Automated detection of early signs of irreversible ischemic change on CTA source images in patients with large vessel occlusion.

PURPOSE: To create and validate an automated pipeline for detection of early signs of irreversible ischemic change from admission CTA in patients with large vessel occlusion (LVO) stroke. METHODS: We retrospectively included 368 patients for training and 143 for external validation. All patients had anterior circulation LVO stroke, endovascular therapy with successful reperfusion, and follow-up diffusion-weighted imaging (DWI). We devised a pipeline to automatically segment Alberta Stroke Program Early CT Score (ASPECTS) regions and extracted their relative Hounsfield unit (rHU) values. We determined the optimal rHU cut points for prediction of final infarction in each ASPECT region, performed 10-fold cross-validation in the training set, and measured the performance via external validation in patients from another institute. We compared the model with an expert neuroradiologist for prediction of final infarct volume and poor functional outcome. RESULTS: We achieved a mean area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of 0.69±0.13, 0.69±0.09, 0.61±0.23, and 0.72±0.11 across all regions and folds in cross-validation. In the external validation cohort, we achieved a median [interquartile] AUC, accuracy, sensitivity, and specificity of 0.71 [0.68-0.72], 0.70 [0.68-0.73], 0.55 [0.50-0.63], and 0.74 [0.73-0.77], respectively. The rHU-based ASPECTS showed significant correlation with DWI-based ASPECTS (rS = 0.39, p<0.001) and final infarct volume (rS = -0.36, p<0.001). The AUC for predicting poor functional outcome was 0.66 (95%CI: 0.57-0.75). The predictive capabilities of rHU-based ASPECTS were not significantly different from the neuroradiologist's visual ASPECTS for either final infarct volume or functional outcome. CONCLUSIONS: Our study demonstrates the feasibility of an automated pipeline and predictive model based on relative HU attenuation of ASPECTS regions on baseline CTA and its non-inferior performance in predicting final infarction on post-stroke DWI compared to an expert human reader.

Diffusion-Weighted Imaging Fluid-Attenuated Inversion Recovery Mismatch on Portable, Low-Field Magnetic Resonance Imaging Among Acute Stroke Patients.

OBJECTIVE: For stroke patients with unknown time of onset, mismatch between diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) can guide thrombolytic intervention. However, access to MRI for hyperacute stroke is limited. Here, we sought to evaluate whether a portable, low-field (LF)-MRI scanner can identify DWI-FLAIR mismatch in acute ischemic stroke. METHODS: Eligible patients with a diagnosis of acute ischemic stroke underwent LF-MRI acquisition on a 0.064-T scanner within 24 h of last known well. Qualitative and quantitative metrics were evaluated. Two trained assessors determined the visibility of stroke lesions on LF-FLAIR. An image coregistration pipeline was developed, and the LF-FLAIR signal intensity ratio (SIR) was derived. RESULTS: The study included 71 patients aged 71 ± 14 years and a National Institutes of Health Stroke Scale of 6 (interquartile range 3-14). The interobserver agreement for identifying visible FLAIR hyperintensities was high (κ = 0.85, 95% CI 0.70-0.99). Visual DWI-FLAIR mismatch had a 60% sensitivity and 82% specificity for stroke patients <4.5 h, with a negative predictive value of 93%. LF-FLAIR SIR had a mean value of 1.18 ± 0.18 <4.5 h, 1.24 ± 0.39 4.5-6 h, and 1.40 ± 0.23 >6 h of stroke onset. The optimal cut-point for LF-FLAIR SIR was 1.15, with 85% sensitivity and 70% specificity. A cut-point of 6.6 h was established for a FLAIR SIR <1.15, with an 89% sensitivity and 62% specificity. INTERPRETATION: A 0.064-T portable LF-MRI can identify DWI-FLAIR mismatch among patients with acute ischemic stroke. Future research is needed to prospectively validate thresholds and evaluate a role of LF-MRI in guiding thrombolysis among stroke patients with uncertain time of onset. ANN NEUROL 2024.

Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival.

The mortality rate of acute intracerebral hemorrhage (ICH) can reach up to 40%. Although the radiomics of ICH have been linked to hematoma expansion and outcomes, no research to date has explored their correlation with mortality. In this study, we determined the admission non-contrast head CT radiomic correlates of survival in supratentorial ICH, using the Antihypertensive Treatment of Acute Cerebral Hemorrhage II (ATACH-II) trial dataset. We extracted 107 original radiomic features from n = 871 admission non-contrast head CT scans. The Cox Proportional Hazards model, Kaplan-Meier Analysis, and logistic regression were used to analyze survival. In our analysis, the "first-order energy" radiomics feature, a metric that quantifies the sum of squared voxel intensities within a region of interest in medical images, emerged as an independent predictor of higher mortality risk (Hazard Ratio of 1.64, p < 0.0001), alongside age, National Institutes of Health Stroke Scale (NIHSS), and baseline International Normalized Ratio (INR). Using a Receiver Operating Characteristic (ROC) analysis, "the first-order energy" was a predictor of mortality at 1-week, 1-month, and 3-month post-ICH (all p < 0.0001), with Area Under the Curves (AUC) of >0.67. Our findings highlight the potential role of admission CT radiomics in predicting ICH survival, specifically, a higher "first-order energy" or very bright hematomas are associated with worse survival outcomes.

Salary Equity in Academic Radiology Relative to Other Clinical Specialties.

BACKGROUND: Equity in faculty compensation in U.S. academic radiology physicians relative to other specialties is not well known. OBJECTIVE: The aim of this study is to assess salary equity in U.S. academic radiology physicians at different ranks relative to other clinical specialties. METHODS: The American Association of Medical Colleges (AAMC) Faculty Salary Survey was used to collect information for full-time faculty at U.S. medical schools. Financial compensation data were collected for 2023 for faculty with MD or equivalent degree in medical specialties, stratified by gender and rank. RESULTS: The AAMC Faculty Salary Survey data for 2023 included responses for 97,224 faculty members in clinical specialties, with 5847 faculty members in Radiology departments. In radiology, compared to men (n = 3839), the women faculty members (n = 1763) had a lower median faculty compensation by 6% at the rank of Assistant Professor, 3% for Associate Professors, 4% for Professors and 6% for Section Chief positions. Surgery had the highest difference in median compensation with 21%, 24%, 22% and 19% lower faculty compensation, respectively, for women faculty members at corresponding ranks. Pathology had the lowest percent difference (<1%) in median compensation for all professor ranks. Salary inequity in radiology was lower compared to most other specialties. From assistant to full professors, all other clinical specialties except Pathology and Psychiatry, had a greater salary inequity than Radiology. CONCLUSION: The salary inequity in academic radiology faculty is lower than most other specialties. Further efforts should be made to reduce salary inequities as broader efforts to provide a more diverse, equitable and inclusive environment. SUMMARY STATEMENT: Salary inequity in academic radiology faculty is lower than most other specialties.

Financial Conflicts of Interest Among Physician Authors of ACR Appropriateness Criteria.

BACKGROUND: The accuracy and completeness of self-disclosures by authors of imaging guidelines are not well known. OBJECTIVE: The aim of this study was to assess the accuracy of financial disclosures by US authors of ACR appropriateness criteria. METHODS: We reviewed financial disclosures provided by US-based authors of all ACR-AC published in 2019, 2021 and 2023. For each US- based author, payment reports were extracted from the Open Payments Database (OPD) in the previous 36 months related to general category and research payments categories. We analyzed each author individually to determine if the reported disclosures matched results from OPD. RESULTS: A total of 633 authorships, including 333 unique authors were included from 38 ACR AC articles in 2019, with 606 authorships (387 unique authors) from 35 ACR-AC articles published in 2021, and 540 authorships (367 unique authors) from 32 ACR AC articles published in 2023. Among authors who received industry payments, failure to disclose any financial relationship was seen in 125/147 unique authors in 2019, 142/148 authors in 2021 and 95/125 unique authors in 2023. The proportion of nondisclosed total value of payments was 86.1% in 2019, 88.6% in 2021 and 56.7% in 2023. General category payments were nondisclosed in 94.1% in 2019, 89.7% in 2021 and 94.4% in 2023 by payment value. CONCLUSION: Industry payments to authors of radiology guidelines are common and frequently undisclosed.

Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.

BACKGROUND: A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics. METHODS: We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy. RESULTS: We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort. CONCLUSIONS: Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.

Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.

Accuracy of Financial Disclosures in Radiology Journals.

PURPOSE: The accuracy and completeness of self-disclosures of the value of industry payments by authors publishing in radiology journals are not well known. The aim of this study was to assess the accuracy of financial disclosures by US authors in five prominent radiology journals. METHODS: Financial disclosures provided by US-based authors in five prominent radiology journals from original research and review articles published in 2021 were reviewed. For each author, payment reports were extracted from the Open Payments Database (OPD) in the previous 36 months related to general, research, and ownership payments. Each author was analyzed individually to determine if the reported disclosures matched results from the OPD. RESULTS: A total of 4,076 authorships, including 3,406 unique authors, were selected from 643 articles across the five journals; 1,388 (1,032 unique authors) received industry payments within the previous 36 months, with a median total amount received per authorship of $6,650 (interquartile range, $355-$87,725). Sixty-one authors (4.4%) disclosed all industry relationships, 205 (14.8%) disclosed some of the OPD-reported relationships, and 1,122 (80.8%) failed to disclose any relationships. Undisclosed payments totaled $186,578,350, representing 67.2% of all payments. Radiology had the highest proportion of authorships disclosing some or all OPD-reported relationships (32.3%), compared with the Journal of Vascular and Interventional Radiology (18.2%), the American Journal of Neuroradiology (17.3%), JACR (13.1%), and the American Journal of Roentgenology (10.3%). CONCLUSIONS: Financial relationships with industry are common among US physician authors in prominent radiology journals, and nondisclosure rates are high.

Uncertainty-aware deep-learning model for prediction of supratentorial hematoma expansion from admission non-contrast head computed tomography scan.

Hematoma expansion (HE) is a modifiable risk factor and a potential treatment target in patients with intracerebral hemorrhage (ICH). We aimed to train and validate deep-learning models for high-confidence prediction of supratentorial ICH expansion, based on admission non-contrast head Computed Tomography (CT). Applying Monte Carlo dropout and entropy of deep-learning model predictions, we estimated the model uncertainty and identified patients at high risk of HE with high confidence. Using the receiver operating characteristics area under the curve (AUC), we compared the deep-learning model prediction performance with multivariable models based on visual markers of HE determined by expert reviewers. We randomly split a multicentric dataset of patients (4-to-1) into training/cross-validation (n = 634) versus test (n = 159) cohorts. We trained and tested separate models for prediction of ≥6 mL and ≥3 mL ICH expansion. The deep-learning models achieved an AUC = 0.81 for high-confidence prediction of HE≥6 mL and AUC = 0.80 for prediction of HE≥3 mL, which were higher than visual maker models AUC = 0.69 for HE≥6 mL (p = 0.036) and AUC = 0.68 for HE≥3 mL (p = 0.043). Our results show that fully automated deep-learning models can identify patients at risk of supratentorial ICH expansion based on admission non-contrast head CT, with high confidence, and more accurately than benchmark visual markers.

Time-Dependent Changes in Hematoma Expansion Rate after Supratentorial Intracerebral Hemorrhage and Its Relationship with Neurological Deterioration and Functional Outcome.

