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Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr

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Introduction: Early focal brain injuries lead to long-term disabilities with frequent cognitive impairments, suggesting global dysfunction beyond the lesion. While plasticity of the immature brain promotes better learning, outcome variability across individuals is multifactorial. Males are more vulnerable to early injuries and neurodevelopmental disorders than females, but long-term sex differences in brain growth after an early focal lesion have not been described yet. With this MRI longitudinal morphometry study of brain development after a Neonatal Arterial Ischemic Stroke (NAIS), we searched for differences between males and females in the trajectories of ipsi- and contralesional gray matter growth in childhood and adolescence, while accounting for lesion characteristics. Methods: We relied on a longitudinal cohort (AVCnn) of patients with unilateral NAIS who underwent clinical and MRI assessments at ages 7 and 16 were compared to age-matched controls. Non-lesioned volumes of gray matter (hemispheres, lobes, regions, deep structures, cerebellum) were extracted from segmented T1 MRI images at 7 (Patients: 23 M, 16 F; Controls: 17 M, 18 F) and 16 (Patients: 18 M, 11 F; Controls: 16 M, 15 F). These volumes were analyzed using a Linear Mixed Model accounting for age, sex, and lesion characteristics. Results: Whole hemisphere volumes were reduced at both ages in patients compared to controls (gray matter volume: -16% in males, -10% in females). In ipsilesional hemisphere, cortical gray matter and thalamic volume losses (average -13%) mostly depended on lesion severity, suggesting diaschisis, with minimal effect of patient sex. In the contralesional hemisphere however, we consistently found sex differences in gray matter volumes, as only male volumes were smaller than inmale controls (average-7.5%), mostly in territoriesmirroring the contralateral lesion. Females did not significantly deviate from the typical trajectories of female controls. Similar sex differences were found in both cerebellar hemispheres. Discussion: These results suggest sex-dependent growth trajectories after an early brain lesion with a contralesional growth deficit in males only. The similarity of patterns at ages 7 and 16 suggests that puberty has little effect on these trajectories, and that most of the deviation in males occurs in early childhood, in line with the well-described perinatal vulnerability of themale brain, and with no compensation thereafter. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a developmental disorder under-diagnosed in adults. To date, no consistent evidence of alterations in brain structure has been reported in adults with ASD and few studies were conducted at that age. We analyzed structural magnetic resonance imaging data from 167 high functioning adults with ASD and 195 controls. We ran our analyses on a discovery (n = 301) and a replication sample (n = 61). The right caudal anterior cingulate cortical thickness was significantly thinner in adults with ASD compared to controls in both the discovery and the replication sample. Our work underlines the relevance of studying the brain anatomy of an adult ASD population.

The cerebellum has been involved in social abilities and autism. Given that the cerebellum is connected to the cortex via the cerebello‐thalamo‐cortical loop, the connectivity between the cerebellum and cortical regions involved in social interactions, that is, the right temporo‐parietal junction (rTPJ) has been studied in individuals with autism, who suffer from prototypical deficits in social abilities. However, existing studies with small samples of categorical, case–control comparisons have yielded inconsistent results due to the inherent heterogeneity of autism, suggesting that investigating how clinical dimensions are related to cerebellar–rTPJ functional connectivity might be more relevant. Therefore, our objective was to study the functional connectivity between the cerebellum and rTPJ, focusing on its association with social abilities from a dimensional perspective in a transdiagnostic sample. We analyzed structural magnetic resonance imaging (MRI) and functional MRI (fMRI) scans obtained during naturalistic films watching from a large transdiagnostic dataset, the Healthy Brain Network (HBN), and examined the association between cerebellum–rTPJ functional connectivity and social abilities measured with the social responsiveness scale (SRS). We conducted univariate seed‐to‐voxel analysis, multivariate canonical correlation analysis (CCA), and predictive support vector regression (SVR). We included 1404 subjects in the structural analysis (age: 10.516 ± 3.034, range: 5.822–21.820, 506 females) and 414 subjects in the functional analysis (age: 11.260 ± 3.318 years, range: 6.020–21.820, 161 females). Our CCA model revealed a significant association between cerebellum–rTPJ functional connectivity, full‐scale IQ (FSIQ) and SRS scores. However, this effect was primarily driven by FSIQ as suggested by SVR and univariate seed‐to‐voxel analysis. We also demonstrated the specificity of the rTPJ and the influence of structural anatomy in this association. Our results suggest that there is a complex relationship between cerebellum–rTPJ connectivity, social performance and IQ. This relationship is specific to the cerebellum–rTPJ connectivity, and is largely related to structural anatomy in these two regions. Practitioner Points: We analyzed cerebellum–right temporoparietal junction (rTPJ) connectivity in a pediatric transdiagnostic sample.We found a complex relationship between cerebellum and rTPJ connectivity, social performance and IQ.Cerebellum and rTPJ functional connectivity is related to structural anatomy in these two regions. [ABSTRACT FROM AUTHOR]

