Genetic variants for head size share genes and pathways with cancer

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Intensive Blood Pressure Treatment and Subclinical Brain Infarcts: A Secondary Analysis of SPRINT (Systolic Pressure Intervention Trial)

OBJECTIVE

Subclinical brain infarcts (SBI) increase the risk for stroke and dementia, but whether they should be considered equivalent to symptomatic stroke when determining blood pressure targets remains unclear. We tested whether intensive systolic blood pressure (SBP) treatment reduced the risk of new SBI or stroke and determined the association between SBI and cognitive impairment.

METHODS

In this secondary analysis of SPRINT (Systolic Pressure Intervention Trial), participants ≥50 years old, with SBP 130-180mmHg and elevated cardiovascular risk but without known clinical stroke, dementia, or diabetes, were randomized to intensive (<120mmHg) or standard (<140mmHg) SBP treatment. Brain magnetic resonance images collected at baseline and follow-up were read for SBI. The occurrence of mild cognitive impairment (MCI) or probable dementia (PD) was evaluated.

RESULTS

For 667 participants at baseline, SBI were identified in 75 (11%). At median 3.9 years follow-up, 12 of 457 had new SBI on magnetic resonance imaging (5 intensive, 7 standard), whereas 8 had clinical stroke (4 per group). Baseline SBI (subhazard ratio [sHR] = 3.90; 95% CI 1.49 to 10.24; p = 0.006), but not treatment group, was associated with new SBI or stroke. For participants with baseline SBI, intensive treatment reduced their risk for recurrent SBI or stroke (sHR = 0.050; 95% CI 0.0031 to 0.79; p = 0.033). Baseline SBI also increased risk for MCI or PD during follow-up (sHR = 2.38; 95% CI 1.23 to 4.61; p = 0.010).

INTERPRETATION

New cerebral ischemic events were infrequent, but intensive treatment mitigated the increased risk for participants with baseline SBI, indicating primary prevention SBP goals are still appropriate when SBI are present. ANN NEUROL 2024;95:866-875.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

Importance

Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases.

Objective

To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories.

Design, Setting, and Participants

Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points.

Exposures

Individuals WODCI at baseline scan.

Main Outcomes and Measures

Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed.

Results

In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7).

Conclusions and Relevance

The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

MRI Investigation of the Association of Left Atrial and Left Atrial Appendage Hemodynamics with Silent Brain Infarction

BACKGROUND

Left atrial (LA) myopathy is thought to be associated with silent brain infarctions (SBI) through changes in blood flow hemodynamics leading to thrombogenesis. 4D-flow MRI enables in-vivo hemodynamic quantification in the left atrium (LA) and LA appendage (LAA).

PURPOSE

To determine whether LA and LAA hemodynamic and volumetric parameters are associated with SBI.

STUDY TYPE

Prospective observational study.

POPULATION

A single-site cohort of 125 Participants of the multiethnic study of atherosclerosis (MESA), mean age: 72.3 ± 7.2 years, 56 men.

FIELD STRENGTH/SEQUENCE

1.5T. Cardiac MRI: Cine balanced steady state free precession (bSSFP) and 4D-flow sequences. Brain MRI: T1- and T2-weighted SE and FLAIR.

ASSESSMENT

Presence of SBI was determined from brain MRI by neuroradiologists according to routine diagnostic criteria in all participants without a history of stroke based on the MESA database. Minimum and maximum LA volumes and ejection fraction were calculated from bSSFP data. Blood stasis (% of voxels <10 cm/sec) and peak velocity (cm/sec) in the LA and LAA were assessed by a radiologist using an established 4D-flow workflow.

STATISTICAL TESTS

Student's t test, Mann-Whitney U test, one-way ANOVA, chi-square test. Multivariable stepwise logistic regression with automatic forward and backward selection. Significance level P < 0.05.

RESULTS

26 (20.8%) had at least one SBI. After Bonferroni correction, participants with SBI were significantly older and had significantly lower peak velocities in the LAA. In multivariable analyses, age (per 10-years) (odds ratio (OR) = 1.99 (95% confidence interval (CI): 1.30-3.04)) and LAA peak velocity (per cm/sec) (OR = 0.87 (95% CI: 0.81-0.93)) were significantly associated with SBI.

CONCLUSION

Older age and lower LAA peak velocity were associated with SBI in multivariable analyses whereas volumetric-based measures from cardiac MRI or cardiovascular risk factors were not. Cardiac 4D-flow MRI showed potential to serve as a novel imaging marker for SBI.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Heart rate fragmentation and brain MRI markers of small vessel disease in MESA

INTRODUCTION

Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored.

METHODS

In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later.

RESULTS

In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03).

DISCUSSION

HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.

The Association of Upper Airway Anatomy with Brain Structure: The Multi-Ethnic Study of Atherosclerosis

Sleep apnea, affecting an estimated 1 in 4 American adults, has been reported to be associated with both brain structural abnormality and impaired cognitive function. Obstructive sleep apnea is known to be affected by upper airway anatomy. To better understand the contribution of upper airway anatomy to pathways linking sleep apnea with impaired cognitive function, we investigated the association of upper airway anatomy with structural brain abnormalities. Based in the Multi-Ethnic Study of Atherosclerosis, a longitudinal cohort study of community-dwelling adults, a comprehensive sleep study and an MRI of the upper airway and brain were performed on 578 participants. Machine learning models were used to select from 74 upper airway measures those measures most associated with selected regional brain volumes and white matter hyperintensity volume. Linear regression assessed associations between the selected upper airway measures, sleep measures, and brain structure. Maxillary divergence was positively associated with hippocampus volume, and mandible length was negatively associated with total white and gray matter volume. Both coefficients were small (coefficients per standard deviation 0.063 mL, p = 0.04, and - 7.0 mL, p < 0.001 respectively), and not affected by adjustment for sleep study measures. Self-reported snoring >2 times per week was associated with larger hippocampus volume (coefficient 0.164 mL, p = 0.007), and higher percentage of time in the N3 sleep stage was associated with larger total white and gray matter volume (4.8 mL, p = 0.004). Despite associations of two upper airway anatomy measures with brain volume, the evidence did not suggest that these upper airway and brain structure associations were acting primarily through the pathway of sleep disturbance.

Genomic loci influence patterns of structural covariance in the human brain

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

The association of upper airway anatomy with cognitive test performance: the Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Numerous upper airway anatomy characteristics are risk factors for sleep apnea, which affects 26% of older Americans, and more severe sleep apnea is associated with cognitive impairment. This study explores the pathophysiology and links between upper airway anatomy, sleep, and cognition.

METHODS

Participants in the Multi-Ethnic Study of Atherosclerosis underwent an upper airway MRI, polysomnography to assess sleep measures including the apnea-hypopnea index (AHI) and completed the Cognitive Abilities Screening Instrument (CASI). Two model selection techniques selected from among 67 upper airway measures those that are most strongly associated with CASI score. The associations of selected upper airway measures with AHI, AHI with CASI score, and selected upper airway anatomy measures with CASI score, both alone and after adjustment for AHI, were assessed using linear regression.