BACKGROUND: Hematoma expansion (HE) following an intracerebral hemorrhage (ICH) is a modifiable risk factor and a treatment target. We examined the association of HE with neurological deterioration (ND), functional outcome, and mortality based on the time gap from onset to baseline CT. METHODS: We included 567 consecutive patients with supratentorial ICH and baseline head CT within 24 h of onset. ND was defined as a ≥4-point increase on the NIH stroke scale (NIHSS) or a ≥2-point drop on the Glasgow coma scale. Poor outcome was defined as a modified Rankin score of 4 to 6 at 3-month follow-up. RESULTS: The rate of HE was higher among those scanned within 3 h (124/304, 40.8%) versus 3 to 24 h post-ICH onset (53/263, 20.2%) (p < 0.001). However, HE was an independent predictor of ND (p < 0.001), poor outcome (p = 0.010), and mortality (p = 0.003) among those scanned within 3 h, as well as those scanned 3-24 h post-ICH (p = 0.043, p = 0.037, and p = 0.004, respectively). Also, in a subset of 180/567 (31.7%) patients presenting with mild symptoms (NIHSS ≤ 5), hematoma growth was an independent predictor of ND (p = 0.026), poor outcome (p = 0.037), and mortality (p = 0.027). CONCLUSION: Despite decreasing rates over time after ICH onset, HE remains an independent predictor of ND, functional outcome, and mortality among those presenting >3 h after onset or with mild symptoms.

Young Professionals in Neuroradiology: Opportunities, Challenges, and Future Directions.

The Young Professionals Committee of the American Society of Neuroradiology identifies and serves the interests of young professionals in neuroradiology, defined as those still in training or within 5 years of its completion. Being a young professional is an exciting, dynamic, and demanding stage of one's professional career. As the landscape of neuroradiology practice changes, new opportunities and challenges occur for those in the early stage of their career. It is important to recognize and support the needs of young professionals because an investment in their professional development is an investment in the future of our specialty. In this article, we aimed to address the most notable developments relevant to current and future young professionals in neuroradiology as well as highlight the work done by the Young Professionals Committee of the American Society of Neuroradiology in serving the needs of these young professionals, focusing on early neuroradiology engagement, flexible work arrangements, private practice, social media, artificial intelligence, and international collaborations.

Peri-hematomal edema shape features related to 3-month outcome in acute supratentorial intracerebral hemorrhage.

INTRODUCTION: Perihematomal edema (PHE) represents secondary brain injury and a potential treatment target in intracerebral hemorrhage (ICH). However, studies differ on optimal PHE volume metrics as prognostic factor(s) after spontaneous, non-traumatic ICH. This study examines associations of baseline and 24-h PHE shape features with 3-month outcomes. PATIENTS AND METHODS: We included 796 patients from a multicentric trial dataset and manually segmented ICH and PHE on baseline and follow-up CTs, extracting 14 shape features. We explored the association of baseline, follow-up, difference (baseline/follow-up) and temporal rate (difference/time gap) of PHE shape changes with 3-month modified Rankin Score (mRS) - using Spearman correlation. Then, using multivariable analysis, we determined if PHE shape features independently predict outcome adjusting for patients' age, sex, NIH stroke scale (NIHSS), Glasgow Coma Scale (GCS), and hematoma volume. RESULTS: Baseline PHE maximum diameters across various planes, main axes, volume, surface, and sphericity correlated with 3-month mRS adjusting for multiple comparisons. The 24-h difference and temporal change rates of these features had significant association with outcome - but not the 24-h absolute values. In multivariable regression, baseline PHE shape sphericity (OR = 2.04, CI = 1.71-2.43) and volume (OR = 0.99, CI = 0. 98-1.0), alongside admission NIHSS (OR = 0.86, CI = 0.83-0.88), hematoma volume (OR = 0.99, CI = 0. 99-1.0), and age (OR = 0.96, CI = 0.95-0.97) were independent predictors of favorable outcomes. CONCLUSION: In acute ICH patients, PHE shape sphericity at baseline emerged as an independent prognostic factor, with a less spherical (more irregular) shape associated with worse outcome. The PHE shape features absolute values over the first 24 h provide no added prognostic value to baseline metrics.

Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank.

BACKGROUND AND OBJECTIVES: Poor oral health is a modifiable risk factor that is associated with clinically observed cardiovascular disease. However, the relationship between oral and brain health is not well understood. We tested the hypothesis that poor oral health is associated with worse neuroimaging brain health profiles in middle-aged persons without stroke or dementia. METHODS: We performed a 2-stage cross-sectional neuroimaging study using UK Biobank data. First, we tested for association between self-reported poor oral health and MRI neuroimaging markers of brain health. Second, we used Mendelian randomization (MR) analyses to test for association between genetically determined poor oral health and the same neuroimaging markers. Poor oral health was defined as the presence of dentures or loose teeth. As instruments for the MR analysis, we used 116 independent DNA sequence variants linked to increased composite risk of dentures or teeth that are decayed, missing, or filled. Neuroimaging markers of brain health included white matter hyperintensity (WMH) volume and aggregate measures of fractional anisotropy (FA) and mean diffusivity (MD), 2 metrics indicative of white matter tract disintegrity obtained through diffusion tensor imaging across 48 brain regions. RESULTS: We included 40,175 persons (mean age 55 years, female sex 53%) enrolled from 2006 to 2010, who underwent a dedicated research brain MRI between 2014 and 2016. Among participants, 5,470 (14%) had poor oral health. Poor oral health was associated with a 9% increase in WMH volume (ß = 0.09, SD = 0.014, p < 0.001), 10% change in aggregate FA score (ß = 0.10, SD = 0.013, p < 0.001), and 5% change in aggregate MD score (ß = 0.05, SD = 0.013, p < 0.001). Genetically determined poor oral health was associated with a 30% increase in WMH volume (ß = 0.30, SD = 0.06, p < 0.001), 43% change in aggregate FA score (ß = 0.43, SD = 0.06, p < 0.001), and 10% change in aggregate MD score (ß = 0.10, SD = 0.03, p < 0.01). DISCUSSION: Among middle age Britons without stroke or dementia, poor oral health was associated with worse neuroimaging brain health profiles. Genetic analyses confirmed these associations, supporting a potentially causal association. Because the neuroimaging markers evaluated in this study precede and are established risk factors of stroke and dementia, our results suggest that oral health, an easily modifiable process, may be a promising target for very early interventions focused on improving brain health.

Impaired mobility and MRI markers of vascular brain injury: Atherosclerosis Risk in Communities and UK Biobank studies.

Background: Vascular brain injury (VBI) may be an under-recognised contributor to mobility impairment. We examined associations between MRI VBI biomarkers and impaired mobility. Methods: We separately analysed Atherosclerosis Risk in Communities (ARIC) and UK Biobank (UKB) study cohorts. Inclusion criteria were no prevalent clinical stroke, and available brain MRI and balance and gait data. MRI VBI biomarkers were (ARIC: ventricular and white matter hyperintensity (WMH) volumes, non-lacunar and lacunar infarctions, microhaemorrhage; UKB: ventricular, brain and WMH volumes, fractional anisotropy (FA), mean diffusivity (MD), intracellular and isotropic free water volume fractions). Quantitative biomarkers were categorised into tertiles. Mobility impairment outcomes were imbalance and slow walk in ARIC and recent fall and slow walk in UKB. Adjusted multivariable logistic regression analyses were performed. Results: We included 1626 ARIC (mean age 76.2 years; 23.4% imbalance, 25.0% slow walk) and 40 098 UKB (mean age 55 years; 15.8% falls, 2.8% slow walk) participants. In ARIC, imbalance associated with four of five VBI measures (all p values<0.05), most strongly with WMH (adjusted OR, aOR 1.64; 95% CI 1.18 to 2.29). Slow walk associated with four of five VBI measures, most strongly with WMH (aOR 2.32; 95% CI 1.66 to 3.24). In UKB, falls associated with all VBI measures except WMH, most strongly with FA (aOR 1.16; 95% CI 1.08 to 1.24). Slow walking associated with WMH, FA and MD, most strongly with FA (aOR 1.57; 95% CI 1.32 to 1.87). Conclusions: VBI is associated with mobility impairment in community-dwelling, clinically stroke-free cohorts. Consequences of VBI may extend beyond clinically apparent stroke to include mobility.

