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INTRODUCTIONIt remains unclear to which extent vascular burden promotes neurodegeneration and cognitive dysfunction in a cohort spanning low-to-severe small vessel disease (SVD) and amyloid-beta pathology.METHODSIn 120 subjects, we investigated 1) whether vascular burden, quantified as total or lobar white matter hyperintensity (WMH) volumes, is associated with different cognitive domains; and 2) whether the total WMH effect on cognition is mediated by amyloid (18F-AV45-PET), glucose metabolism (18F-FDG-PET), and/or cortical atrophy.RESULTSIncreased total WMH volume was associated with poorer performance in all cognitive domains tested, with the strongest effects observed for semantic fluency. These relationships were mediated mainly through cortical atrophy, particularly in the temporal lobe, and to a lesser extent through amyloid and metabolism. WMH volumes differentially impacted cognition depending on lobar location and amyloid status.DISCUSSIONOur study suggests mainly an amyloid-independent pathway in which vascular burden affects cognitive impairment through temporal lobe atrophy.

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

Background Cerebral small vessel disease is associated with higher ratios of soluble‐epoxide hydrolase derived linoleic acid diols (12,13‐dihydroxyoctadecenoic acid [DiHOME] and 9,10‐DiHOME) to their parent epoxides (12(13)‐epoxyoctadecenoic acid [EpOME] and 9(10)‐EpOME); however, the relationship has not yet been examined in stroke. Methods and Results Participants with mild to moderate small vessel stroke or large vessel stroke were selected based on clinical and imaging criteria. Metabolites were quantified by ultra‐high‐performance liquid chromatography–mass spectrometry. Volumes of stroke, lacunes, white matter hyperintensities, magnetic resonance imaging visible perivascular spaces, and free water diffusion were quantified from structural and diffusion magnetic resonance imaging (3 Tesla). Adjusted linear regression models were used for analysis. Compared with participants with large vessel stroke (n=30), participants with small vessel stroke (n=50) had a higher 12,13‐DiHOME/12(13)‐EpOME ratio (β=0.251, P =0.023). The 12,13‐DiHOME/12(13)‐EpOME ratio was associated with more lacunes (β=0.266, P =0.028) but not with large vessel stroke volumes. Ratios of 12,13‐DiHOME/12(13)‐EpOME and 9,10‐DiHOME/9(10)‐EpOME were associated with greater volumes of white matter hyperintensities (β=0.364, P P P =0.011; β=0.314, P =0.006). In small vessel stroke, the 12,13‐DiHOME/12(13)‐EpOME ratio was associated with higher white matter free water diffusion (β=0.439, P =0.016), which was specific to the temporal lobe in exploratory regional analyses. The 9,10‐DiHOME/9(10)‐EpOME ratio was associated with temporal lobe atrophy (β=−0.277, P =0.031). Conclusions Linoleic acid markers of cytochrome P450/soluble‐epoxide hydrolase activity were associated with small versus large vessel stroke, with small vessel disease markers consistent with blood brain barrier and neurovascular‐glial disruption, and temporal lobe atrophy. The findings may indicate a novel modifiable risk factor for small vessel disease and related neurodegeneration.