RESULTS

Soft palate volume, maxillary divergence, and upper facial height were significantly positively associated with higher CASI score, indicating better cognition. The coefficients were small, with a 1 standard deviation (SD) increase in these variables being associated with a 0.83, 0.75, and 0.70 point higher CASI score, respectively. Additional adjustment for AHI very slightly attenuated these associations. Larger soft palate volume was significantly associated with higher AHI (15% higher AHI (95% CI 2%,28%) per SD). Higher AHI was marginally associated with higher CASI score (0.43 (95% CI 0.01,0.85) per AHI doubling).

CONCLUSIONS

Three upper airway measures were weakly but significantly associated with higher global cognitive test performance. Sleep apnea did not appear to be the mechanism through which these upper airway and cognition associations were acting. Further research on the selected upper airway measures is recommended.

Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA

INTRODUCTION

Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations.

METHODS

In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review.

RESULTS

Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy.

DISCUSSION

Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.

Measures of MRI Brain Biomarkers in Middle Age According to Average Modified Mediterranean Diet Scores Throughout Young and Middle Adulthood

BACKGROUND

The Mediterranean diet (MedDiet) has been linked with better cognitive function and brain integrity.

OBJECTIVE

To examine the association of modified Mediterranean diet (mMedDiet) scores from early through middle adulthood in relation to volumetric and microstructural midlife MRI brain measures. Assess the association of mMedDiet and brain measures with four cognitive domains. If variables are correlated, determine if brain measures mediate the relationship between mMedDiet and cognition.

METHODS

618 participants (mean age 25.4±3.5 at year 0) of the Coronary Artery Risk Development in Young Adults (CARDIA) study were included. Cumulative average mMedDiet scores were calculated by averaging scores from years 0, 7, and 20. MRI scans were obtained at years 25 and 30. General linear models were used to examine the association between mMedDiet and brain measures.

RESULTS

Higher cumulative average mMedDiet scores were associated with better microstructural white matter (WM) integrity measured by fractional anisotropy (FA) at years 25 and 30 (all ptrend <0.05). Higher mMedDiet scores at year 7 were associated with higher WM FA at year 25 (β= 0.003, ptrend = 0.03). Higher mMedDiet scores at year 20 associated with higher WM FA at years 25 (β= 0.0005, ptrend = 0.002) and 30 (β= 0.0003, ptrend = 0.02). mMedDiet scores were not associated with brain volumes. Higher mMedDiet scores and WM FA were both correlated with better executive function, processing speed, and global cognition (all ptrend <0.05). WM FA did not mediate the association between mMedDiet scores and cognition.

CONCLUSIONS

mMedDiet scores may be associated with microstructural WM integrity at midlife.

Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes

Importance

Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging.

Objective

To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices.

Design, Setting, and Participants

This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022.

Exposure

T1D diagnosis.

Main Outcomes and Measures

Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy.

Results

This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: β, 6.16; SE, 0.71; control participants: β, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (β, -0.04; SE, 0.01; P < .001), but not among controls.

Conclusions and Relevance

The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.

Intensive Blood Pressure Management Preserves Functional Connectivity in Patients with Hypertension from the Systolic Blood Pressure Intervention Randomized Trial

BACKGROUND AND PURPOSE

The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure.

MATERIALS AND METHODS

Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics.

RESULTS

Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013).

CONCLUSIONS

Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis

Importance

Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain.

Objective

Toperform a whole-brain investigation of ePVSs in a large community-based cohort.

Design, Setting, and Participants

This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included.

Main Outcomes and Measures

Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models.

Results

In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (β = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (β = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (β = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (β = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (β = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (β = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (β = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (β = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (β = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (β = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (β = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (β = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (β = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2).

Conclusions and Relevance

In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

Integrative diagnostics: the time is now-a report from the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers: Post Hoc Secondary Analysis of the SPRINT MIND Randomized Clinical Trial

IMPORTANCE

Little is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers.

OBJECTIVE

To investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF).

DESIGN, SETTING, AND PARTICIPANTS

The Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target <120 mm Hg) vs standard control (SBP target <140 mm Hg) among participants aged 50 years or older with hypertension and without diabetes or a history of stroke. The study began randomization on November 8, 2010, and stopped July 1, 2016, with a follow-up duration of approximately 4 years. A total of 670 and 458 participants completed brain magnetic resonance imaging at baseline and follow-up, respectively, and comprise the cohort for this post hoc analysis. Statistical analyses for this post hoc analysis were performed between August 2020 and October 2022.

INTERVENTIONS

At baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment.

MAIN OUTCOMES AND MEASURES

The main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest).

RESULTS

At baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]).

CONCLUSIONS AND RELEVANCE

This study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01206062.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Metabolic and Vascular Risk Factor Variability Over 25 Years Relates to Midlife Brain Volume and Cognition

BACKGROUND

Metabolic and vascular risk factors (MVRF) are associated with neurodegeneration and poor cognition. There is a need to better understand the impact of these risk factors on brain health in the decades that precede cognitive impairment. Longitudinal assessments can provide new insight regarding changes in MVRFs that are related to brain imaging features.

OBJECTIVE

To investigate whether longitudinal changes in MVRF spanning up to 25 years would be associated with midlife brain volume and cognition.

METHODS

Participants were from the CARDIA study (N = 467, age at year 25 = 50.6±3.4, female/male = 232/235, black/white = 161/306). Three models were developed, each designed to capture change over time; however, we were primarily interested in the average real variability (ARV) as a means of quantifying MVRF variability across all available assessments.

RESULTS

Multivariate partial least squares that used ARV metrics identified two significant latent variables (partial correlations ranged between 0.1 and 0.26, p < 0.01) that related MVRF ARV and regional brain volumes. Both latent variables reflected associations between brain volume and MVRF ARV in obesity, cholesterol, blood pressure, and glucose. Subsequent bivariate correlations revealed associations among MVRF factors, aggregate brain volume and cognition.

CONCLUSION

This study demonstrates that MVRF variability over time is associated with midlife brain volume in regions that are relevant to later-life cognitive decline.

Integrative Diagnostics: The Time Is Now-A Report From the International Society for Strategic Studies in Radiology

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

Left Atrial Function and Arrhythmias in Relation to Small Vessel Disease on Brain MRI: The Multi-Ethnic Study of Atherosclerosis

Background

Atrial fibrillation (AF) is associated with increased stroke risk and accelerated cognitive decline, but the association of early manifestations of left atrial (LA) impairment with subclinical changes in brain structure is unclear. We investigated whether abnormal LA structure and function, greater supraventricular ectopy, and intermittent AF are associated with small vessel disease on magnetic resonance imaging of the brain.

Methods and Results

In the Multi‐Ethnic Study of Atherosclerosis, 967 participants completed 14‐day ambulatory electrocardiographic monitoring, speckle tracking echocardiography and, a median 17 months later, magnetic resonance imaging of the brain. We assessed associations of LA volume index and reservoir strain, supraventricular ectopy, and prevalent AF with brain magnetic resonance imaging measures of small vessel disease and atrophy. The mean age of participants was 72 years; 53% were women. In multivariable models, LA enlargement was associated with lower white matter fractional anisotropy and greater prevalence of microbleeds; reduced LA strain, indicating worse LA function, was associated with more microbleeds. More premature atrial contractions were associated with lower total gray matter volume. Compared with no AF, intermittent AF (prevalent AF with <100% AF during electrocardiographic monitoring) was associated with lower white matter fractional anisotropy (−0.25 SDs [95% CI, −0.44 to −0.07]) and greater prevalence of microbleeds (prevalence ratio: 1.42 [95% CI, 1.12–1.79]).

