with NC, SGD was significantly inversely correlated with the number of CFs in the SCD group. We calculated regional homogeneity (ReHo) of functional magnetic resonance imaging (MRI) data of each subject. At a corrected threshold, the right occipital gyrus (SOG.R) and right fusiform gyrus (FFG.R) exhibited positive correlation with SGD in patients with SCD. Mediation analyses to query the inter-relationships between the neural markers and clinical variables exhibited a best fit of the model with CFs → FFG.R → SGD → SOG.R → SCD. These findings suggested a pathway whereby social relationships alter the function of specific brain regions, and SGD may be an early symptom of SCD. We observed that the FFG.R mediate social relationships and SGD, and the abnormality of the SOG.R may be a key factor in the SCD caused by depression. Moreover, a greater number of CFs may reduce the risk of developing SGD.</p

The prefix ’t’ denotes the t-test for CC and RMSE between the methods with age and the corresponding method without using age.

<p>The prefix ’<i>p</i>’ denotes <i>p</i> value.</p><p>The prefix ’t’ denotes the t-test for CC and RMSE between the methods with age and the corresponding method without using age.</p

Scatter plots of the true IQ vs. the estimated IQ by multi-kernel SVR.

<p>Scatter plots of the true IQ vs. the estimated IQ by the four competing methods with multi-kernel SVR, along with the standard deviation of the distance for each point to the fitted line.</p

Performance (mean ± standard deviation) comparison among all competing methods in both experiments.

<p>The prefix ’A’ denotes the use of age as a kernel matrix. (CC: Correlation Coefficient; RMSE: Root Mean Square Error)</p><p>Performance (mean ± standard deviation) comparison among all competing methods in both experiments.</p

Comparison of weight coefficient matrices for three different feature selection methods.

<p>Each colored square corresponds to a non-zero element after feature selection. Circled squares (with the yellow ellipse outlines) correspond to the selected group-wise features, and circled squares (with black rectangle outlines) correspond to the selected pair-wise correlated features. (A) Group lasso. (B) Traditional dirty model. (C) The proposed extended dirty model.</p

Scatter plots of the true IQ vs. the estimated IQ by single-kernel SVR.

<p>Scatter plots of the true IQ vs. the estimated IQ by the four competing methods with a single-kernel SVR, along with the standard deviation of the distance for each point to the fitted line.</p

Classification accuracy of site classifier for each repetition, where a 10-fold cross-validation was performed.

<p>Classification accuracy of site classifier for each repetition, where a 10-fold cross-validation was performed.</p

The 15 most frequently selected brain areas by the proposed method.

<p>Colors mainly show different regions.</p

Demographic characteristics of the used subjects. For age and IQ scores, we show the mean and corresponding standard deviations (SD).

<p>Demographic characteristics of the used subjects. For age and IQ scores, we show the mean and corresponding standard deviations (SD).</p

A schematic diagram of the proposed IQ estimation framework using structural MRI data.

<p>A schematic diagram of the proposed IQ estimation framework using structural MRI data.</p