scholarly journals Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5908 ◽  
Author(s):  
Geneviève Richard ◽  
Knut Kolskår ◽  
Anne-Marthe Sanders ◽  
Tobias Kaufmann ◽  
Anders Petersen ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Cognitive Aging ◽  
Diffusion Tensor ◽  
Imaging Features ◽  
Tissue Specific ◽  
Brain Morphometry ◽  
White Matter Microstructure ◽  
Brain Age ◽  
Age Prediction

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20–88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78–0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74–0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.

scholarly journals Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

2018 ◽  
Author(s):  
Geneviève Richard ◽  
Knut Kolskår ◽  
Anne-Marthe Sanders ◽  
Tobias Kaufmann ◽  
Anders Petersen ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Cognitive Aging ◽  
Diffusion Tensor ◽  
Imaging Features ◽  
Tissue Specific ◽  
Brain Morphometry ◽  
White Matter Microstructure ◽  
Brain Age ◽  
Age Prediction

AbstractMultimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have to a lesser extent been characterized.Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue- specific brain ages and their cognitive sensitivity we applied each of the 11 models in an independent and cognitively well-characterized sample (n=265, 20–88 years). Correlations between true and estimated age in our test sample were highest for the most comprehensive brain morphometry (r=0.83, CI:0.78–0.86) and white matter microstructure (r=0.79, CI:0.74–0.83) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.

scholarly journals Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging

2021 ◽  
Vol 10 (3) ◽  
pp. 418
Author(s):  
Torgeir Hellstrøm ◽  
Nada Andelic ◽  
Ann-Marie G. de Lange ◽  
Eirik Helseth ◽  
Kristin Eiklid ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
Diffusion Tensor ◽  
Traumatic Injury ◽  
Brain Morphometry ◽  
Cingulum Bundle ◽  
Brain Age ◽  
And Diffusion ◽  
Age Prediction

Background: Apolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI). Methods: Patients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Corrections for multiple comparisons were done using false discovery rate (FDR). Results: Of included patients, 123 patients had available APOE, volumetric, and DTI data of sufficient quality. There were no differences between APOE ɛ4 carriers (39%) and non-carriers in demographic and clinical data. Age prediction revealed high accuracy both for the DTI-based and the brain morphometry based model. Group comparisons revealed no significant differences in brain-age gap between ɛ4 carriers and non-carriers, and no significant differences in conventional measures of brain morphometry and volumes. Compared to non-carriers, APOE ɛ4 carriers showed lower fractional anisotropy (FA) in the hippocampal part of the cingulum bundle, which did not remain significant after FDR adjustment. Conclusion: APOE ɛ4 carriers might be vulnerable to reduced neuronal integrity in the cingulum. Larger cohort studies are warranted to replicate this finding.

scholarly journals Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

2019 ◽  
Author(s):  
Siren Tønnesen ◽  
Tobias Kaufmann ◽  
Ann-Marie de Lange ◽  
Genevieve Richard ◽  
Nhat Trung Doan ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Imaging Study ◽  
Brain Maturation ◽  
Group Differences ◽  
Healthy Controls ◽  
Brain White Matter ◽  
Brain Age ◽  
Age Prediction

AbstractBackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts.MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics.ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

Sign in / Sign up

Export Citation Format

Share Document