Conclusions

In individuals without a history of stroke or transient ischemic attack, alterations of LA structure and function, including enlargement, reduced strain, frequent premature atrial contractions, and intermittent AF, were associated with increased markers of small vessel disease. Detailed assessment of LA structure and function and extended ECG monitoring may enable early identification of individuals at greater risk of small vessel disease.

A population-based meta-analysis of circulating GFAP for cognition and dementia risk

OBJECTIVE

Expression of glial fibrillary acidic protein (GFAP), a marker of reactive astrocytosis, colocalizes with neuropathology in the brain. Blood levels of GFAP have been associated with cognitive decline and dementia status. However, further examinations at a population-based level are necessary to broaden generalizability to community settings.

METHODS

Circulating GFAP levels were assayed using a Simoa HD-1 analyzer in 4338 adults without prevalent dementia from four longitudinal community-based cohort studies. The associations between GFAP levels with general cognition, total brain volume, and hippocampal volume were evaluated with separate linear regression models in each cohort with adjustment for age, sex, education, race, diabetes, systolic blood pressure, antihypertensive medication, body mass index, apolipoprotein E ε4 status, site, and time between GFAP blood draw and the outcome. Associations with incident all-cause and Alzheimer's disease dementia were evaluated with adjusted Cox proportional hazard models. Meta-analysis was performed on the estimates derived from each cohort using random-effects models.

RESULTS

Meta-analyses indicated that higher circulating GFAP associated with lower general cognition (ß = -0.09, [95% confidence interval [CI]: -0.15 to -0.03], p = 0.005), but not with total brain or hippocampal volume (p > 0.05). However, each standard deviation unit increase in log-transformed GFAP levels was significantly associated with a 2.5-fold higher risk of incident all-cause dementia (Hazard Ratio [HR]: 2.47 (95% CI: 1.52-4.01)) and Alzheimer's disease dementia (HR: 2.54 [95% CI: 1.42-4.53]) over up to 15-years of follow-up.

INTERPRETATION

Results support the potential role of circulating GFAP levels for aiding dementia risk prediction and improving clinical trial stratification in community settings.

Brain Structure Among Middle-aged and Older Adults With Long-standing Type 1 Diabetes in the DCCT/EDIC Study

OBJECTIVE

Individuals with type 1 diabetes mellitus (T1DM) are living to ages when neuropathological changes are increasingly evident. We hypothesized that middle-aged and older adults with long-standing T1DM will show abnormal brain structure in comparison with control subjects without diabetes.

RESEARCH DESIGN AND METHODS

MRI was used to compare brain structure among 416 T1DM participants in the Epidemiology of Diabetes Interventions and Complications (EDIC) study with that of 99 demographically similar control subjects without diabetes at 26 U.S. and Canadian sites. Assessments included total brain (TBV) (primary outcome), gray matter (GMV), white matter (WMV), ventricle, and white matter hyperintensity (WMH) volumes and total white matter mean fractional anisotropy (FA). Biomedical assessments included HbA1c and lipid levels, blood pressure, and cognitive assessments of memory and psychomotor and mental efficiency (PME). Among EDIC participants, HbA1c, severe hypoglycemia history, and vascular complications were measured longitudinally.

RESULTS

Mean age of EDIC participants and control subjects was 60 years. T1DM participants showed significantly smaller TBV (least squares mean ± SE 1,206 ± 1.7 vs. 1,229 ± 3.5 cm3, P < 0.0001), GMV, and WMV and greater ventricle and WMH volumes but no differences in total white matter mean FA versus control subjects. Structural MRI measures in T1DM were equivalent to those of control subjects who were 4-9 years older. Lower PME scores were associated with altered brain structure on all MRI measures in T1DM participants.

CONCLUSIONS

Middle-aged and older adults with T1DM showed brain volume loss and increased vascular injury in comparison with control subjects without diabetes, equivalent to 4-9 years of brain aging.

Kidney Disease, Hypertension Treatment, and Cerebral Perfusion and Structure

RATIONALE & OBJECTIVE

The safety of intensive blood pressure (BP) targets is controversial for persons with chronic kidney disease (CKD). We studied the effects of hypertension treatment on cerebral perfusion and structure in individuals with and without CKD.

STUDY DESIGN

Neuroimaging substudy of a randomized trial.

SETTING & PARTICIPANTS

A subset of participants in the Systolic Blood Pressure Intervention Trial (SPRINT) who underwent brain magnetic resonance imaging studies. Presence of baseline CKD was assessed by estimated glomerular filtration rate (eGFR) and urinary albumin-creatinine ratio (UACR).

INTERVENTION

Participants were randomly assigned to intensive (systolic BP <120 mm Hg) versus standard (systolic BP <140 mm Hg) BP lowering.

OUTCOMES

The magnetic resonance imaging outcome measures were the 4-year change in global cerebral blood flow (CBF), white matter lesion (WML) volume, and total brain volume (TBV).

RESULTS

A total of 716 randomized participants with a mean age of 68 years were enrolled; follow-up imaging occurred after a median 3.9 years. Among participants with eGFR <60 mL/min/1.73 m2 (n = 234), the effects of intensive versus standard BP treatment on change in global CBF, WMLs, and TBV were 3.38 (95% CI, 0.32 to 6.44) mL/100 g/min, -0.06 (95% CI, -0.16 to 0.04) cm3 (inverse hyperbolic sine-transformed), and -3.8 (95% CI, -8.3 to 0.7) cm3, respectively. Among participants with UACR >30 mg/g (n = 151), the effects of intensive versus standard BP treatment on change in global CBF, WMLs, and TBV were 1.91 (95% CI, -3.01 to 6.82) mL/100 g/min, 0.003 (95% CI, -0.13 to 0.13) cm3 (inverse hyperbolic sine-transformed), and -7.0 (95% CI, -13.3 to -0.3) cm3, respectively. The overall treatment effects on CBF and TBV were not modified by baseline eGFR or UACR; however, the effect on WMLs was attenuated in participants with albuminuria (P = 0.04 for interaction).

LIMITATIONS

Measurement variability due to multisite design.

CONCLUSIONS

Among adults with hypertension who have primarily early kidney disease, intensive versus standard BP treatment did not appear to have a detrimental effect on brain perfusion or structure. The findings support the safety of intensive BP treatment targets on brain health in persons with early kidney disease.

FUNDING

SPRINT was funded by the National Institutes of Health (including the National Heart, Lung, and Blood Institute; the National Institute of Diabetes and Digestive and Kidney Diseases; the National Institute on Aging; and the National Institute of Neurological Disorders and Stroke), and this substudy was funded by the National Institutes of Diabetes and Digestive and Kidney Diseases.

TRIAL REGISTRATION

SPRINT was registered at ClinicalTrials.gov with study number NCT01206062.