Suboptimal Sleep Duration Is Associated With Poorer Neuroimaging Brain Health Profiles in Middle-Aged Individuals Without Stroke or Dementia.

BACKGROUND: The American Heart Association's Life's Simple 7, a public health construct capturing key determinants of cardiovascular health, became the Life's Essential 8 after the addition of sleep duration. The authors tested the hypothesis that suboptimal sleep duration is associated with poorer neuroimaging brain health profiles in asymptomatic middle-aged adults. METHODS AND RESULTS: The authors conducted a prospective magnetic resonance neuroimaging study in middle-aged individuals without stroke or dementia enrolled in the UK Biobank. Self-reported sleep duration was categorized as short (<7 hours), optimal (7-<9 hours), or long (≥9 hours). Evaluated neuroimaging markers included the presence of white matter hyperintensities (WMHs), volume of WMH, and fractional anisotropy, with the latter evaluated as the average of 48 white matter tracts. Multivariable logistic and linear regression models were used to test for an association between sleep duration and these neuroimaging markers. The authors evaluated 39 771 middle-aged individuals. Of these, 28 912 (72.7%) had optimal, 8468 (21.3%) had short, and 2391 (6%) had long sleep duration. Compared with optimal sleep, short sleep was associated with higher risk of WMH presence (odds ratio, 1.11 [95% CI, 1.05-1.18]; P<0.001), larger WMH volume (beta=0.06 [95% CI, 0.04-0.08]; P<0.001), and worse fractional anisotropy profiles (beta=-0.04 [95% CI, -0.06 to -0.02]; P=0.001). Compared with optimal sleep, long sleep duration was associated with larger WMH volume (beta=0.04 [95% CI, 0.01-0.08]; P=0.02) and worse fractional anisotropy profiles (beta=-0.06 [95% CI, -0.1 to -0.02]; P=0.002), but not with WMH presence (P=0.6). CONCLUSIONS: Among middle-aged adults without stroke or dementia, suboptimal sleep duration is associated with poorer neuroimaging brain health profiles. Because these neuroimaging markers precede stroke and dementia by several years, these findings are consistent with other findings evaluating early interventions to improve this modifiable risk factor.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes.

Introduction: Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. Methods: We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. Results: In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. Discussion: We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Early brain microstructural development among preterm infants requiring caesarean section versus those delivered vaginally.

It is known that the rate of caesarean section (C-section) has been increasing among preterm births. However, the relationship between C-section and long-term neurological outcomes is unclear. In this study, we utilized diffusion tensor imaging (DTI) to characterize the association of delivery method with brain white matter (WM) microstructural integrity in preterm infants. We retrospectively analyzed the DTI scans and health records of preterm infants without neuroimaging abnormality on pre-discharge term-equivalent MRI. We applied both voxel-wise and tract-based analyses to evaluate the association between delivery method and DTI metrics across WM tracts while controlling for numerous covariates. We included 68 preterm infants in this study (23 delivered vaginally, 45 delivered via C-section). Voxel-wise and tract-based analyses revealed significantly lower fractional anisotropy values and significantly higher diffusivity values across major WM tracts in preterm infants delivered via C-section when compared to those delivered vaginally. These results may be partially, but not entirely, mediated by lower birth weight among infants delivered by C-section. Nevertheless, these infants may be at risk for delayed neurodevelopment and could benefit from close neurological follow up for early intervention and mitigation of adverse long-term outcomes.