scholarly journals Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

2020 ◽  
Author(s):  
Ann-Marie G. de Lange ◽  
Melis Anatürk ◽  
Tobias Kaufmann ◽  
James H. Cole ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
White Matter ◽  
Functional Connectivity ◽  
Prediction Accuracy ◽  
Brain Aging ◽  
Training Sample ◽  
Resting State Functional Connectivity ◽  
White Matter Microstructure ◽  
Brain Age ◽  
Age Prediction

AbstractBrain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

scholarly journals Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

2020 ◽  
Author(s):  
Jaroslav Rokicki ◽  
Thomas Wolfers ◽  
Wibeke Nordhøy ◽  
Natalia Tesli ◽  
Daniel S. Quintana ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Neurological Disorders ◽  
Prediction Accuracy ◽  
Brain Aging ◽  
Autism Spectrum ◽  
Clinical Group ◽  
Operating Characteristics ◽  
Prediction Approach ◽  
Brain Age ◽  
Age Prediction

BackgroundThe deviation between chronological age and age predicted using brain MRI is a putative marker of brain health and disease-related deterioration. Age prediction based on structural MRI data shows high accuracy and sensitivity to common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the biological processes involved. Here, we implemented a multimodal age prediction approach and tested the predictive value across patients with a range of disorders with distinct etiologies and clinical features.MethodsWe implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) calculated from functional arterial spin labeling (ASL) data. For each of the 11 models we assessed the age prediction accuracy in HC n=761 and compared the resulting brain age gaps (BAGs) between each clinical group and age-matched subsets of HC in patients with Alzheimer’s disease (AD, n=54), mild cognitive impairment (MCI, n=88), subjective cognitive impairment (SCI, n=55), schizophrenia (SZ, n=156), bipolar disorder (BD, n=136), autism spectrum disorder (ASD, n=28).ResultsAmong the 11 models, we found highest age prediction accuracy in HC when integrating all modalities (mean absolute error=6.5 years). Beyond this global BAG, the area under the curve for the receiver-operating characteristics based on two-group case-control classifications showed strongest effects for AD and ASD in global T1-weighted BAG (T1w-BAG), while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs.ConclusionsCombining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and controls were often larger for BAGs based on single modalities. These findings demonstrate that multidimensional phenotyping provides a mapping of overlapping and distinct pathophysiology in common disorders of the brain, and specifically suggest metabolic and neurovascular aberrations in SZ and at-risk and early stage dementia.

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 A Reproducible Neurobiology of Depressive Rumination

2018 ◽  
Author(s):  
D.A. Pisner ◽  
J. Shumake ◽  
C.G. Beevers ◽  
D.M. Schnyer
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Executive Control ◽  
Structural Basis ◽  
Functional Magnetic Resonance ◽  
Resting State Functional Connectivity ◽  
Control Networks ◽  
Depressive Rumination ◽  
White Matter Microstructure ◽  
The Right

AbstractDepressive Rumination (DR), which involves a repetitive focus on one’s distress, has been linked to alterations in functional connectivity of the ‘triple-network’, consisting of Default-Mode, Salience, and Executive Control networks. A structural basis for these functional alterations that can dually explain DR’s persistence as a stable trait remains unexplored, however. Using diffusion and functional Magnetic Resonance Imaging, we investigated multimodal relationships between DR severity, white-matter microstructure, and resting-state functional connectivity in depressed adults, and then directly replicated our results in a phenotypically-matched, independent sample (total N = 78). Among the fully-replicated findings, DR severity was associated with: (a) global microstructure of the right Superior Longitudinal Fasciculus and local microstructure of distributed primary-fiber and crossing-fiber white-matter; (b) an imbalance of functional connectivity segregation and integration of the triple-network; and (c) ‘multi-layer’ associations linking these microstructural and functional connectivity biomarkers to one another. Taken together, the results provide reproducible evidence for a multi-layer, microstructural-functional network model of rumination in the depressed brain.