Background and Hypothesis The emergence of psychosis in ultra-high-risk subjects (UHR) is influenced by gene-environment interactions that rely on epigenetic mechanisms such as microRNAs. However, whether they can be relevant pathophysiological biomarkers of psychosis' onset remains unknown. Study Design We present a longitudinal study of microRNA expression, measured in plasma by high-throughput sequencing at baseline and follow-up, in a prospective cohort of 81 UHR, 35 of whom developed psychosis at follow-up (converters). We combined supervised machine learning and differential graph analysis to assess the relative weighted contribution of each microRNA variation to the difference in outcome and identify outcome-specific networks. We then applied univariate models to the resulting microRNA variations common to both strategies, to interpret them as a function of demographic and clinical covariates. Study Results We identified 207 microRNA variations that significantly contributed to the classification. The differential network analysis found 276 network-specific correlations of microRNA variations. The combination of both strategies identified 25 microRNAs, whose gene targets were overrepresented in cognition and schizophrenia genome-wide association studies findings. Interpretable univariate models further supported the relevance of miR-150-5p and miR-3191-5p variations in psychosis onset, independent of age, sex, cannabis use, and medication. Conclusions In this first longitudinal study of microRNA variation during conversion to psychosis, we combined 2 methodologically independent data-driven strategies to identify a dynamic epigenetic signature of the emergence of psychosis that is pathophysiologically relevant. [ABSTRACT FROM AUTHOR]

Aim: Neuroimaging‐based machine‐learning predictions of psychosis onset rely on the hypothesis that structural brain anomalies may reflect the underlying pathophysiology. Yet, current predictors remain difficult to interpret in light of brain structure. Here, we combined an advanced interpretable supervised algorithm and a model of neuroanatomical age to identify the level of brain maturation of the regions most predictive of psychosis. Methods: We used the voxel‐based morphometry of a healthy control dataset (N = 2024) and a prospective longitudinal UHR cohort (N = 82), of which 27 developed psychosis after one year. In UHR, psychosis was predicted at one year using Elastic‐Net‐Total‐Variation (Enet‐TV) penalties within a five‐fold cross‐validation, providing an interpretable map of distinct predictive regions. Using both the whole brain and each predictive region separately, a brain age predictor was then built and validated in 1605 controls, externally tested in 419 controls from an independent cohort, and applied in UHR. Brain age gaps were computed as the difference between chronological and predicted age, providing a proxy of whole‐brain and regional brain maturation. Results: Psychosis prediction was performant with 80 ± 4% of area‐under‐curve and 69 ± 5% of balanced accuracy (P < 0.001), and mainly leveraged volumetric increases in the ventromedial prefrontal cortex and decreases in the left precentral gyrus and the right orbitofrontal cortex. These regions were predicted to have delayed and accelerated maturational patterns, respectively. Conclusion: By combining an interpretable supervised model of conversion to psychosis with a brain age predictor, we showed that inter‐regional asynchronous brain maturation underlines the predictive signature of psychosis. [ABSTRACT FROM AUTHOR]

