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Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study

  • Magnetic Resonance
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Abstracts

Objectives

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer’s disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD.

Methods

One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers.

Results

We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity.

Conclusion

This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD.

Key Points

• Multimodal brain networks improve the detection accuracy of SCD.

• Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.

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Abbreviations

Aβ:

Amyloid-β

AD:

Alzheimer’s disease

ADNI:

Alzheimer’s Disease Neuroimaging Initiative

APOE:

Apolipoprotein E

CAT:

Computational Anatomy Toolbox

CCI:

Cognitive Change Index

CDR:

Clinical dementia rating

CDT:

Clock-Drawing Test

CEN:

Central executive network

CI:

Confidence intervals

CSF:

Cerebrospinal fluid

DELCODE:

DZNE-Longitudinal Cognitive Impairment and Dementia

DMN:

Default mode network

DPABI:

Data Processing & Analysis for Brain Imaging

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

FA:

Fractional anisotropy

FDR:

False discovery rate

fMRI:

Resting-state functional magnetic resonance imaging

GDS:

Geriatric depression scale

GM:

Gray matter

HC:

Healthy controls

JSS:

Jensen-Shannon distance-based similarity

LOOCV:

Leave-one-out cross-validation

MCI:

Mild cognitive impairment

MKL-SVM:

Multiple kernel learning SVM

MMSE:

Mini-Mental State Examination

MNI:

Montreal Neurological Institute

PANDA:

Pipeline for Analyzing braiN Diffusion imAges

PET:

Positron emission tomography

RAVLT:

Rey Auditory Verbal Learning Test

ROC:

Receiver operating characteristic curve

ROIs:

Regions of interest

SCD:

Subjective cognitive decline

SD:

Standard deviation

sMRI:

Structural MRI

SN:

Salience network

SPM:

Statistical parametric mapping

SUVRs:

Standard uptake value ratios

SVM:

Support vector machine

TE:

Echo time

TI:

Inversion time

TMT:

Trail-Making Test

TR:

Repetition time

WM:

White matter

WMS-LM:

Wechsler Memory Scale-Logical Memory

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Acknowledgements

Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, and the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Funding

This work was supported partly by grants from the National Natural Science Foundation of China (No. 81822013; 82071186), the Jiangsu Provincial Key Medical Talents (No. ZDRCA2016085), the Key Research and Development Program of Jiangsu Province of China (BE2016610), the National Key Research and Development Program of China (2016YFC1300500-504), and the Jiangsu Province Key Medical Discipline (ZDXKA2016020).

Author information

Author notes

  1. Haifeng Chen and Weikai Li have contributed equally to this work.

Authors and Affiliations

  1. Department of Neurology, Affiliated Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China

    Haifeng Chen, Xiaoning Sheng, Qing Ye, Hui Zhao, Yun Xu & Feng Bai

  2. Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China

    Haifeng Chen, Xiaoning Sheng, Qing Ye, Hui Zhao, Yun Xu & Feng Bai

  3. Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China

    Haifeng Chen, Xiaoning Sheng, Qing Ye, Hui Zhao, Yun Xu & Feng Bai

  4. Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China

    Haifeng Chen, Xiaoning Sheng, Qing Ye, Hui Zhao, Yun Xu & Feng Bai

  5. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Weikai Li

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  1. Haifeng Chen
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Consortia

for the Alzheimer’s Disease Neuroimaging Initiative

Corresponding author

Correspondence to Feng Bai.

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Guarantor

The scientific guarantor of this publication is Prof. Feng Bai.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Subject data used in the preparation for this article was obtained from publicly available study samples, i.e., ADNI. Written informed consent was obtained as part of these studies.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Subject data (SCD patients) used in the preparation for this article was obtained from publicly available study samples, i.e., ADNI, which have not been used in previous publications.

Methodology

• Retrospective

• Cross sectional study

• Multicenter study

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

aData used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

The abbreviations of 246 ROIs are shown in Supplemental Table 1.

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Chen, H., Li, W., Sheng, X. et al. Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer’s disease: a preliminary study. Eur Radiol 32, 448–459 (2022). https://doi.org/10.1007/s00330-021-08080-9

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