Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: a fractional amplitude of low frequency fluctuation-based machine learning analysis

Abstract

Background and objective: Insomnia is one of the common problems encountered in the hemodialysis (HD) population, but the mechanisms remain unclear. we aimed to (1) detect the spontaneous brain activity pattern in HD patients with insomnia (HDWI) by using fractional fractional amplitude of low frequency fluctuation (fALFF) method and (2) further identify brain regions showing altered fALFF as neural markers to discriminate HDWI patients from those on hemodialysis but without insomnia (HDWoI) and healthy controls (HCs).

Method: We compared fALFF differences among HDWI subjects (28), HDWoI subjects (28) and HCs (28), and extracted altered fALFF features for the subsequent discriminative analysis. Then, we constructed a support vector machine (SVM) classifier to identify distinct neuroimaging markers for HDWI.

Results: Compared with HCs, both HDWI and HDWoI patients exhibited significantly decreased fALFF in the bilateral calcarine (CAL), right middle occipital gyrus (MOG), left precentral gyrus (PreCG), bilateral postcentral gyrus (PoCG) and bilateral temporal middle gyrus (TMG), whereas increased fALFF in the bilateral cerebellum and right insula. Conversely, increased fALFF in the bilateral CAL/right MOG and decreased fALFF in the right cerebellum was observed in HDWI patients when compared with HDWoI patients. Moreover, the SVM classification achieved a good performance [accuracy = 82.14%, area under the curve (AUC) = 0.8202], and the consensus brain regions with the highest contributions to classification were located in the right MOG and right cerebellum.

Conclusion: Our result highlights that HDWI patients had abnormal neural activities in the right MOG and right cerebellum, which might be potential neural markers for distinguishing HDWI patients from non-insomniacs, providing further support for the pathological mechanism of HDWI.

Keywords: Amplitude of low frequency fluctuation; Hemodialysis; Insomnia; Neural markers; Support vector machine (SVM).

Fig. 1

The flow diagram of classification. Study 1: fMRI preprocessing and fALFF analysis; Study 2: Machine learning analyses. HDWI = hemodialysis with insomnia; HDWoI = hemodialysis without insomnia; HC = healthy controls; fALFF = fractional fractional amplitude of low frequency fluctuation; CAL = calcarine; MOG = middle occipital gyrus; Bi = bilateral; R = right; LOOCV = leave-one-out cross-validation; SVM = support vector machine

Fig. 2

fALFF difference among and between groups (A-D). A) AVOVA analysis showed significant increased fALFF in the right MOG/ cerebellum and bilateral CAL among the three groups; B) Compared with HDWoI, HDWI subjects showed significant increased fALFF value in the right MOG and bilateral CAL whereas decreased fALFF value in the right cerebellum. C) Compared with HCs, HDWI subjects showed significant decreased fALFF value in the right MOG and bilateral CAL whereas increased fALFF value in the right cerebellum. D) Compared with HCs, HDoWI subjects showed significant decreased fALFF value in the right MOG and bilateral CAL whereas increased fALFF value in the right cerebellum. Abbreviation: Bi = bilateral; R = Right; CAL = calcarine; MOG = middle occipital gyrus; * = a threshold of voxel-wise p < 0.001 uncorrected and cluster-level p < 0.05; ANOVA: analysis of variance; yellow represents a significant increase and green represents a significant decrease

Fig. 3

The correlation analyses between the ALFF features and clinical measurements in HDWI patients (A-B). A) Positive Correlation between the bilateral calcarine and the sleep efficiency subscore of PSQI( r = 0.460, p = 0.014); B) Negative correlation between the right cerebelum and the sleep quality subscore of PSQI( r = 0.421, p = 0.026)

Fig. 4

Discriminative brain regions. The discriminative regions included the right cerebelum and the right middle occipital gyrus. The color bar value represents the absolute value of the weight value of the brain regions. Yellow means positive weight and blue means negative weight

Fig. 5

Classification performance of SVR model. A) The accuracy of classification with the increased number of features; when including 78 discriminative features, the highest accuracy of the classification model is 82.14%. B) Area under the curve of the classification model (AUC = 0.8202) with the highest accuracy. C) The result of the permutation test with the highest accuracy ( p < 0.0002)