In fetal alcohol spectrum disorders (FASD), brain growth deficiency is a hallmark of subjects both with fetal alcohol syndrome (FAS) and with non‐syndromic FASD (NS‐FASD, i.e., those without specific diagnostic features). However, although the cerebellum was suggested to be more severely undersized than the rest of the brain, it has not yet been given a specific place in the FASD diagnostic criteria where neuroanatomical features still count for little if anything in diagnostic specificity. We applied a combination of cerebellar segmentation tools on a 1.5 T 3DT1 brain MRI dataset from a monocentric population of 89 FASD (52 FAS, 37 NS‐FASD) and 126 typically developing controls (6–20 years old), providing 8 volumes: cerebellum, vermis and 3 lobes (anterior, posterior, inferior), plus total brain volume. After adjustment of confounders, the allometric scaling relationship between these cerebellar volumes (Vi) and the total brain or cerebellum volume (Vt) was fitted (Vi = bVta), and the effect of group (FAS, control) on allometric scaling was evaluated. We then estimated for each cerebellar volume in the FAS population the deviation from the typical scaling (vDTS) learned in the controls. Lastly, we trained and tested two classifiers to discriminate FAS from controls, one based on the total cerebellum vDTS only, the other based on all the cerebellar vDTS, comparing their performance both in the FAS and the NS‐FASD group. Allometric scaling was significantly different between FAS and control group for all the cerebellar volumes (p <.001). We confirmed the excess of total cerebellum volume deficit (vDTS = −10.6%) and revealed an antero‐inferior‐posterior gradient of volumetric undersizing in the hemispheres (−12.4%, 1.1%, 2.0%, respectively) and the vermis (−16.7%, −9.2%, −8.6%, repectively). The classifier based on the intracerebellar gradient of vDTS performed more efficiently than the one based on total cerebellum vDTS only (AUC = 92% vs. 82%, p =.001). Setting a high probability threshold for >95% specificity of the classifiers, the gradient‐based classifier identified 35% of the NS‐FASD to have a FAS cerebellar phenotype, compared to 11% with the cerebellum‐only classifier (pFISHER = 0.027). In a large series of FASD, this study details the volumetric undersizing within the cerebellum at the lobar and vermian level using allometric scaling, revealing an anterior‐inferior‐posterior gradient of vulnerability to prenatal alcohol exposure. It also strongly suggests that this intracerebellar gradient of volumetric undersizing may be a reliable neuroanatomical signature of FAS that could be used to improve the specificity of the diagnosis of NS‐FASD. [ABSTRACT FROM AUTHOR]

The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields in vivo is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size. Furthermore, there is to date no unified segmentation protocol for the hippocampal subfields, which limits comparisons between studies. Therefore, we introduced a novel segmentation tool called HSF short for hippocampal segmentation factory, which leverages an end-to-end deep learning pipeline. First, we validated HSF against currently used tools (ASHS, HIPS, and HippUnfold). Then, we used HSF on 3,750 subjects from the HCP development, young adults, and aging datasets to study the effect of age and sex on hippocampal subfields volumes. Firstly, we showed HSF to be closer to manual segmentation than other currently used tools (p < 0.001), regarding the Dice Coefficient, Hausdorff Distance, and Volumetric Similarity. Then, we showed differential maturation and aging across subfields, with the dentate gyrus being the most affsected by age. We also found faster growth and decay in men than in women for most hippocampal subfields. Thus, while we introduced a new, fast and robust end-to-end segmentation tool, our neuroanatomical results concerning the lifespan trajectories of the hippocampal subfields reconcile previous conflicting results. [ABSTRACT FROM AUTHOR]

