Haifeng Chen1,2,3,4, Weikai Li5, Xiaoning Sheng1,2,3,4, Qing Ye1,2,3,4, Hui Zhao1,2,3,4, Yun Xu1,2,3,4, Feng Bai6,7,8,9. 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. 2. Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. 3. Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. 4. Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China. 5. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China. 6. 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. baifeng@njglyy.com. 7. Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China. baifeng@njglyy.com. 8. Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China. baifeng@njglyy.com. 9. Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China. baifeng@njglyy.com.
Abstract
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.
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.
Authors: Frank Jessen; Rebecca E Amariglio; Martin van Boxtel; Monique Breteler; Mathieu Ceccaldi; Gaël Chételat; Bruno Dubois; Carole Dufouil; Kathryn A Ellis; Wiesje M van der Flier; Lidia Glodzik; Argonde C van Harten; Mony J de Leon; Pauline McHugh; Michelle M Mielke; Jose Luis Molinuevo; Lisa Mosconi; Ricardo S Osorio; Audrey Perrotin; Ronald C Petersen; Laura A Rabin; Lorena Rami; Barry Reisberg; Dorene M Rentz; Perminder S Sachdev; Vincent de la Sayette; Andrew J Saykin; Philip Scheltens; Melanie B Shulman; Melissa J Slavin; Reisa A Sperling; Robert Stewart; Olga Uspenskaya; Bruno Vellas; Pieter Jelle Visser; Michael Wagner Journal: Alzheimers Dement Date: 2014-05-03 Impact factor: 21.566
Authors: José L Molinuevo; Laura A Rabin; Rebecca Amariglio; Rachel Buckley; Bruno Dubois; Kathryn A Ellis; Michael Ewers; Harald Hampel; Stefan Klöppel; Lorena Rami; Barry Reisberg; Andrew J Saykin; Sietske Sikkes; Colette M Smart; Beth E Snitz; Reisa Sperling; Wiesje M van der Flier; Michael Wagner; Frank Jessen Journal: Alzheimers Dement Date: 2016-11-05 Impact factor: 21.566
Authors: Sander C J Verfaillie; Rosalinde E R Slot; Ellen Dicks; Niels D Prins; Jozefien M Overbeek; Charlotte E Teunissen; Philip Scheltens; Frederik Barkhof; Wiesje M van der Flier; Betty M Tijms Journal: Hum Brain Mapp Date: 2018-03-30 Impact factor: 5.038