Association of Intensive vs Standard Blood Pressure Control With Cerebral Blood Flow: Secondary Analysis of the SPRINT MIND Randomized Clinical Trial

IMPORTANCE

Antihypertensive treatments benefit cerebrovascular health and cognitive function in patients with hypertension, but it is uncertain whether an intensive blood pressure target leads to potentially harmful cerebral hypoperfusion.

OBJECTIVE

To investigate the association of intensive systolic blood pressure (SBP) control vs standard control with whole-brain cerebral blood flow (CBF).

DESIGN, SETTING, AND PARTICIPANTS

This substudy of the Systolic Blood Pressure Intervention Trial (SPRINT) randomized clinical trial compared the efficacy of 2 different blood pressure-lowering strategies with longitudinal brain magnetic resonance imaging (MRI) including arterial spin labeled perfusion imaging to quantify CBF. A total of 1267 adults 50 years or older with hypertension and increased cardiovascular risk but free of diabetes or dementia were screened for the SPRINT substudy from 6 sites in the US. Randomization began in November 2010 with final follow-up MRI in July 2016. Analyses were performed from September 2020 through December 2021.

INTERVENTIONS

Study participants with baseline CBF measures were randomized to an intensive SBP target less than 120 mm Hg or standard SBP target less than 140 mm Hg.

MAIN OUTCOMES AND MEASURES

The primary outcome was change in whole-brain CBF from baseline. Secondary outcomes were change in gray matter, white matter, and periventricular white matter CBF.

RESULTS

Among 547 participants with CBF measured at baseline, the mean (SD) age was 67.5 (8.1) years and 219 (40.0%) were women; 315 completed follow-up MRI at a median (IQR) of 4.0 (3.7-4.1) years after randomization. Mean whole-brain CBF increased from 38.90 to 40.36 (difference, 1.46 [95% CI, 0.08-2.83]) mL/100 g/min in the intensive treatment group, with no mean increase in the standard treatment group (37.96 to 37.12; difference, -0.84 [95% CI, -2.30 to 0.61] mL/100 g/min; between-group difference, 2.30 [95% CI, 0.30-4.30; P = .02]). Gray, white, and periventricular white matter CBF showed similar changes. The association of intensive vs standard treatment with CBF was generally similar across subgroups defined by age, sex, race, chronic kidney disease, SBP, orthostatic hypotension, and frailty, with the exception of an indication of larger mean increases in CBF associated with intensive treatment among participants with a history of cardiovascular disease (interaction P = .05).

CONCLUSIONS AND RELEVANCE

Intensive vs standard antihypertensive treatment was associated with increased, rather than decreased, cerebral perfusion, most notably in participants with a history of cardiovascular disease.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01206062.

Association of Brain Volumes and White Matter Injury With Race, Ethnicity, and Cardiovascular Risk Factors: The Multi-Ethnic Study of Atherosclerosis

Background Cardiovascular risk factors are associated with cognitive decline and dementia. Magnetic resonance imaging provides sensitive measurement of brain morphology and vascular brain injury. However, associations of risk factors with brain magnetic resonance imaging findings have largely been studied in White participants. We investigated associations of race, ethnicity, and cardiovascular risk factors with brain morphology and white matter (WM) injury in a diverse population. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, measures were made in 2018 to 2019 of total brain volume, gray matter and WM volume, and WM injury, including WM hyperintensity volume and WM fractional anisotropy. We assessed cross-sectional associations of race and ethnicity and of cardiovascular risk factors with magnetic resonance imaging measures. Magnetic resonance imaging data were complete in 1036 participants; 25% Black, 15% Chinese-American, 19% Hispanic, and 41% White. Mean (SD) age was 72 (8) years and 53% were women. Although WM injury was greater in Black than in White participants in a minimally adjusted model, additional adjustment for cardiovascular risk factors and socioeconomic status each attenuated this association, rendering it nonsignificant. Overall, greater average WM hyperintensity volume was associated with older age and current smoking (69% greater vs never smoking); lower fractional anisotropy was additionally associated with higher diastolic blood pressure, use of antihypertensive medication, and diabetes. Conclusions We found no statistically significant difference in measures of WM injury by race and ethnicity after adjustment for cardiovascular risk factors and socioeconomic status. In all racial and ethnic groups, older age, current smoking, hypertension, and diabetes were strongly associated with WM injury.

Association of Early Adulthood 25-Year Blood Pressure Trajectories With Cerebral Lesions and Brain Structure in Midlife

IMPORTANCE

Midlife elevated blood pressure (BP) is an important risk factor associated with brain structure and function. Little is known about trajectories of BP that modulate this risk.

OBJECTIVE

To identify BP trajectory patterns from young adulthood to midlife that are associated with brain structure in midlife.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used data of US adults from Coronary Artery Risk Development in Young Adults (CARDIA), a prospective longitudinal study of Black and White men and women (baseline age 18 to 30 years) examined up to 8 times over 30 years (1985-1986 to 2015-2016). There were 885 participants who underwent brain magnetic resonance imaging (MRI) in the 25th or 30th year examinations. Analyses were conducted November 2019 to December 2020.

EXPOSURES

Using group-based trajectory modeling, 5 25-year BP trajectories for 3 BP traits were identified in the total CARDIA cohort of participants with 3 or more BP measures, which were then applied to analyses of the subset of 853 participants in the Brain MRI substudy. Mean arterial pressure (MAP) was examined as an integrative measure of systolic and diastolic BP. With linear regression, the associations of the BP trajectories with brain structures were examined, adjusting sequentially for demographics, cardiovascular risk factors, and antihypertensive medication use.

MAIN OUTCOMES AND MEASURES

Brain MRI outcomes include total brain, total gray matter, normal-looking and abnormal white matter volumes, gray matter cerebral blood flow, and white matter fractional anisotropy.

RESULTS

Brain MRI analyses were conducted on 853 participants (mean [SD] age, 50.3 [3.6] years; 399 [46.8%] men; 354 [41.5%] Black and 499 [58.5%] White individuals). The MAP trajectory distribution was 187 individuals (21.1%) with low-stable, 385 (43.5%) with moderate-gradual, 71 (8.0%) with moderate-increasing, 204 (23.1%) with elevated-stable, and 38 (4.3%) with elevated-increasing. Compared with the MAP low-stable trajectory group, individuals in the moderate-increasing and elevated-increasing groups were more likely to have higher abnormal white matter volume (moderate: β, 0.52; 95% CI, 0.23 to 0.82; elevated: β, 0.57; 95% CI, 0.19 to 0.95). Those in the MAP elevated-increasing group had lower gray matter cerebral blood flow (β, -0.42; 95% CI, -0.79 to -0.05) after adjusting for sociodemographics and cardiovascular risk factors. After adjustment for antihypertensive medication use, the difference was consistent for abnormal white matter volume, but results were no longer significant for gray matter cerebral blood flow.

CONCLUSIONS AND RELEVANCE

Among young adults with moderate to high levels of BP, a gradual increase in BP to middle-age may increase the risk in diffuse small vessel disease and lower brain perfusion.

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors

BACKGROUND

In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability.

PURPOSE

To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction.

STUDY TYPE

Retrospective.

