scholarly journals Peer Review #3 of "Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry (v0.2)"

2018 ◽  
Author(s):  
M Dyrba
Keyword(s):  
Diffusion Tensor Imaging ◽  
Peer Review ◽  
Cognitive Aging ◽  
Diffusion Tensor ◽  
Tissue Specific ◽  
Brain Morphometry ◽  
Brain Age ◽  
Age Prediction

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.

Neuroimaging-based brain-age prediction of first-episode schizophrenia and the alteration of brain age after early medication

2021 ◽  
pp. 1-8
Author(s):  
Yi-Bin Xi ◽  
Xu-Sha Wu ◽  
Long-Biao Cui ◽  
Li-Jun Bai ◽  
Shuo-Qiu Gan ◽  
...  
Keyword(s):  
Diffusion Tensor ◽  
First Episode ◽  
Age Difference ◽  
Healthy Controls ◽  
Data Set ◽  
Brain Ageing ◽  
First Episode Schizophrenia ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Background Neuroimaging- and machine-learning-based brain-age prediction of schizophrenia is well established. However, the diagnostic significance and the effect of early medication on first-episode schizophrenia remains unclear. Aims To explore whether predicted brain age can be used as a biomarker for schizophrenia diagnosis, and the relationship between clinical characteristics and brain-predicted age difference (PAD), and the effects of early medication on predicted brain age. Method The predicted model was built on 523 diffusion tensor imaging magnetic resonance imaging scans from healthy controls. First, the brain-PAD of 60 patients with first-episode schizophrenia, 60 healthy controls and 21 follow-up patients from the principal data-set and 40 pairs of individuals in the replication data-set were calculated. Next, the brain-PAD between groups were compared and the correlations between brain-PAD and clinical measurements were analysed. Results The patients showed a significant increase in brain-PAD compared with healthy controls. After early medication, the brain-PAD of patients decreased significantly compared with baseline (P < 0.001). The fractional anisotropy value of 31/33 white matter tract features, which related to the brain-PAD scores, had significantly statistical differences before and after measurements (P < 0.05, false discovery rate corrected). Correlation analysis showed that the age gap was negatively associated with the positive score on the Positive and Negative Syndrome Scale in the principal data-set (r = −0.326, P = 0.014). Conclusions The brain age of patients with first-episode schizophrenia may be older than their chronological age. Early medication holds promise for improving the patient's brain ageing. Neuroimaging-based brain-age prediction can provide novel insights into the understanding of schizophrenia.

scholarly journals Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

2019 ◽  
Author(s):  
Geneviève Richard ◽  
Knut Kolskår ◽  
Kristine M. Ulrichsen ◽  
Tobias Kaufmann ◽  
Dag Alnæs ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Outcome Prediction ◽  
Cognitive Intervention ◽  
Chronological Age ◽  
Double Blind Study ◽  
Stroke Patients ◽  
Brain Morphometry ◽  
Age Gap ◽  
Brain Age ◽  
Age Prediction

AbstractCognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age – chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.

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