Importance: Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Identifying neuroimaging-based biomarkers might aid in defining the disease-related dimensions that characterize MDD and predict treatment response. Objective: To investigate the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressant or placebo. Design: Big data consortium (COORDINATE-MDD) sharing raw MRI data in first episode and recurrent MDD, deep clinical phenotyping, and state-of-the art machine learning analysis, involving harmonization of multi-center MRI data and the application of semi-supervised machine learning clustering, HYDRA, to regional brain volumes. Setting: International, multi-center, community-based MDD and healthy controls. Participants: International sample (N=1384), consisting of medication-free, first episode and recurrent MDD individuals (N=685) in a current depressive episode of moderate to severe intensity, that is not treatment resistant depression, and healthy controls (N=699). Prospective longitudinal treatment response data were available in a subset of MDD individuals (N=359 MDD). Treatments were SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Treatment duration was 6-8 weeks, and symptom severity was measured by clinician-rated scales. Main outcomes: First episode and recurrent MDD is optimally characterized by two neuroanatomical dimensions, which show distinct treatment effects to placebo and SSRI antidepressant medications. Results: Dimension 1 is characterized by preserved gray and white matter (N=290 MDD), whereas Dimension 2 is characterized by widespread subtle reductions in gray and white matter (N=395 MDD) relative to healthy controls. There are no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, but there is a significant dimension by treatment response interaction effect. Dimension 1 shows a significant decrease in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%), whereas Dimension 2 shows a comparable improvement to either SSRI (46.9%) or placebo (42.2%) (β=-18.3, 95% CI (-0.34 to -0.2), p=0.03). Conclusions and Relevance: Neuroimaging-based markers may aid in characterizing the MDD dimensions that predict treatment response. In an iterative process, we can characterize the disease-based dimensions that comprise MDD.

Large-scale data-centric challenges faced by neuroscientists, such as improving reproducibility and data reuse, could be overcome by adopting open science practises. The Canadian Open Neuroscience Platform (CONP) takes a multi-faceted approach to enabling open neuroscience, aiming to make research, data, and tools accessible to everyone, with the ultimate objective of accelerating discovery. Central to the tailor-made CONP infrastructure is its Portal, where datasets and analysis tools can be shared in accordance with FAIR principles. Another key piece of CONP infrastructure is NeuroLibre, a preprint server for interactive, fully reproducible scientific notebooks that embed text, figures, and code. To encourage responsible sharing, the CONP has constructed governance frameworks and toolkits that strike a balance between safeguarding the rights of data subjects and promoting widespread public benefit from scientific advancement. The CONP is also focussed on supporting the next generation of neuroscientists through its scholar and training program. The collective experience of our engaged community and leaders has generated a platform that supports multiple facets of open neuroscience, a unique approach within the neuroscience landscape. Together, the various elements of the platform serve the CONP’s vision for promoting open neuroscience and yielding the associated benefits for individual researchers and the wider community.

White matter hyperintensities (WMHs) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D convolutional neural network with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control, and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. Four hundred thirty-two subjects were recruited to train the CNNs from four multisite imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multisite study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on "clinical adversarial cases" simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io.

Objectives Recent studies suggest that interindividual genetic differences in glial-dependent CSF flow through the brain parenchyma, known as glymphatic flow, may trigger compensatory changes in human sleep physiology. In animal models, brain perivascular spaces are a critical conduit for glymphatic flow. We tested the hypothesis that MRI-visible PVS volumes, a putative marker of perivascular dysfunction, are associated with compensatory differences in real-world human sleep behavior. Methods We analyzed data from 152 cerebrovascular disease patients from the Ontario Neurodegenerative Disease Research Initiative (ONDRI). PVS volumes were measured using 3T-MRI. Self-reported total sleep time, time in bed, and daytime dysfunction were extracted from the Pittsburgh Sleep Quality Index. Results Individuals with greater PVS volumes reported longer time in bed (+0.85 h per log10 proportion of intracranial volume (ICV) occupied by PVS, SE = 0.30, p = 0.006) and longer total sleep times (+0.70 h per log10 proportion of ICV occupied by PVS volume, SE = 0.33, p = 0.04), independent of vascular risk factors, sleep apnea, nocturnal sleep disturbance, depression, and global cognitive status. Further analyses suggested that the positive association between PVS volumes and total sleep time was mediated by greater time in bed. Moreover, despite having on average greater total sleep times, individuals with greater basal ganglia PVS volumes were more likely to report daytime dysfunction (OR 5.63 per log10 proportion of ICV occupied by PVS, 95% CI: 1.38–22.26, p = 0.018). Conclusions Individuals with greater PVS volumes spend more time in bed, resulting in greater total sleep time, which may represent a behavioral compensatory response to perivascular space dysfunction.

