Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques

Xifra‐Porxas, Alba; Ghosh, Arna; Mitsis, Georgios D.; Boudrias, Marie‐Hélène

doi:10.1016/j.neuroimage.2021.117822

Cited by 33 publications

(35 citation statements)

References 119 publications

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“…First, relative to the abundance of studies using structural MRI features, attempts employing functional measurements to predict brain age have been few. [16,17] This knowledge gap is surprising given that 1) growing evidence implies that changes in the brain's functional organization precede changes in anatomy; [18] 2) functional measurements have higher relevance to cognition and are more likely disrupted in developmental disorders. [19] Second, most studies concern more on improving prediction accuracy or testing the feasibility of brain age model in a cascade of psychiatric disorders than interpreting the predictive neuroimaging signatures, which can hamper gaining clinically and biologically meaningful insights into the underlying mechanisms involved.…”

Section: Introductionmentioning

confidence: 99%

A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

et al. 2022

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Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

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Section: Introductionmentioning

confidence: 99%

A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

et al. 2022

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“…The absorption and metabolism of drugs are commonly influenced by compound structural features, including MW, hydrogen bond donors or acceptors (HBDs or HBAs), and the number of rotatable bonds (RotBs). − The features’ values can be considered individual variables and comprise a data set for each compound. Dimensionality reduction algorithms have been widely used to reduce the number of variables and extract the representative features. − Principal component analysis (PCA) and discriminant analysis (DA) are two popular dimensionality reduction approaches, which are unsupervised and supervised methods, respectively . PCA uses a multiple-feature extraction model, which may provide a good overview to use to interpret the data .…”

Section: Results and Discussionmentioning

confidence: 99%

“…H NMR (500 MHz, DMSO-d 6 ): δ 7.48 (d, J = 8.7 Hz, 2H), 7.03 (d, J = 8.7 Hz, 2H), 3.18−3.14 (m, 4H), 2.89−2.74 (m, 4H). ESI [M + H] + (m/z): 231.10.4-(4-(4-(Trifluoromethyl)phenyl)piperazin-1-yl)but-2-yn-1-ol(34). General procedure B.…”

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confidence: 99%

Structural Feature Analyzation Strategies toward Discovery of Orally Bioavailable PROTACs of Bruton’s Tyrosine Kinase for the Treatment of Lymphoma

et al. 2022

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Bruton's tyrosine kinase proteolysis-targeting chimeras (BTK-PROTACs) have emerged as a promising approach to address the limitations of BTK inhibitors. However, conducting the rational discovery of orally bioavailable BTK-PROTACs presents significant challenges. In this study, dimensionality reduction analysis and model molecule validation were utilized to identify some key structural features for improving the oral absorption of BTK-PROTACs. The results were applied to optimize the newly discovered BTK-PROTACs B1 and B2. Compound C13 was discovered with improved oral bioavailability, high BTK degradation activity, and selectivity. It exhibited inhibitory effects against different hematologic cancer cells and attenuated the BTK-related signaling pathway. The oral administration of C13 effectively reduced BTK protein levels and suppressed tumor growth. This study led to the discovery of a new orally bioavailable BTK-PROTAC for the treatment of lymphoma, and we hope that the strategy will find wide utility.

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“…Most prior brain age algorithms have used features derived from structural T 1 -weighted MR images ( Franke et al, 2010 ; Gaser et al, 2013 ; Valizadeh et al, 2017 ; Wang J. et al, 2019 ). Less commonly, studies have predicted brain age with diffusion-weighted MRI ( Mwangi et al, 2013 ; Tonnesen et al, 2020 ; Beck et al, 2021 ), functional MRI ( Liem et al, 2017 ), MR angiography ( Nam et al, 2020 ), FDG PET ( Goyal et al, 2019 ), EEG ( Sun et al, 2019 ; Paixao et al, 2020 ), or MEG ( Engemann et al, 2020 ; Xifra-Porxas et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging

Besson

Rogalskı

Gill

et al. 2022

Front. Aging Neurosci.

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BackgroundBrain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures.MethodsMRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject’s age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer’s disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD.FindingsUsing an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala.ConclusionAlthough similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

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Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques

Cited by 33 publications

References 119 publications

A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity

Structural Feature Analyzation Strategies toward Discovery of Orally Bioavailable PROTACs of Bruton’s Tyrosine Kinase for the Treatment of Lymphoma

Geometric deep learning reveals a structuro-temporal understanding of healthy and pathologic brain aging

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