Gadolinium Enhanced T2 FLAIR is an Imaging Biomarker of Radiation Necrosis and Tumor Progression in Patients with Brain Metastases.

BACKGROUND AND PURPOSE: Differentiating radiation necrosis (RN) from tumor progression (TP) after radiotherapy for brain metastases is an important clinical problem requiring advanced imaging techniques which may not be widely available and are challenging to perform at multiple time points. The ability to leverage conventional MRI for this problem, could have meaningful clinical impact. The purpose of this study was to explore contrast enhanced T2 FLAIR (T2FLAIRc) as a new imaging biomarker of RN and TP. MATERIALS AND METHODS: This single-institution retrospective study included patients with treated brain metastases undergoing DSC-MRI between January 2021 and June 2023. Reference standard assessment was based on histopathology or serial follow-up including results of DSC-MRI for a minimum of 6 months from the first DSC-MRI. The index test was implemented as part of the institutional brain tumor MRI protocol and preceded the first DSC-MRI. T2FLAIRc and gadolinium enhanced T1 MPRAGE (T1c) signal were normalized against normal brain parenchyma and expressed as z-score. Mean signal intensity of enhancing disease for RN and TP groups were compared using unpaired t-test. Receiver operator characteristic (ROC) curves and area under the ROC curve (AUC) were derived by bootstrapping. DeLong test was used to compare AUC. RESULTS: 56 participants (mean age, 62 years +/-12.7 [SD]; 39 females); 28 RN, 28 TP were evaluated. The index MRI was performed on average 73 days +/-34 [SD] before the first DSC-MRI. Significantly higher z-scores were found for RN using T2FLAIRc (8.3 versus 5.8, p<0.001) and T1c (4.1 versus 3.5, p=0.02). AUC for T2FLAIRc (0.83, 95% CI, 0.72-0.92) was greater than T1c (0.70, 95% CI, 0.560.83) (p = 0.04). The AUC of DSC derived rCBV (0.82, 95% CI, 0.70-0.93) was not significantly different from T2FLAIRc (p = 0.9). CONCLUSIONS: A higher normalized T1c and T2FLAIRc signal intensity was found for RN. In a univariable test, mean T2FLAIRc signal intensity of enhancing voxels showed good discrimination performance for distinguishing RN from TP. The results of this work demonstrate the potential of T2FLAIRc as an imaging biomarker in the work-up of RN in patients with brain metastases. ABBREVIATIONS: AUC = area under the receiver operating characteristic curve; RN = radiation necrosis; ROC = receiver operating characteristic; SRS = stereotactic radiosurgery; T1c = contrast enhanced T1; T2FLAIRc = contrast enhanced T2 FLAIR; TP = tumor progression.

Effects of post-acute COVID-19 syndrome on cerebral white matter and emotional health among non-hospitalized individuals.

Introduction: Post-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health. Methods: Data were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4-5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect. Results: Despite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter. Discussion: These results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder.

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD) from healthy controls (HC) based on neuroimaging data, such as T1-weighted Magnetic Resonance Imaging (MRI). The goal of the current study is to investigate whether modern, flexible architectures such as EfficientNet provide any performance boost over more standard architectures. METHODS: MRI data was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and processed with a minimal preprocessing pipeline. Among the various architectures tested, the minimal 3D convolutional neural network SFCN stood out, composed solely of 3x3x3 convolution, batch normalization, ReLU, and max-pooling. We also examined the influence of scale on performance, testing SFCN versions with trainable parameters ranging from 720 up to 2.9 million. RESULTS: SFCN achieves a test ROC AUC of 96.0% while EfficientNet got an ROC AUC of 94.9 %. SFCN retained high performance down to 720 trainable parameters, achieving an ROC AUC of 91.4%. COMPARISON WITH EXISTING METHODS: The SFCN is compared to DenseNet and EfficientNet as well as the results of other publications in the field. CONCLUSIONS: The results indicate that using the minimal 3D convolutional neural network SFCN with a minimal preprocessing pipeline can achieve competitive performance in AD classification, challenging the necessity of employing more complex architectures with a larger number of parameters. This finding supports the efficiency of simpler deep learning models for neuroimaging-based AD diagnosis, potentially aiding in better understanding and diagnosing Alzheimer's disease.

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.

