Data_Sheet_1_Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set.docx

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study’s primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.MethodResting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.ResultThe data-driven method–ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds.ConclusionThis preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.</p

Agreement comparison with respect to data with good and poor sleep efficiency.

Agreement comparison with respect to data with good and poor sleep efficiency.</p

Hypnograms of the subject no. 4.

The hypnograms scored by fully manual scoring (scorer 2) (A), fully automatic staging (B) and the HCSS system (C).</p

Comparison of agreement between different automatic scoring methods.

Comparison of agreement between different automatic scoring methods.</p

Evaluation of the HCSS system.

(A) The agreement in overall, high-reliability and low-reliability epochs, along with the kappa coefficient between the manual scorings and the HCSS system collaborated scorings. (B) The average of the scoring time for one subject spent in manual and HCSS groups. Percentage of reduced manual scoring time with the assistance of the HCSS system; OA: overall, HR: high-reliability, LR: low-reliability.</p

The hypnogram and SCF values of subject no. 5.

The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCF (C). The red lines indicate disagreement between the expert and the automatic scoring system.</p

The hypnogram and SCD value of PSG from subject no. 3.

The hypnograms scored by gold standard (A) and the automatic staging system (B). The value of feature SCD (C). The red lines indicate disagreement between the expert and the automatic scoring system.</p

Reliability analysis examples.

Disagreements can be detected by using the SWR (A), SCD (B) and SCF (C) features.</p

The architecture of the voting process.

According to the value of the SWR, SCD, and SCF features, the voting process determines a scored epoch as a high reliability or low reliability.</p

The flow chart of the human-computer collaborative sleep scoring system.

The system consists of (A) fully automatic scoring, (B) reliability analysis, and (C) human-computer collaboration.</p