POPULATION

Eight thousand eight hundred and seventy-six subjects from six sites. Harmonization models were trained using all subjects. Age prediction models were trained using 2739 subjects from a single site and tested using the remaining 6137 subjects from various other sites.

FIELD STRENGTH/SEQUENCE

Brain imaging with magnetization prepared rapid acquisition with gradient echo or spoiled gradient echo sequences at 1.5 T and 3 T.

ASSESSMENT

StarGAN v2, was used to perform a canonical mapping from diverse datasets to a reference domain to reduce site-based variation while preserving semantic information. Generalization performance of deep learning age prediction was evaluated using harmonized, histogram matched, and unharmonized data.

STATISTICAL TESTS

Mean absolute error (MAE) and Pearson correlation between estimated age and biological age quantified the performance of the age prediction model.

RESULTS

Our results indicated a substantial improvement in age prediction in out-of-sample data, with the overall MAE improving from 15.81 (±0.21) years to 11.86 (±0.11) with histogram matching to 7.21 (±0.22) years with generative adversarial network (GAN)-based harmonization. In the multisite case, across the 5 out-of-sample sites, MAE improved from 9.78 (±6.69) years to 7.74 (±3.03) years with histogram normalization to 5.32 (±4.07) years with GAN-based harmonization.

DATA CONCLUSION

While further research is needed, GAN-based medical image harmonization appears to be a promising tool for improving cross-site deep learning generalization.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 1.

Automated multiclass tissue segmentation of clinical brain MRIs with lesions

•

A U-Net incorporating spatial prior information can successfully segment 6 brain tissue types.

•

The U-Net was able to segment gray and white matter in the presence of lesions.

•

The U-Net surpassed the performance of its source algorithm in an external dataset.

•

Segmentations were produced in a hundredth of the time of its predecessor algorithm.

Delineation and quantification of normal and abnormal brain tissues on Magnetic Resonance Images is fundamental to the diagnosis and longitudinal assessment of neurological diseases. Here we sought to develop a convolutional neural network for automated multiclass tissue segmentation of brain MRIs that was robust at typical clinical resolutions and in the presence of a variety of lesions. We trained a 3D U-Net for full brain multiclass tissue segmentation from a prior atlas-based segmentation method on an internal dataset that consisted of 558 clinical T1-weighted brain MRIs (453/52/53; training/validation/test) of patients with one of 50 different diagnostic entities (n = 362) or with a normal brain MRI (n = 196). We then used transfer learning to refine our model on an external dataset that consisted of 7 patients with hand-labeled tissue types. We evaluated the tissue-wise and intra-lesion performance with different loss functions and spatial prior information in the validation set and applied the best performing model to the internal and external test sets. The network achieved an average overall Dice score of 0.87 and volume similarity of 0.97 in the internal test set. Further, the network achieved a median intra-lesion tissue segmentation accuracy of 0.85 inside lesions within white matter and 0.61 inside lesions within gray matter. After transfer learning, the network achieved an average overall Dice score of 0.77 and volume similarity of 0.96 in the external dataset compared to human raters. The network had equivalent or better performance than the original atlas-based method on which it was trained across all metrics and produced segmentations in a hundredth of the time. We anticipate that this pipeline will be a useful tool for clinical decision support and quantitative analysis of clinical brain MRIs in the presence of lesions.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84–0.88 and 0.40–0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75–0.81 and 0.62–0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Association of Intensive vs Standard Blood Pressure Control With Magnetic Resonance Imaging Biomarkers of Alzheimer Disease: Secondary Analysis of the SPRINT MIND Randomized Trial

Importance

Meta-analyses of randomized clinical trials have indicated that improved hypertension control reduces the risk for cognitive impairment and dementia. However, it is unclear to what extent pathways reflective of Alzheimer disease (AD) pathology are affected by hypertension control.

Objective

To evaluate the association of intensive blood pressure control on AD-related brain biomarkers.

Design, Setting, and Participants

This is a substudy of the Systolic Blood Pressure Intervention Trial (SPRINT MIND), a multicenter randomized clinical trial that compared the efficacy of 2 different blood pressure-lowering strategies. Potential participants (n = 1267) 50 years or older with hypertension and without a history of diabetes or stroke were approached for a brain magnetic resonance imaging (MRI) study. Of these, 205 participants were deemed ineligible and 269 did not agree to participate; 673 and 454 participants completed brain MRI at baseline and at 4-year follow-up, respectively; the final follow-up date was July 1, 2016. Analysis began September 2019 and ended November 2020.

Interventions

Participants were randomized to either a systolic blood pressure goal of less than 120 mm Hg (intensive treatment: n = 356) or less than 140 mm Hg (standard treatment: n = 317).

Main Outcomes and Measures

Changes in hippocampal volume, measures of AD regional atrophy, posterior cingulate cerebral blood flow, and mean fractional anisotropy in the cingulum bundle.

Results

Among 673 recruited patients who had baseline MRI (mean [SD] age, 67.3 [8.2] years; 271 women [40.3%]), 454 completed the follow-up MRI at a median (interquartile range) of 3.98 (3.7-4.1) years after randomization. In the intensive treatment group, mean hippocampal volume decreased from 7.45 cm3 to 7.39 cm3 (difference, -0.06 cm3; 95% CI, -0.08 to -0.04) vs a decrease from 7.48 cm3 to 7.46 cm3 (difference, -0.02 cm3; 95% CI, -0.05 to -0.003) in the standard treatment group (between-group difference in change, -0.033 cm3; 95% CI, -0.062 to -0.003; P = .03). There were no significant treatment group differences for measures of AD regional atrophy, cerebral blood flow, or mean fractional anisotropy.

Conclusions and Relevance

Intensive treatment was associated with a small but statistically significant greater decrease in hippocampal volume compared with standard treatment, consistent with the observation that intensive treatment is associated with greater decreases in total brain volume. However, intensive treatment was not associated with changes in any of the other MRI biomarkers of AD compared with standard treatment.

Trial Registration

ClinicalTrials.gov Identifier: NCT01206062.

Cognitive Processing Speed Is Strongly Related to Driving Skills, Financial Abilities, and Other Instrumental Activities of Daily Living in Persons With Mild Cognitive Impairment and Mild Dementia

BACKGROUND

Cognitive processing speed is important for performing everyday activities in persons with mild cognitive impairment (MCI). However, its role in daily function has not been examined while simultaneously accounting for contributions of Alzheimer's disease (AD) risk biomarkers. We examine the relationships of processing speed and genetic and neuroimaging biomarkers to composites of daily function, mobility, and driving.

METHOD

We used baseline data from 103 participants on the MCI/mild dementia spectrum from the Applying Programs to Preserve Skills trial. Linear regression models examined relationships of processing speed, structural magnetic resonance imaging (MRI), and genetic risk alleles for AD to composites of performance-based instrumental activities of daily living (IADLs), community mobility, and on-road driving evaluations.

RESULTS

In multivariable models, processing speed and the brain MRI neurodegeneration biomarker Spatial Pattern of Abnormality for Recognition of Early Alzheimer's disease (SPARE-AD) were significantly associated with functional and mobility composite performance. Better processing speed and younger age were associated with on-road driving ratings. Genetic risk markers, left hippocampal atrophy, and white matter lesion volumes were not significant correlates of these abilities. Processing speed had a strong positive association with IADL function (p < .001), mobility (p < .001), and driving (p = .002).