Autism spectrum disorder (ASD) is diagnosed using comprehensive behavioral information. Neuroimaging offers additional information but lacks clinical utility for diagnosis. This study investigates whether multi-forms of magnetic resonance imaging (MRI) contrast can be used individually and in combination to produce a categorical classification of young individuals with ASD. MRI data were accessed from the Autism Brain Imaging Data Exchange (ABIDE). Young participants (ages 2-30) were selected, and two group cohorts consisted of 702 participants: 351 ASD and 351 controls. Image-based classification was performed using one-channel and two-channel inputs to 3D-DenseNet deep learning networks. The models were trained and tested using tenfold cross-validation. Two-channel models were twinned with combinations of structural MRI (sMRI) maps and amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) maps from resting-state functional MRI (rs-fMRI). All models produced classification accuracy that exceeded 65.1%. The two-channel ALFF-sMRI model achieved the highest mean accuracy of 76.9% ± 2.34. The one-channel ALFF-based model alone had mean accuracy of 72% ± 3.1. This study leveraged the ABIDE dataset to produce ASD classification results that are comparable and/or exceed literature values. The deep learning approach was conducive to diverse neuroimaging inputs. Findings reveal that the ALFF-sMRI two-channel model outperformed all others.

Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers.

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have associated motor outcomes with the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to model chronic motor outcomes after stroke and compares the accuracy of these associations to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients from the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) Stroke Recovery Working Group. Using the explained variance metric to measure the strength of the association between chronic motor outcomes and imaging biomarkers, we compared theory-based biomarkers, like lesion load to known motor tracts, to three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had stronger associations with chronic motor outcomes accuracy than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R 2 = 0.210, P < 0.001), performing significantly better than the theory-based biomarkers of lesion load of the corticospinal tract (R 2 = 0.132, P < 0.001) and of multiple descending motor tracts (R 2 = 0.180, P < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R 2 = 0.200, P < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R 2 = 0.167, P < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved the strength of associations for theory-based and data-driven biomarkers. Combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R 2 = 0.241, P < 0.001. Overall, these results demonstrate that out-of-sample associations between chronic motor outcomes and data-driven imaging features, particularly when lesion data is represented in terms of structural disconnection, are stronger than associations between chronic motor outcomes and theory-based biomarkers. However, combining both theory-based and data-driven models provides the most robust associations.

Cerebral blood flow in relation to peripheral endothelial function in youth bipolar disorder.

INTRODUCTION: Anomalous cerebral blood flow (CBF) is evident in bipolar disorder (BD), however the extent to which CBF reflects peripheral vascular function in BD is unknown. This study investigated endothelial function, an index of early atherosclerosis and cardiovascular disease risk, in relation to CBF among youth with BD. METHODS: Participants included 113 youth, 13-20 years old (66 BD; 47 healthy controls [HC]). CBF was measured using arterial spin labeling with 3T MRI. Region of interest analyses (ROI; global grey matter, middle frontal gyrus, anterior cingulate cortex, temporal cortex, caudate) were undertaken alongside voxel-wise analyses. Reactive hyperemia index (RHI), a measure of endothelial function, was assessed non-invasively via pulse amplitude tonometry. General linear models were used to examine RHI and RHI-by-diagnosis associations with CBF, controlling for age, sex, and body mass index. Bonferroni correction for multiple comparisons was used for ROI analyses, such that the significance level was divided by the number of ROIs (α = 0.05/5 = 0.01). Cluster-extent thresholding was used to correct for multiple comparisons for voxel-wise analyses. RESULTS: ROI findings were not significant after correction. Voxel-wise analyses found that higher RHI was associated with lower left thalamus CBF in the whole group (p < 0.001). Additionally, significant RHI-by-diagnosis associations with CBF were found in three clusters: left intracalcarine cortex (p < 0.001), left thalamus (p < 0.001), and right frontal pole (p = 0.006). Post-hoc analyses showed that in each cluster, higher RHI was associated with lower CBF in BD, but higher CBF in HC. CONCLUSION: We found that RHI was differentially associated with CBF in youth with BD versus HC. The unanticipated association of higher RHI with lower CBF in BD could potentially reflect a compensatory mechanism. Future research, including prospective studies and experimental designs are warranted to build on the current findings.

Cytochrome P450-soluble epoxide hydrolase oxylipins, depression and cognition in type 2 diabetes.