Correlation Partial-Least Squares (PLSC) provides a robust model for analyzing functional neuroimaging data, which is used to identify functional brain networks that show the largest covariance with task stimuli. However, neuroimaging data tend to be high-dimensional (i.e., there are far more variables P than samples N), with significant noise confounds and variability in brain response. It is therefore challenging to identify the significant, stable components of PLSC analysis. Empirical significance estimators are widely used, as they make minimal assumptions about data structure. The most common estimator in neuroimaging PLS is Bootstrapped Variance (BV), which tests whether bootstrap-stabilized mean component eigenvalues (i.e., covariance) are significantly different from a permuted null distribution; however, recent studies have highlighted issues with this model. Two alternatives were proposed that instead focus on reliability of the PLSC saliences (i.e., singular vectors): a Split-half Stability (SS) model that measures the consistency of reconstructed components for split-half data, and Split-half Reproducibility (SR) which measures the reliability across independent split-half analyses. We compare BV, SS, and SR estimators on functional Magnetic Resonance Imaging (f MRI) data, for both simulated and experimental datasets. The SS and SR methods have comparable sensitivity in detecting “brain” components for most simulated and experimental conditions. However, SR shows consistently greater sensitivity for “task” components. We demonstrate that this is due to relative bias in the SS model: both “brain” and “task” components have biased null distributions, but for the low-dimensional “task” vectors, this bias becomes sufficiently high that it is often impossible to distinguish a significant effect from the null distribution.

We explore functional connectivity in nine subjects measured with 1.5T fMRI-BOLD in a longitudinal study of recovery from unilateral stroke affecting the motor area (Small et al., 2002). We found that several measures of complexity of covariance matrices show strong correlations with behavioral measures of recovery. In Schmah et al. (2010), we applied Linear and Quadratic Discriminants (LD and QD) computed on a principal components (PC) subspace to classify the fMRI volumes into "early" and "late" sessions. We demonstrated excellent classification accuracy with QD but not LD, indicating that potentially important differences in functional connectivity exist between the early and late sessions. Motivated by Mclntosh et al. (2008), who showed that EEG brain-signal variability and behavioral performance both increased with age during development, we investigated complexity of the covariance matrix for this longitudinal stroke recovery data set. We used three complexity measures: the sphericity index described by Abdi (2010); "unsupervised dimensionality", which is the number of PCs that minimizes unsupervised generalization error of a covariance matrix (Hansen et al., 1999); and "QD dimensionality", which is the number of PCs that minimizes the classification accuracy of QD. Although these approaches measure different kinds of complexity, all showed strong correlations with one or more behavioral tests: nine-hole peg test, hand grip test and pinch test. We could not demonstrate that either sphericity or unsupervised dimensionality were significantly different for the "early" and "late" sessions using a paired Wilcoxon test. However, the amount of relative behavioral improvement was correlated with sphericity of the overall covariance matrix (pooled across all sessions), as well as with the divergence of the eigenspectra between the "early" and "late" covariance matrices. Complexity measures that use the number of PCs (which optimize QD classification or unsupervised generalization) were correlated with the behavioral performance of the final session, but not with the relative improvement. These are suggestive, but limited, results given the sample size, restricted behavioral measurements and older 1.5T BOLD data sets. Nevertheless, they indicate one potentially fruitful direction for future data-driven fMRI studies of stroke recovery in larger, better-characterized longitudinal stroke data sets recorded at higher field strength. Finally, we produced sensitivity maps (Kjems et al., 2002) corresponding to both linear and quadratic discriminants for the "early" vs. "late" classification. These maps measure the influence of each voxel on the class assignments for a given classifier. Differences between the scaled sensitivity maps for the linear and quadratic discriminants indicate brain regions involved in changes in functional connectivity. These regions are highly variable across subjects, but include the cerebellum and the motor area contralateral to the lesion.