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 White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

2020 ◽  
Author(s):  
Dani Beck ◽  
Ann-Marie de Lange ◽  
Ivan I. Maximov ◽  
Geneviève Richard ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Accuracy ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Diffusion Models ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Brain White Matter ◽  
Brain Age ◽  
Age Prediction

AbstractThe macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk’s theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

Altered Functional Connectivity Patterns of Parietal Subregions Contribute to Cognitive Dysfunction in Patients with White Matter Hyperintensities

2021 ◽  
pp. 1-11
Author(s):  
Qiang Wei ◽  
Shanshan Cao ◽  
Yang Ji ◽  
Jun Zhang ◽  
Chen Chen ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
White Matter Hyperintensities ◽  
Parietal Lobe ◽  
Small Vessel Disease ◽  
Anterior Cingulate ◽  
Healthy Controls ◽  
Cingulate Gyrus ◽  
Anterior Cingulate Gyrus ◽  
Resting State Functional Connectivity

Background: The white matter hyperintensities (WMHs) are considered as one of the core neuroimaging findings of cerebral small vessel disease and independently associated with cognitive deficit. The parietal lobe is a heterogeneous area containing many subregions and play an important role in the processes of neurocognition. Objective: To explore the relationship between parietal subregions alterations and cognitive impairments in WHMs. Methods: Resting-state functional connectivity (rs-FC) analyses of parietal subregions were performed in 104 right-handed WMHs patients divided into mild (n = 39), moderate (n = 37), and severe WMHs (n = 28) groups according to the Fazekas scale and 36 healthy controls. Parietal subregions were defined using tractographic Human Brainnetome Atlas and included five subregions for superior parietal lobe, six subregions for inferior parietal lobe (IPL), and three subregions for precuneus. All participants underwent a neuropsychological test battery to evaluate emotional and general cognitive functions. Results: Differences existed between the rs-FC strength of IPL_R_6_2 with the left anterior cingulate gyrus, IPL_R_6_3 with the right dorsolateral superior frontal gyrus, and the IPL_R_6_5 with the left anterior cingulate gyrus. The connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus were correlated with AVLT-immediate and AVLT-recognition test in WMHs. Conclusion: We explored the roles of parietal subregions in WMHs using rs-FC. The functional connectivity of parietal subregions with the cortex regions showed significant differences between the patients with WMHs and healthy controls which may be associated with cognitive deficits in WMHs.

scholarly journals Traumatic Brain Injury as a Disorder of Brain Connectivity

2016 ◽  
Vol 22 (2) ◽  
pp. 120-137 ◽  
Author(s):  
Jasmeet P. Hayes ◽  
Erin D. Bigler ◽  
Mieke Verfaellie
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Functional Connectivity ◽  
Negative Impact ◽  
Axonal Injury ◽  
Resting State Functional Connectivity ◽  
Post Injury ◽  
Recent Advances

AbstractObjectives:Recent advances in neuroimaging methodologies sensitive to axonal injury have made it possible to assess in vivo the extent of traumatic brain injury (TBI) -related disruption in neural structures and their connections. The objective of this paper is to review studies examining connectivity in TBI with an emphasis on structural and functional MRI methods that have proven to be valuable in uncovering neural abnormalities associated with this condition.Methods:We review studies that have examined white matter integrity in TBI of varying etiology and levels of severity, and consider how findings at different times post-injury may inform underlying mechanisms of post-injury progression and recovery. Moreover, in light of recent advances in neuroimaging methods to study the functional connectivity among brain regions that form integrated networks, we review TBI studies that use resting-state functional connectivity MRI methodology to examine neural networks disrupted by putative axonal injury.Results:The findings suggest that TBI is associated with altered structural and functional connectivity, characterized by decreased integrity of white matter pathways and imbalance and inefficiency of functional networks. These structural and functional alterations are often associated with neurocognitive dysfunction and poor functional outcomes.Conclusions:TBI has a negative impact on distributed brain networks that lead to behavioral disturbance. (JINS, 2016,22, 120–137)

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