AIMS: This study examined serum cytochrome P450-soluble epoxide hydrolase (CYP450-sEH) oxylipins and depressive symptoms together in relation to cognitive performance in individuals with type 2 diabetes mellitus (T2DM). METHODS: Clinically cognitively normal T2DM individuals were recruited (NCT04455867). Depressive symptom severity was assessed using the Beck Depression Inventory-II (BDI-II; total scores ≤13 indicated minimal depressive symptoms and ≥ 14 indicated significant depressive symptoms). Executive function and verbal memory were assessed. Fasting serum oxylipins were quantified by ultra-high-performance liquid chromatography tandem mass-spectrometry. RESULTS: The study included 85 participants with minimal depressive symptoms and 27 with significant symptoms (mean age: 63.3 ± 9.8 years, 49 % women). In all participants, higher concentrations of linoleic acid derived sEH (12,13-dihydroxyoctadecamonoenoic acid; DiHOME) and CYP450 (12(13)-epoxyoctadecamonoenoic acid; EpOME) metabolites were associated with poorer executive function (F1,101 = 6.094, p = 0.015 and F1,101 = 5.598, p = 0.020, respectively). Concentrations of multiple sEH substrates interacted with depressive symptoms to predict 1) poorer executive function, including 9(10)-EpOME (F1,100 = 12.137, p < 0.001), 5(6)-epoxyeicosatrienoic acid (5(6)-EpETrE; F1,100 = 6.481, p = 0.012) and 11(12)-EpETrE (F1,100 = 4.409, p = 0.038), and 2) verbal memory, including 9(10)-EpOME (F1,100 = 4.286, p = 0.041), 5(6)-EpETrE (F1,100 = 6.845, p = 0.010), 11(12)-EpETrE (F1,100 = 3.981, p = 0.049) and 14(15)-EpETrE (F1,100 = 5.019, p = 0.027). CONCLUSIONS: Associations of CYP450-sEH metabolites and depressive symptoms with cognition highlight the biomarker and therapeutic potential of the CYP450-sEH pathway in T2DM.

Engaging Mood Brain Circuits with Psilocybin (EMBRACE): a study protocol for a randomized, placebo-controlled and delayed-start, neuroimaging trial in depression.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide across domains of health and cognition, affecting overall quality of life. Approximately one third of individuals with depression do not fully respond to treatments (e.g., conventional antidepressants, psychotherapy) and alternative strategies are needed. Recent early phase trials suggest psilocybin may be a safe and efficacious intervention with rapid-acting antidepressant properties. Psilocybin is thought to exert therapeutic benefits by altering brain network connectivity and inducing neuroplastic changes that endure for weeks post-treatment. Although early clinical results are encouraging, psilocybin's acute neurobiological effects on neuroplasticity have not been fully investigated. We aim to examine for the first time how psilocybin acutely (intraday) and subacutely (weeks) alters functional brain networks implicated in depression. METHODS: Fifty participants diagnosed with MDD or persistent depressive disorder (PDD) will be recruited from a tertiary mood disorders clinic and undergo 1:1 randomization into either an experimental or control arm. Participants will be given either 25 mg psilocybin or 25 mg microcrystalline cellulose (MCC) placebo for the first treatment. Three weeks later, those in the control arm will transition to receiving 25 mg psilocybin. We will investigate whether treatments are associated with changes in arterial spin labelling and blood oxygenation level-dependent contrast neuroimaging assessments at acute and subacute timepoints. Primary outcomes include testing whether psilocybin demonstrates acute changes in (1) cerebral blood flow and (2) functional brain activity in networks associated with mood regulation and depression when compared to placebo, along with changes in MADRS score over time compared to placebo. Secondary outcomes include changes across complementary clinical psychiatric, cognitive, and functional scales from baseline to final follow-up. Serum peripheral neurotrophic and inflammatory biomarkers will be collected at baseline and follow-up to examine relationships with clinical response, and neuroimaging measures. DISCUSSION: This study will investigate the acute and additive subacute neuroplastic effects of psilocybin on brain networks affected by depression using advanced serial neuroimaging methods. Results will improve our understanding of psilocybin's antidepressant mechanisms versus placebo response and whether biological measures of brain function can provide early predictors of treatment response. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT06072898. Registered on 6 October 2023.

Standardized evaluation of the extent of resection in glioblastoma with automated early post-operative segmentation.