CONCLUSIONS

Cognitive processing speed is strongly and consistently associated with critical daily functions in persons with MCI in models including genetic and neuroimaging biomarkers of AD risk. SPARE-AD scores also significantly correlate with IADL performance and mobility. Results highlight the central role of processing speed in everyday task performance among persons with MCI/mild dementia.

Cerebral small vessel disease genomics and its implications across the lifespan

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Carotid Intima-Media Thickness and Markers of Brain Health in a Biracial Middle-Aged Cohort: CARDIA Brain MRI Sub-study

BACKGROUND

We investigated whether carotid intima-media thickness is associated with measures of cerebral blood flow (CBF), white matter hyperintensities, and brain volume in a biracial cohort of middle-aged individuals.

METHODS

We performed a cross-sectional cohort study based on data from a multicenter, population-based study Coronary Artery Risk Development in Young Adults. Using linear and logistic regression, we estimated the association of the composite intima-media thickness measured in three segments of carotid arteries (common carotid artery, carotid artery bulb, and internal carotid artery) with volume (cm3) and CBF (mL/100 g/min) in the total brain and gray matter as well as volume of white matter hyperintensities (cm3).

RESULTS

In the analysis, 461 participants (54% women, 34% African Americans) were included. Greater intima-media thickness was associated with lower CBF in gray matter (β=-1.36; p = .04) and total brain (β=-1.26; p = .04), adjusting for age, sex, race, education, and total brain volume. The associations became statistically nonsignificant after further controlling for cardiovascular risk factors. Intima-media thickness was not associated with volumes of total brain, gray matter, and white matter hyperintensities.

CONCLUSIONS

This study suggests that lower CBF in middle age is associated with markers of atherosclerosis in the carotid arteries. This association may reflect early long-term exposure to traditional cardiovascular risk factors. Early intervention on atherosclerotic risk factors may modulate the trajectory of CBF as people age and develop brain pathology.

Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study

Purpose

To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies.

Materials and Methods

In this retrospective study, multimodal brain MRI scans from 212 patients (mean age, 55 years ± 17 [standard deviation]; 113 women) with 35 neurologic diseases and normal brain MRI scans obtained between January 2008 and January 2018 were included (110 patients in the training set, 102 patients in the test set). MRI scans from 178 patients (mean age, 48 years ± 17; 106 women) were used to supplement training of the neural networks. Three-dimensional convolutional neural networks and atlas-based image processing were used for extraction of 11 imaging features. Expert-derived Bayesian networks incorporating domain knowledge were used for differential diagnosis generation. The performance of the artificial intelligence (AI) system was assessed by comparing diagnostic accuracy with that of radiologists of varying levels of specialization by using the generalized estimating equation with robust variance estimator for the top three differential diagnoses (T3DDx) and the correct top diagnosis (TDx), as well as with receiver operating characteristic analyses.

Results

In the held-out test set, the imaging pipeline detected 11 key features on brain MRI scans with 89% accuracy (sensitivity, 81%; specificity, 95%) relative to academic neuroradiologists. The Bayesian network, integrating imaging features with clinical information, had an accuracy of 85% for T3DDx and 64% for TDx, which was better than that of radiology residents (n = 4; 56% for T3DDx, 36% for TDx; P < .001 for both) and general radiologists (n = 2; 53% for T3DDx, 31% for TDx; P < .001 for both). The accuracy of the Bayesian network was better than that of neuroradiology fellows (n = 2) for T3DDx (72%; P = .003) but not for TDx (59%; P = .19) and was not different from that of academic neuroradiologists (n = 2; 84% T3DDx, 65% TDx; P > .09 for both).

Conclusion

A hybrid AI system was developed that simultaneously provides a quantitative assessment of disease burden, explainable intermediate imaging features, and a probabilistic differential diagnosis that performed at the level of academic neuroradiologists. This type of approach has the potential to improve clinical decision making for common and rare diseases.Supplemental material is available for this article.© RSNA, 2020.

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

INTRODUCTION

Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

METHODS

Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

RESULTS

WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

DISCUSSION

A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Exome Chip Analysis Identifies Low-Frequency and Rare Variants in MRPL38 for White Matter Hyperintensities on Brain Magnetic Resonance Imaging

Background and Purpose- White matter hyperintensities (WMH) on brain magnetic resonance imaging are typical signs of cerebral small vessel disease and may indicate various preclinical, age-related neurological disorders, such as stroke. Though WMH are highly heritable, known common variants explain a small proportion of the WMH variance. The contribution of low-frequency/rare coding variants to WMH burden has not been explored. Methods- In the discovery sample we recruited 20 719 stroke/dementia-free adults from 13 population-based cohort studies within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, among which 17 790 were of European ancestry and 2929 of African ancestry. We genotyped these participants at ≈250 000 mostly exonic variants with Illumina HumanExome BeadChip arrays. We performed ethnicity-specific linear regression on rank-normalized WMH in each study separately, which were then combined in meta-analyses to test for association with single variants and genes aggregating the effects of putatively functional low-frequency/rare variants. We then sought replication of the top findings in 1192 adults (European ancestry) with whole exome/genome sequencing data from 2 independent studies. Results- At 17q25, we confirmed the association of multiple common variants in TRIM65, FBF1, and ACOX1 ( P<6×10^-7). We also identified a novel association with 2 low-frequency nonsynonymous variants in MRPL38 (lead, rs34136221; P_EA=4.5×10^-8) partially independent of known common signal ( P_{EA(conditional)}=1.4×10^-3). We further identified a locus at 2q33 containing common variants in NBEAL1, CARF, and WDR12 (lead, rs2351524; P_all=1.9×10^-10). Although our novel findings were not replicated because of limited power and possible differences in study design, meta-analysis of the discovery and replication samples yielded stronger association for the 2 low-frequency MRPL38 variants ( P_rs34136221=2.8×10^-8). Conclusions- Both common and low-frequency/rare functional variants influence WMH. Larger replication and experimental follow-up are essential to confirm our findings and uncover the biological causal mechanisms of age-related WMH.

Association of Intensive vs Standard Blood Pressure Control With Cerebral White Matter Lesions

Importance

The effect of intensive blood pressure lowering on brain health remains uncertain.

Objective

To evaluate the association of intensive blood pressure treatment with cerebral white matter lesion and brain volumes.

Design, Setting, and Participants

A substudy of a multicenter randomized clinical trial of hypertensive adults 50 years or older without a history of diabetes or stroke at 27 sites in the United States. Randomization began on November 8, 2010. The overall trial was stopped early because of benefit for its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. Brain magnetic resonance imaging (MRI) was performed on a subset of participants at baseline (n = 670) and at 4 years of follow-up (n = 449); final follow-up date was July 1, 2016.

Interventions

Participants were randomized to a systolic blood pressure (SBP) goal of either less than 120 mm Hg (intensive treatment, n = 355) or less than 140 mm Hg (standard treatment, n = 315).

Main Outcomes and Measures

The primary outcome was change in total white matter lesion volume from baseline. Change in total brain volume was a secondary outcome.