Background: Analysis of cortical folding may provide insight into neurodevelopment deviations, which, in turn, can predispose to depression that responds particularly poorly to medications. We hypothesized that patients with treatment-resistant depression would exhibit measurable alterations in cortical folding. Methods: We computed hemispheric global sulcal indices (g-Sls) in T, weighted magnetic resonance images obtained from 76 patients and 70 healthy controls. We separately searched for anatomic deviations in patients with bipolar disorder (16 patients with treatment-resistant depression, 25 with euthymia) and unipolar depression (35 patients with treatment-resistant depression). Results: Compared with healthy controls, both groups of patients with treatment-resistant depression exhibited reduced g-Sls: in the right hemisphere among patients with bipolar disorder and in both hemispheres among those with unipolar depression. Patients with euthymic bipolar disorder did not differ significantly from depressed patients or healthy controls. Among patients with bipolar disorder who were taking lithium, we found positive correlations between current lithium dose and g-Sls in both hemispheres. Limitations: We cannot estimate the extent to which the observed g-SI reductions are linked to treatment resistance and to what extent they are state-dependent. Furthermore, we cannot disentangle the impact of medications from that of the affective disorder. Finally, there is interindividual variation and overlap of g-Sls among patients and healthy controls that need to be considered when interpreting our results. Conclusion: Reduced global cortical folding surface appears to be characteristic of patients with treatment-resistant depression, either unipolar or bipolar. In patients with bipolar disorder, treatment with lithium may modify cortical folding surface. Contexte: L&apos;examen des plissements du cortex pourrait fournir un argument en faveur de deviations de son developpement qui, en retour, predisposeraient certains individus a la depression refractaire a la pharmacotherapie. Nous avons formule l&apos;hypothese que les patients ayant une depression refractaire au traitement presenteraient des anomalies mesurables des plissements corticaux. Methodes Nous avons calcula des indices hemispheriques globaux de sulcation (g-SI) a partir des images par resonance magnetique ponderees en T, obtenues chez 76 patients et de 70 temoins sains. Nous avons recherche des deviations anatomiques de maniere separee chez des patients atteints de trouble bipolaire (1e patients ayant une depression refractaire au traitement et 25 en etat euthymique) et chez des patients ayant une depression unipolaire (35 patients atteints de depression refractaire au traitement). Resultats : Comparativement aux temoins sains, les 2 groupes de patients atteints de depression refractaire au traitement presentaient une diminution des g-SI : clans l&apos;hemisphere droit chez les patients atteints de trouble bipolaire et bilateralement chez les patients atteints de depression unipolaire. Les patients ayant un trouble bipolaire euthymique ne differaient pas sign if icativement des patients deprimes ou des temoins sains. Parmi les patients ayant un trouble bipolaire qui prenaient du lithium, nous avons observa des correlations positives entre la dose actuelle de lithium et les indices de sulcation clans les 2 hemispheres. Limites : Nous ne pouvons determiner clans quelle mesure les recluctions des indices observees sont lides a la resistance au traitement ou a l&apos;etat des patients. En outre, nous ne pouvons distinguer (&apos;impact des meclicaments de celui des troubles affectifs. Enfin, l&apos;existance d&apos;une variation inter-individuelle et d&apos;un chevauchement des valeurs des g-SI entre patients et temoins doit etre prise en compte clans l&apos;interpretation des resultats. Conclusion : La recluction globale de la surface des plissements corticaux semble caracteristique des patients ayant une depression refractaire au traitement qu&apos;elle soit unipolaire ou bipolaire. Chez les patients atteints de trouble bipolaire, le traitement au lithium pourrait modifier la surface des sillons corticaux., Introduction Affective disorders have increasingly been associated with deviations in brain structure, (1-3) such as a widening of cortical sulci in bipolar disorder. (4) Some of these alterations may be [...]

Positron emission tomography (PET) is a useful tool for pharmacokinetics studies in rodents during the preclinical phase of drug and tracer development. However, rodent organs are small as compared to the scanner's intrinsic resolution and are affected by physiological movements. We present a new method for the segmentation of rodent whole-body PET images that takes these two difficulties into account by estimating the pharmacokinetics far from organ borders. The segmentation method proved efficient on whole-body numerical rat phantom simulations, including 3-14 organs, together with physiological movements (heart beating, breathing, and bladder filling). The method was resistant to spillover and physiological movements, while other methods failed to obtain a correct segmentation. The radioactivity concentrations calculated with this method also showed an excellent correlation with the manual delineation of organs in a large set of preclinical images. In addition, it was faster, detected more organs, and extracted organs' mean time activity curves with a better confidence on the measure than manual delineation. Index Terms--Partial volume effect, physiological movements, positron emission tomography (PET), rodent whole-body, segmentation.

We describe here a classification system based on automatically identified cortical sulci. Multivariate recognition methods are required for the detection of complex brain patterns with a spatial distribution. However, such methods may face the well-known issue of the curse of dimensionality--the risk of overfitting the training dataset in high-dimensional space. We overcame this problem, using a classifier pipeline with one- or two-stage of descriptor selection based on machine-learning methods, followed by a support vector machine classifier or linear discriminant analysis. We compared alternative designs of the pipeline on two different datasets built from the same database corresponding to 151 brains. The first dataset dealt with cortex asymmetry and the second dealt with the effect of the subject's sex. Our system successfully (98%) distinguished between the left and right hemispheres on the basis of sulcal shape (size, depth, etc.). The sex of the subject could be determined with a success rate of 85%. These results highlight the attractiveness of multivariate recognition models combined with appropriate descriptor selection. The sulci selected by the pipeline are consistent with previous whole-brain studies on sex effects and hemispheric asymmetries. Index Terms--Feature selection, pattern recognition, sulcal morphometry.