Standard treatment of patients with glioblastoma includes surgical resection of the tumor. The extent of resection (EOR) achieved during surgery significantly impacts prognosis and is used to stratify patients in clinical trials. In this study, we developed a U-Net-based deep-learning model to segment contrast-enhancing tumor on post-operative MRI exams taken within 72 h of resection surgery and used these segmentations to classify the EOR as either maximal or submaximal. The model was trained on 122 multiparametric MRI scans from our institution and achieved a mean Dice score of 0.52 ± 0.03 on an external dataset (n = 248), a performance -on par with the interrater agreement between expert annotators as reported in literature. We obtained an EOR classification precision/recall of 0.72/0.78 on the internal test dataset (n = 462) and 0.90/0.87 on the external dataset. Furthermore, Kaplan-Meier curves were used to compare the overall survival between patients with maximal and submaximal resection in the internal test dataset, as determined by either clinicians or the model. There was no significant difference between the survival predictions using the model's and clinical EOR classification. We find that the proposed segmentation model is capable of reliably classifying the EOR of glioblastoma tumors on early post-operative MRI scans. Moreover, we show that stratification of patients based on the model's predictions offers at least the same prognostic value as when done by clinicians.

Pilot study of circulating cell-free mitochondrial DNA in relation to brain structure in youth bipolar disorder.

BACKGROUND: Mitochondrial dysfunction is implicated in the neuropathology of bipolar disorder (BD). Higher circulating cell-free mitochondrial DNA (ccf-mtDNA), generally reflecting poorer mitochondrial health, has been associated with greater symptoms severity in BD. The current study examines the association of serum ccf-mtDNA and brain structure in relation to youth BD. We hypothesized that higher ccf-mtDNA will be associated with measures of lower brain structure, particularly in the BD group. METHODS: Participants included 40 youth (BD, n = 19; Control group [CG], n = 21; aged 13-20 years). Serum ccf-mtDNA levels were assayed. T1-weighted brain images were acquired using 3T-MRI. Region of interest (ROI) analyses examined prefrontal cortex (PFC) and whole brain gray matter, alongside exploratory vertex-wise analyses. Analyses examined ccf-mtDNA main-effects and ccf-mtDNA-by-diagnosis interaction effects controlling for age, sex, and intracranial volume. RESULTS: There was no significant difference in ccf-mtDNA levels between BD and CG. In ROI analyses, higher ccf-mtDNA was associated with higher PFC surface area (SA) (ß = 0.32 p < 0.001) and PFC volume (ß = 0.32 p = 0.002) in the overall sample. In stratified analyses, higher ccf-mtDNA was associated with higher PFC SA within both subgroups (BD: ß = 0.39 p = 0.02; CG: ß = 0.24 p = 0.045). Higher ccf-mtDNA was associated with higher PFC volume within the BD group (ß = 0.39 p = 0.046). In vertex-wise analyses, higher ccf-mtDNA was associated with higher SA and volume in frontal clusters within the overall sample and within the BD group. There were significant ccf-mtDNA-by-diagnosis interactions in three frontal and parietal clusters, whereby higher ccf-mtDNA was associated with higher neurostructural metrics in the BD group but lower neurostructural metrics in CG. CONCLUSIONS: Contrasting our hypothesis, higher ccf-mtDNA was consistently associated with higher, rather than lower, regional neuralstructural metrics among youth with BD. While this finding may reflect a compensatory mechanism, future repeated-measures prospective studies evaluating the inter-relationship among ccf-mtDNA, mood, and brain structure across developmental epochs and illness stages are warranted.

Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation.

Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.

Longitudinal cerebral perfusion in presymptomatic genetic frontotemporal dementia: GENFI results.

INTRODUCTION: Effective longitudinal biomarkers that track disease progression are needed to characterize the presymptomatic phase of genetic frontotemporal dementia (FTD). We investigate the utility of cerebral perfusion as one such biomarker in presymptomatic FTD mutation carriers. METHODS: We investigated longitudinal profiles of cerebral perfusion using arterial spin labeling magnetic resonance imaging in 42 C9orf72, 70 GRN, and 31 MAPT presymptomatic carriers and 158 non-carrier controls. Linear mixed effects models assessed perfusion up to 5 years after baseline assessment. RESULTS: Perfusion decline was evident in all three presymptomatic groups in global gray matter. Each group also featured its own regional pattern of hypoperfusion over time, with the left thalamus common to all groups. Frontal lobe regions featured lower perfusion in those who symptomatically converted versus asymptomatic carriers past their expected age of disease onset. DISCUSSION: Cerebral perfusion is a potential biomarker for assessing genetic FTD and its genetic subgroups prior to symptom onset. HIGHLIGHTS: Gray matter perfusion declines in at-risk genetic frontotemporal dementia (FTD). Regional perfusion decline differs between at-risk genetic FTD subgroups . Hypoperfusion in the left thalamus is common across all presymptomatic groups. Converters exhibit greater right frontal hypoperfusion than non-converters past their expected conversion date. Cerebral hypoperfusion is a potential early biomarker of genetic FTD.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Investigating the impact of hypertension with and without diabetes on Alzheimer's disease risk: A clinico-pathological study.