Results

Among 670 recruited patients who had baseline MRI (mean age, 67.3 [SD, 8.2] years; 40.4% women), 449 (67.0%) completed the follow-up MRI at a median of 3.97 years after randomization, after a median intervention period of 3.40 years. In the intensive treatment group, based on a robust linear mixed model, mean white matter lesion volume increased from 4.57 to 5.49 cm3 (difference, 0.92 cm3 [95% CI, 0.69 to 1.14]) vs an increase from 4.40 to 5.85 cm3 (difference, 1.45 cm3 [95% CI, 1.21 to 1.70]) in the standard treatment group (between-group difference in change, -0.54 cm3 [95% CI, -0.87 to -0.20]). Mean total brain volume decreased from 1134.5 to 1104.0 cm3 (difference, -30.6 cm3 [95% CI, -32.3 to -28.8]) in the intensive treatment group vs a decrease from 1134.0 to 1107.1 cm3 (difference, -26.9 cm3 [95% CI, 24.8 to 28.8]) in the standard treatment group (between-group difference in change, -3.7 cm3 [95% CI, -6.3 to -1.1]).

Conclusions and Relevance

Among hypertensive adults, targeting an SBP of less than 120 mm Hg, compared with less than 140 mm Hg, was significantly associated with a smaller increase in cerebral white matter lesion volume and a greater decrease in total brain volume, although the differences were small.

Trial Registration

ClinicalTrials.gov Identifier: NCT01206062.

Artificial intelligence for precision education in radiology

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.

White Matter Lesion Penumbra Shows Abnormalities on Structural and Physiologic MRIs in the Coronary Artery Risk Development in Young Adults Cohort

BACKGROUND AND PURPOSE

White matter lesions are 1 age-related manifestation of cerebrovascular disease, but subthreshold abnormalities have been identified in nonlesional WM. We hypothesized that structural and physiologic MR imaging findings of early cerebrovascular disease can be measured in middle-aged subjects in tissue adjacent to WM lesions, termed "penumbra."

MATERIALS AND METHODS

WM lesions were defined using automated segmentation in 463 subjects, 43-56 years of age, from the Coronary Artery Risk Development in Young Adults (CARDIA) longitudinal observational cohort study. We described 0- to 2-mm and 2- to 4-mm-thick spatially defined penumbral WM tissue ROIs as rings surrounding WM lesions. The remaining WM was defined as distant normal-appearing WM. Mean signal intensities were measured for FLAIR, T1-, and T2-weighted images, and from fractional anisotropy, mean diffusivity, CBF, and vascular reactivity maps. Group comparisons were made using Kruskal-Wallis and pair-wise t tests.

RESULTS

Lesion volumes averaged 0.738 ± 0.842 cm³ (range, 0.005-7.27 cm³). Mean signal intensity for FLAIR, T2, and mean diffusivity was increased, while T1, fractional anisotropy, and CBF were decreased in white matter lesions versus distant normal-appearing WM, with penumbral tissues showing graded intermediate values (corrected P < .001 for all group/parameter comparisons). Vascular reactivity was significantly elevated in white matter lesions and penumbral tissue compared with distant normal-appearing white matter (corrected P ≤ .001).

CONCLUSIONS

Even in relatively healthy 43- to 56-year-old subjects with small white matter lesion burden, structural and functional MR imaging in penumbral tissue reveals significant signal abnormalities versus white matter lesions and other normal WM. Findings suggest that the onset of WM injury starts by middle age and involves substantially more tissue than evident from focal white matter lesions visualized on structural imaging.

Impact of Intensive Lifestyle Intervention on Neural Food Cue Reactivity: Action for Health in Diabetes Brain Ancillary Study

OBJECTIVE

The Action for Health in Diabetes (Look AHEAD) research study was a randomized trial comparing the effects of an intensive lifestyle intervention (ILI) versus a diabetes support and education (DSE) control group in adults with type 2 diabetes and overweight or obesity. Functional magnetic resonance imaging was used to determine whether neural food cue reactivity differed for these groups 10 years after randomization.

METHODS

A total of 232 participants (ILI, n = 125, 72% female; DSE, n = 107, 64% female) were recruited at three of the Look AHEAD sites for functional magnetic resonance imaging. Neural response to high-calorie foods compared with nonfoods was assessed in DSE versus ILI. Exploratory correlations were conducted within ILI to identify regions in which activity was associated with degree of weight loss.

RESULTS

Voxel-wise whole-brain comparisons revealed greater reward-processing activity in left caudate for DSE compared with ILI and greater activity in attention- and visual-processing regions for ILI than DSE (P < 0.05, family-wise error corrected). Exploratory analyses revealed that greater weight loss among ILI participants from baseline was associated with brain activation indicative of increased cognitive control and attention and visual processing in response to high-calorie food cues (P < 0.001, uncorrected).

CONCLUSIONS

These findings suggest there may be legacy effects of participation in a behavioral weight loss intervention, with reduced reward-related activity and enhanced attention or visual processing in response to high-calorie foods.

Cigarette smoking and cerebral blood flow in a cohort of middle-aged adults

Cigarette smoking is often associated with dementia. This association is thought to be mediated by hypoperfusion; however, how smoking behavior relates to cerebral blood flow (CBF) remains unclear. Using data from the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (mean age = 50; n = 522), we examined the association between smoking behavior (status, cumulative pack-years, age at smoking initiation, and years since cessation) and CBF (arterial spin labeling) in brain lobes and regions linked to dementia. We used adjusted linear regression models and tested whether associations differed between current and former-smokers. Compared to never-smokers, former-smokers had lower CBF in the parietal and occipital lobes, cuneus, precuneus, putamen, and insula; in contrast, current-smokers did not have lower CBF. The relationship between pack-years and CBF was different between current and former-smokers (p for interaction < 0.05): Among current-smokers, higher pack-years were associated with higher occipital, temporal, cuneus, putamen, insula, hippocampus, and caudate CBF; former-smokers had lower caudate CBF with increasing pack-years. Results show links between smoking and CBF at middle-age in regions implicated in cognitive and compulsive/addictive processes. Differences between current and former smoking suggest that distinct pathological and/or compensatory mechanisms may be involved depending on the timing and history of smoking exposure.

