The Brain Chart of Aging: Machine‐learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans

Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a rare hereditary vasculopathy that primarily affects the brain, caused mostly by missense mutations of the <i>NOTCH3</i> gene which is located on chromosome 19. Clinically, it manifests as transient ischemic attacks and strokes in individuals under the age of 60 years without vascular risk factors. We report a 46-year-old male with a 9 and 3-month history of progressive unilateral lower limb weakness and dysarthria, respectively. He had a history of diabetes mellitus but no hypertension, hyperlipidemia, or smoking history. Both parents had a stroke at the age of 65 years. Neurological examination was significant for moderate dysarthria and reduced right upper limb dexterity. Magnetic resonance imaging (MRI) of the brain revealed extensive white matter disease, lacunar infarcts, and a few microhemorrhages. Electron microscopy of his skin biopsy showed electron-dense deposits of extracellular osmiophilic granular material adjacent to smooth muscle cells. <i>NOTCH3</i> gene analysis revealed a heterozygous typical mutation in exon 6. He was commenced on aspirin and atorvastatin. Over time, he became more dysarthric and demented. MRI revealed the progression of the white matter disease and a new right subcortical infarct. His aspirin was switched to clopidogrel, and donepezil was added. CADASIL should be considered among younger stroke patients with vascular risk factors, especially in the presence of widespread white matter disease. Genetic counselling may be needed after the diagnosis is made.

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451 - Estimating “Brain Age Gaps” in patients with brain injury: Applying machine learning to advanced neuroimaging techniques

International Psychogeriatrics ◽

10.1017/s1041610220003038 ◽

2020 ◽

Vol 32 (S1) ◽

pp. 171-171

Keyword(s):

Machine Learning ◽

Older Adults ◽

Brain Injury ◽

Brain Aging ◽

High Rate ◽

Intracranial Volume ◽

Increased Risk ◽

Age Range ◽

Brain Age ◽

The Brain

Introduction:A single moderate or severe TBI is associated with accelerated brain aging and increased risk for dementia. Despite the high rate of falls that result in brain injury in older adults, numerous factors such as genetic predisposition to Alzheimer’s disease, sex, education, age are also known to affect multiple age-sensitive neuroimaging markers.METHODS:Here we use the “brain age” metric being tested by the global consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), that employs machine learning to predict a person’s age from multiple age-sensitive imaging markers (e.g., hippocampal volume, regional cortical gray matter thickness, intracranial volume (ICV)), while also taking into account their sex and educational level. We will discuss results from brain injured patients ( n = 60; age range: 20-75 years) and healthy age-matched controls (n = 20 (20-75 years). We will compute the “brain age gap” – between a person’s actual chronological age and that predicted from their brain scan – and test relations between this measure and injury characteristics.RESULTS:In our pilot work, we predicted a person’s age from their MRI scan with a mean absolute error of about 5 years. ENIGMA’s current best model includes: (1) non-normalized brain volumetric measures as predictors including ICV, (2) separate models for males and females, (3) use of a large age range (12-80), and (4) Gaussian process regression (GPR).CONCLUSION:This “overall” marker of accelerated brain aging offers a metric that taps diverse sources of information, weighted by their relevance to brain aging, and is associated with decreased functionality in older adults.

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Nuclear Magnetic Resonance (NMR) Imaging in White Matter Disease of the Brain Using Spin-Echo Sequences

Journal of Computer Assisted Tomography ◽

10.1097/00004728-198304000-00016 ◽

1983 ◽

Vol 7 (2) ◽

pp. 290-294 ◽

Cited By ~ 61

Author(s):

I. R. Young ◽

C. P. Randell ◽

P. W. Kaplan ◽

A. James ◽

G. M. Bydder ◽

...

Keyword(s):

Nuclear Magnetic Resonance ◽

White Matter ◽

Magnetic Resonance ◽

Spin Echo ◽

Nmr Imaging ◽

White Matter Disease ◽

The Brain ◽

Spin Echo Sequences ◽

Nuclear Magnetic

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White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

10.1101/2020.10.19.344879 ◽

2020 ◽

Author(s):

Jingtao Wang ◽

Peter Kochunov ◽

Hemalatha Sampath ◽

Kathryn S. Hatch ◽

Meghann C. Ryan ◽

...

Keyword(s):

Machine Learning ◽

White Matter ◽

Brain Aging ◽

Diffusion Tensor ◽

Life Stage ◽

Temporal Trends ◽

Learning Model ◽

Chronological Age ◽

Imaging Features ◽

Machine Learning Model

AbstractWe hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age >30 group in patients (p < 0.001) but not in age ⩽ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = −0.176, p = 0.007) and processing speed (β = −0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.

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