INTRODUCTION: Hypertension and diabetes are common cardiovascular risk factors that increase Alzheimer's disease (AD) risk. However, it is unclear whether AD risk differs in hypertensive individuals with and without diabetes. METHODS: Cognitively normal individuals (N = 11,074) from the National Alzheimer's Coordinating Center (NACC) were categorized as having (1) hypertension with diabetes (HTN+/DM+), (2) hypertension without diabetes (HTN+/DM-), or (3) neither (HTN-/DM-). AD risk in HTN+/DM+ and HTN+/DM- was compared to HTN-/DM-. This risk was then investigated in those with AD neuropathology (ADNP), cerebral amyloid angiopathy (CAA), cerebrovascular neuropathology (CVNP), arteriolosclerosis, and atherosclerosis. Finally, AD risk in HTN-/DM+ was compared to HTN-/DM-. RESULTS: Seven percent (N = 830) of individuals developed AD. HTN+/DM+ (hazard ratio [HR] = 1.31 [1.19-1.44]) and HTN+/DM- (HR = 1.24 [1.17-1.32]) increased AD risk compared to HTN-/DM-. AD risk was greater in HTN+/DM+ with ADNP (HR = 2.10 [1.16-3.79]) and CAA (HR = 1.52 [1.09-2.12]), and in HTN+/DM- with CVNP (HR = 1.54 [1.17-2.03]). HTN-/DM+ also increased AD risk (HR = 1.88 [1.30-2.72]) compared to HTN-/DM-. DISCUSSION: HTN+/DM+ and HTN+/DM- increased AD risk compared to HTN-/DM-, but pathological differences between groups suggest targeted therapies may be warranted based on cardiovascular risk profiles. HIGHLIGHTS: AD risk was studied in hypertensive (HTN+) individuals with/without diabetes (DM+/-). HTN+/DM+ and HTN+/DM- both had an increased risk of AD compared to HTN-/DM-. Post mortem analysis identified neuropathological differences between HTN+/DM+ and HTN+/DM-. In HTN+/DM+, AD risk was greater in those with AD neuropathology and CAA. In HTN+/DM-, AD risk was greater in those with cerebrovascular neuropathology.

Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study.

INTRODUCTION: Regional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool. METHODS: 15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association between GM volume and age of onset, chlorpromazine (CPZ) equivalent dose, and other clinical variables. RESULTS: Compared with healthy controls, individuals with EOP showed widespread lower GM volume encompassing most of the cortex, with the most marked effect in the left median cingulate (Hedges' g = 0.55, p = 0.001 corrected), as well as small clusters of lower white matter (WM), whereas no regional GM or WM volumes were higher in EOP. Lower GM volume in the cerebellum, thalamus and left inferior parietal gyrus was associated with older age of onset. Deficits in GM in the left inferior frontal gyrus, right insula, right precentral gyrus and right superior frontal gyrus were also associated with higher CPZ equivalent doses. CONCLUSION: EOP is associated with widespread reductions in cortical GM volume, while WM is affected to a smaller extent. GM volume alterations are associated with age of onset and CPZ equivalent dose but these effects are small compared to case-control differences. Mapping anatomical abnormalities in EOP may lead to a better understanding of the role of psychosis in brain development during childhood and adolescence.

An empirical analysis of structural neuroimaging profiles in a staging model of depression.

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

Sex differences in cerebral blood flow among adolescents with bipolar disorder.