Background: Brain development is of utmost importance for the emergence of psychiatric disorders, as the most severe of them arise before 25 years old. However, little is known regarding how early transdiagnostic symptoms, in a dimensional framework, are associated with cortical development. Anxiety and irritability are central vulnerability traits for subsequent mood and anxiety disorders. In this study, we investigate how these dimensions are related to structural changes in the brain to understand how they may increase the transition risk to full-blown disorders. Methods: We used the opportunity of an open access developmental cohort, the Healthy Brain Network, to investigate associations between cortical surface markers and irritability and anxiety scores as measured by parents and self-reports. Results: We found that in 658 young people (with a mean age of 11.6) the parental report of irritability is associated with decreased surface area in the bilateral rostral prefrontal cortex and the precuneus. Furthermore, parental reports of anxiety were associated with decreased local gyrification index in the anterior cingulate cortex and dorsomedial prefrontal cortex. Conclusions: These results are consistent with current models of emotion regulation network maturation, showing decreased surface area or gyrification index in regions associated with impaired affective control in mood and anxiety disorders. Our results highlight how dimensional traits may increase vulnerability for these disorders. [ABSTRACT FROM AUTHOR]

Background: Lithium (Li) is the gold standard treatment for bipolar disorder (BD). However, its mechanisms of action remain unknown but include neurotrophic effects. We here investigated the influence of Li on cortical and local grey matter (GM) volumes in a large international sample of patients with BD and healthy controls (HC). Methods: We analyzed high-resolution T1-weighted structural magnetic resonance imaging scans of 271 patients with BD type I (120 undergoing Li) and 316 HC. Cortical and local GM volumes were compared using voxel-wise approaches with voxel-based morphometry and SIENAX using FSL. We used multiple linear regression models to test the influence of Li on cortical and local GM volumes, taking into account potential confounding factors such as a history of alcohol misuse. Results: Patients taking Li had greater cortical GM volume than patients without. Patients undergoing Li had greater regional GM volumes in the right middle frontal gyrus, the right anterior cingulate gyrus, and the left fusiform gyrus in comparison with patients not taking Li. Conclusions: Our results in a large multicentric sample support the hypothesis that Li could exert neurotrophic and neuroprotective effects limiting pathological GM atrophy in key brain regions associated with BD. [ABSTRACT FROM AUTHOR]

Objectives: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. Method: We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. Result: We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. Conclusions: Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer‐aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI‐based ML studies in BD. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a developmental disorder underdiagnosed in adults. To date, no consistent evidence of alterations in brain structure has been reported in adults with ASD and few studies were conducted at that age. We analyzed structural magnetic resonance imaging data from 167 high functioning adults with ASD and 195 controls. We ran our analyses on a discovery (n = 301) and a replication sample (n = 61). The right caudal anterior cingulate cortical thickness was significantly thinner in adults with ASD compared to controls in both the discovery and the replication sample. Our work underlines the relevance of studying the brain anatomy of an adult ASD population. [ABSTRACT FROM AUTHOR]

Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov’s smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov’s smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision. [ABSTRACT FROM AUTHOR]

Objective: In CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy), white matter hyperintensities (WMH) are considered to result from hypoperfusion. We hypothesized that in fact the burden of WMH results from the combination of several regional populations of WMH with different mechanisms and clinical consequences. Methods: To identify regional WMH populations, we used a 4-step approach. First, we used an unsupervised principal component algorithm to determine, without a priori knowledge, the main sources of variation of the global spatial pattern of WMH. Thereafter, to determine whether these sources are likely to include relevant information regarding regional populations of WMH, we tested their relationships with: (1) MRI markers of the disease; (2) the clinical severity assessed by the Mattis Dementia Rating scale (MDRS) (cognitive outcome) and the modified Rankin's score (disability outcome). Finally, through careful interpretation of all the results, we tried to identify different regional populations of WMH. Results: The unsupervised principal component algorithm identified 3 main sources of variation of the global spatial pattern of WMH, which showed significant and sometime inverse relationships with MRI markers and clinical scores. The models predicting clinical severity based on these sources outperformed those evaluating WMH by their volume (MDRS, coefficient of determination of 39.0 vs. 35.3%, p = 0.01; modified Rankin's score, 43.7 vs. 38.1%, p = 0.001). By carefully interpreting the visual aspect of these sources as well as their relationships with MRI markers and clinical severity, we found strong arguments supporting the existence of different regional populations of WMH. For instance, in multivariate analyses, larger extents of WMH in anterior temporal poles and superior frontal gyri were associated with better outcomes, while larger extents of WMH in pyramidal tracts were associated with worse outcomes, which could not be explained if WMH in these different areas shared the same mechanisms. Conclusion: The results of the present study support the hypothesis that the whole extent of WMH results from a combination of different regional populations of WMH, some of which are associated, for yet undetermined reasons, with milder forms of the disease. [ABSTRACT FROM AUTHOR]