Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

Abstract

The aging process is accompanied by changes in the brain’s cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework’s ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer’s disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

“…Using this analytical approach, previous studies also demonstrated that these identified large-scale structural covariance patterns of the human brain are highly associated with different neuropsychiatric disorders, neurodegenerative diseases, and the healthy aging process (32,(58)(59)(60). In line with previous brain age study which mainly focused on middle-to-late adulthood (31), the current study also demonstrated that the sICA-based feature extraction strategy could identify meaningful large-scale structural covariance patterns for estimating individual brain age with higher prediction accuracy.…”

“…In addition to the parcel-wise feature extraction strategy, we applied multivariate spatial independent component analysis (sICA) and spatial regression analysis as a secondary feature extraction strategy to obtain corresponding input feature sets across study participants. The details of the sICA-based feature extraction procedure have been described in our previous work (30,31). Briefly, the preprocessed MNI space GMV, GMD, WMV, and WMD maps of the training dataset were concatenated as 4D datasets, respectively.…”

Brain age in mood and psychotic disorders: a systematic review and meta‐analysis

“…of identification, screening, and eligibility of the systematic review MDD (n = 4). 16,[35][36][37] Other studies investigated brain-PAD in more than one disorder: SCZ and BD (n = 4), 15,[38][39][40] SCZ and MDD (n = 3), [41][42][43] and SCZ, BD, and MDD. 12 Details for each study included are listed in Table 1.…”

Classification differentiates clinical and neuroanatomic features of cerebral small vessel disease

“…We used the previously proposed analytical framework to extract both global-wise tissue and WMH volume as well as voxel-wise GMV information for each patient. 13 , 24 The following analyses were conducted with Statistical Parametric Mapping (SPM12, version 7487, Wellcome Institute of Neurology, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm/ ) and Matlab R2016a (The Mathworks, Inc., Natick, MA, USA) using default settings. A brief description of the analytical procedure is as follows: (i) using the Lesion Segmentation Toolbox (LST, version 3.0.0, https://www.applied-statistics.de/lst.html ), 25 individual T 2 -weighted FLAIR scans were first affine-registered to the corresponding T 1 -weighted scan.…”