BACKGROUND: Abnormalities in cerebral blood flow (CBF) are common in bipolar disorder (BD). Despite known differences in CBF between healthy adolescent males and females, sex differences in CBF among adolescents with BD have never been studied. OBJECTIVE: To examine sex differences in CBF among adolescents with BD versus healthy controls (HC). METHODS: CBF images were acquired using arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) in 123 adolescents (72 BD: 30M, 42F; 51 HC: 22M, 29F) matched for age (13-20 years). Whole brain voxel-wise analysis was performed in a general linear model with sex and diagnosis as fixed factors, sex-diagnosis interaction effect, and age as a covariate. We tested for main effects of sex, diagnosis, and their interaction. Results were thresholded at cluster forming p = 0.0125, with posthoc Bonferroni correction (p = 0.05/4 groups). RESULTS: A main effect of diagnosis (BD > HC) was observed in the superior longitudinal fasciculus (SLF), underlying the left precentral gyrus (F =10.24 (3), p < 0.0001). A main effect of sex (F > M) on CBF was detected in the precuneus/posterior cingulate cortex (PCC), left frontal and occipital poles, left thalamus, left SLF, and right inferior longitudinal fasciculus (ILF). No regions demonstrated a significant sex-by-diagnosis interaction. Exploratory pairwise testing in regions with a main effect of sex revealed greater CBF in females with BD versus HC in the precuneus/PCC (F = 7.1 (3), p < 0.01). CONCLUSION: Greater CBF in female adolescents with BD versus HC in the precuneus/PCC may reflect the role of this region in the neurobiological sex differences of adolescent-onset BD. Larger studies targeting underlying mechanisms, such as mitochondrial dysfunction or oxidative stress, are warranted.

No association between metformin initiation and incident dementia in older adults newly diagnosed with diabetes.

BACKGROUND: Metformin has been suggested to reduce dementia risk; however, most epidemiologic studies have been limited by immortal time bias or confounding due to disease severity. OBJECTIVES: To investigate the association of metformin initiation with incident dementia using strategies that mitigate these important sources of bias. METHODS: Residents of Ontario, Canada ≥66 years newly diagnosed with diabetes from January 1, 2008 to December 31, 2017 entered this retrospective population-based cohort. To consider the indication for metformin monotherapy initiation, people with hemoglobin A1c of 6.5%-8.0% and estimated glomerular filtration rate ≥45 mL/min/1.73 m2 were selected. Using the landmark method to address immortal time bias, exposure was grouped into "metformin monotherapy initiation within 180 days after new diabetes diagnosis" or "no glucose-lowering medications within 180 days." To address disease latency, 1-year lag time was applied to the end of the 180-day landmark period. Incident dementia was defined using a validated algorithm for Alzheimer's disease and related dementias. Adjusted hazard ratios (aHR) and confidence intervals (CIs) were estimated from propensity-score weighted Cox proportional hazard models. RESULTS: Over mean follow-up of 6.77 years from cohort entry, metformin initiation within 180 days after new diabetes diagnosis (N = 12,331; 978 events; 65,762 person-years) showed no association with dementia risk (aHR [95% CI] = 1.05 [0.96-1.15]), compared to delayed or no glucose-lowering medication initiation (N = 22,369; 1768 events; 117,415 person-years). CONCLUSION: Early metformin initiation was not associated with incident dementia in older adults newly diagnosed with diabetes. The utility of metformin to prevent dementia was not supported.

Neuroimaging and Biosample Collection in the Toronto Adolescent and Youth Cohort Study: Rationale, Methods, and Early Data.

BACKGROUND: The Toronto Adolescent and Youth (TAY) Cohort Study will characterize the neurobiological trajectories of psychosis spectrum symptoms, functioning, and suicidality (i.e., suicidal thoughts and behaviors) in youth seeking mental health care. Here, we present the neuroimaging and biosample component of the protocol. We also present feasibility and quality control metrics for the baseline sample collected thus far. METHODS: The current study includes youths (ages 11-24 years) who were referred to child and youth mental health services within a large tertiary care center in Toronto, Ontario, Canada, with target recruitment of 1500 participants. Participants were offered the opportunity to provide any or all of the following: 1) 1-hour magnetic resonance imaging (MRI) scan (electroencephalography if ineligible for or declined MRI), 2) blood sample for genomic and proteomic data (or saliva if blood collection was declined or not feasible) and urine sample, and 3) heart rate recording to assess respiratory sinus arrhythmia. RESULTS: Of the first 417 participants who consented to participate between May 4, 2021, and February 2, 2023, 412 agreed to participate in the imaging and biosample protocol. Of these, 334 completed imaging, 341 provided a biosample, 338 completed respiratory sinus arrhythmia, and 316 completed all 3. Following quality control, data usability was high (MRI: T1-weighted 99%, diffusion-weighted imaging 99%, arterial spin labeling 90%, resting-state functional MRI 95%, task functional MRI 90%; electroencephalography: 83%; respiratory sinus arrhythmia: 99%). CONCLUSIONS: The high consent rates, good completion rates, and high data usability reported here demonstrate the feasibility of collecting and using brain imaging and biosamples in a large clinical cohort of youths seeking mental health care.