Imaging Patterns of Brain Development and their Relationship to Cognition

Abstract

We present a brain development index (BDI) that concisely summarizes complex imaging patterns of structural brain maturation along a single dimension using a machine learning methodology. The brain was found to follow a remarkably consistent developmental trajectory in a sample of 621 subjects of ages 8-22 participating in the Philadelphia Neurodevelopmental Cohort, reflected by a cross-validated correlation coefficient between chronologic age and the BDI of r = 0.89. Critically, deviations from this trajectory related to cognitive performance. Specifically, subjects whose BDI was higher than their chronological age displayed significantly superior cognitive processing speed compared with subjects whose BDI was lower than their actual age. These results indicate that the multiparametric imaging patterns summarized by the BDI can accurately delineate trajectories of brain development and identify individuals with cognitive precocity or delay.

The association between “Brain‐Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

“…The estimation model in the basis of SVR algorithm has been widely used in different neuroimaging studies (Dosenbach et al., 2011; Erus et al., 2015; Koutsouleris et al., 2014; Lancaster et al., 2018). In the present study, a linear v‐support vector regression (v‐SVR) performed using LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) toolbox with a default setting (i.e., in the LIBSVM: C  = 1, v  =   0.5) was used.…”

Melatonin 4 mg as prophylactic therapy for primary headaches: a pilot study

“…We believe it is necessary to reach a consensus of standardized software algorithms and measurements able to guarantee that all measurements are conducted within the same algorithms in all patients; in this way, variations in the results would be a reflection only of the distribution of the selected biomarkers. For example, a recent study has proposed the use of machine learning, albeit in a younger age group (8-22 years) (Erus et al, 2015). Despite the initially steep learning curve of the open-source software packages used in this study, they are suitable for use on a day-to-day basis in MRI units, for example those supporting geriatric or family medicine studies, and not only in clinical research.…”

Human brain development over the early years

“…For example, the 'brain maturation index' accurately predicts chronological age between approximately 5 and 18 years after birth based on brain volumes in 37 regions measured using MRI [14]. Accumulating evidence from MRI and DTI during development [15], has led to the generation of a 'brain development index' that can accurately predict chronological age between 8 and 22 years of age based on brain anatomy in children and adolescents [16]. Overall, gray matter volumes begin to decrease from mid-childhood, while white matter volumes continue to increase with age [1].…”

“…Overall, gray matter volumes begin to decrease from mid-childhood, while white matter volumes continue to increase with age [1]. Individuals with a higher brain development index-predicted age than actual age (advanced) show an earlier decrease in gray matter volumes compared to individuals with similar predicted and actual age (typical) [16]. In contrast, individuals with a lower predicted age (delayed) show a later developmental shift , 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174…”