Characterizing a perfusion-based periventricular small vessel region of interest

The periventricular white matter (PVWM) is supplied by terminal distributions of small vessels and is particularly susceptible to developing white matter lesions (WML) associated with cerebral small vessel disease (CSVD). We obtained group-averaged cerebral blood flow (CBF) maps from Arterial Spin Labeled (ASL) perfusion MRI data obtained in 436 middle-aged (50.4 ± 3.5 years) subjects in the NHLBI CARDIA study and in 61 elderly (73.3 ± 6.9 years) cognitively normal subjects recruited from the Penn Alzheimer's Disease Center (ADC) and found that the lowest perfused brain voxels are located within the PVWM. We constructed a white matter periventricular small vessel (PSV) region of interest (ROI) by empirically thresholding the group averaged CARDIA CBF map at CBF < 15 ml/100 g/min. Thereafter we compared CBF in the PSV ROI and in the remaining white matter (RWM) with the location and volume of WML measured with Fluid Attenuated Inversion Recovery (FLAIR) MRI. WM CBF was lower within WML than outside WML voxels (p < <0.0001) in both the PSV and RWM ROIs, however this difference was much smaller (p < <0.0001) in the PSV ROI than in the RWM suggesting a more homogenous reduction of CBF in the PSV region. Normalized WML volumes were significantly higher in the PSV ROI than in the RWM and in the elderly cohort as compared to the middle-aged cohort (p < <0.0001). Additionally, the PSV ROI showed a significantly (p = .001) greater increase in lesion volume than the RWM in the elderly ADC cohort than the younger CARDIA cohort. Considerable intersubject variability in PSV CBF observed in both study cohorts likely represents biological variability that may be predictive of future WML and/or cognitive decline. In conclusion, a data-driven PSV ROI defined by voxels with low perfusion in middle age defines a region with homogeneously reduced CBF that is particularly susceptible to progressive ischemic injury in elderly controls. PSV CBF may provide a mechanistically specific biomarker of CSVD.

Effect of Intensive vs Standard Blood Pressure Control on Probable Dementia: A Randomized Clinical Trial

IMPORTANCE

There are currently no proven treatments to reduce the risk of mild cognitive impairment and dementia.

OBJECTIVE

To evaluate the effect of intensive blood pressure control on risk of dementia.

DESIGN, SETTING, AND PARTICIPANTS

Randomized clinical trial conducted at 102 sites in the United States and Puerto Rico among adults aged 50 years or older with hypertension but without diabetes or history of stroke. Randomization began on November 8, 2010. The trial was stopped early for benefit on its primary outcome (a composite of cardiovascular events) and all-cause mortality on August 20, 2015. The final date for follow-up of cognitive outcomes was July 22, 2018.

INTERVENTIONS

Participants were randomized to a systolic blood pressure goal of either less than 120 mm Hg (intensive treatment group; n = 4678) or less than 140 mm Hg (standard treatment group; n = 4683).

MAIN OUTCOMES AND MEASURES

The primary cognitive outcome was occurrence of adjudicated probable dementia. Secondary cognitive outcomes included adjudicated mild cognitive impairment and a composite outcome of mild cognitive impairment or probable dementia.

RESULTS

Among 9361 randomized participants (mean age, 67.9 years; 3332 women [35.6%]), 8563 (91.5%) completed at least 1 follow-up cognitive assessment. The median intervention period was 3.34 years. During a total median follow-up of 5.11 years, adjudicated probable dementia occurred in 149 participants in the intensive treatment group vs 176 in the standard treatment group (7.2 vs 8.6 cases per 1000 person-years; hazard ratio [HR], 0.83; 95% CI, 0.67-1.04). Intensive BP control significantly reduced the risk of mild cognitive impairment (14.6 vs 18.3 cases per 1000 person-years; HR, 0.81; 95% CI, 0.69-0.95) and the combined rate of mild cognitive impairment or probable dementia (20.2 vs 24.1 cases per 1000 person-years; HR, 0.85; 95% CI, 0.74-0.97).

CONCLUSIONS AND RELEVANCE

Among ambulatory adults with hypertension, treating to a systolic blood pressure goal of less than 120 mm Hg compared with a goal of less than 140 mm Hg did not result in a significant reduction in the risk of probable dementia. Because of early study termination and fewer than expected cases of dementia, the study may have been underpowered for this end point.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01206062.

Emerging Applications of Artificial Intelligence in Neuro-Oncology

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

It is About "Time": Academic Neuroradiologist Time Distribution for Interpreting Brain MRIs

RATIONALE AND OBJECTIVES

Efficiency is central to current radiology practice. Knowledge of report generation timing is essential for workload optimization and departmental staffing decisions. Yet little research evaluates the distribution of activities performed by neuroradiologists in daily work.

MATERIALS AND METHODS

This observational study tracked radiologists interpreting 358 brain magnetic resonance imaging (MRI) in an academic practice over 9 months. We measured the total duration from study opening to report signing and times for five activities performed during this period: image viewing, report transcription, obtaining clinical data, education, and other. Attendings, fellows, and residents reading studies independently and attendings over-reading trainee-previewed studies were observed.

RESULTS

Ten attendings, 12 fellows, and 13 residents spent a mean of 11, 18, and 16 minutes reading brain MRIs independently. Mean duration was significantly different comparing attendings in all assignments to fellows (18.36 ± 1.05 minutes, p = 0.0001) or residents (16.31 ± 1.11 minutes, p = 0.001) but not between fellows/residents. Mean duration among attendings reading independently versus over-reading trainees was not statistically different. Attendings spent the same time on image viewing (4.07-5.33 minutes) with or without trainees. Attending transcription time was shortest when over-reading trainees (2.24 minutes) and longest when reading independently (4.20 minutes), demonstrating benefit of the draft report. Fellows and Residents spent longer on image viewing (7.14 minutes and 8.06 minutes, respectively) and transcription (7.02 minutes and 5.40 minutes, respectively) than attendings reading independently.

CONCLUSION

Neuroradiologist time/activity distributions for reading brain MRI studies were measured, setting the stage to establish a benchmark for future reference and suggesting opportunities for greater efficiency. Furthermore, report production time can be decreased when a draft report is available.

Relationships between cerebral structure and cognitive function in African Americans with type 2 diabetes

BACKGROUND

Relationships between cognitive function and brain structure remain poorly defined in African Americans with type 2 diabetes.

METHODS

Cognitive testing and cerebral magnetic resonance imaging in African Americans from the Diabetes Heart Study Memory IN Diabetes (n = 480) and Action to Control Cardiovascular Risk in Diabetes MIND (n = 104) studies were examined for associations. Cerebral gray matter volume (GMV), white matter volume (WMV) and white matter lesion volume (WMLV) and cognitive performance (Mini-mental State Exam [MMSE and 3MSE], Digit Symbol Coding (DSC), Stroop test, and Rey Auditory Verbal Learning Test) were recorded. Multivariable models adjusted for age, sex, BMI, scanner, intracranial volume, education, diabetes duration, HbA1c, LDL-cholesterol, smoking, hypertension and cardiovascular disease assessed associations between cognitive tests and brain volumes by study and meta-analysis.

RESULTS

Mean(SD) participant age was 60.1(7.9) years, diabetes duration 12.1(7.7) years, and HbA1c 8.3(1.7)%. In the fully-adjusted meta-analysis, lower GMV associated with poorer global performance on MMSE/3MSE (β̂ = 7.1 × 10^-3, SE 2.4 × 10^-3, p = 3.6 × 10^-3), higher WMLV associated with poorer performance on DSC (β̂ = -3 × 10^-2, SE 6.4 × 10^-3, p = 5.2 × 10^-5) and higher WMV associated with poorer MMSE/3MSE performance (β̂ = -7.1 × 10^-3, SE = 2.4 × 10^-3, p = 3.6 × 10^-3).

CONCLUSIONS

In African Americans with diabetes, smaller GMV and increased WMLV associated with poorer performance on tests of global cognitive and executive function. These data suggest that WML burden and gray matter atrophy associate with cognitive performance independent of diabetes-related factors in this population.