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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]

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]

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]

Abstract: Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time‐consuming. This article first proposes a machine‐learning algorithm to automatically identify resting‐state fMRI periods that precede hallucinations versus periods that do not. When applied to whole‐brain fMRI data, state‐of‐the‐art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech‐related brain regions. The variation in transition‐to‐hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI‐guided therapy for drug‐resistant hallucinations, such as fMRI‐based neurofeedback. [ABSTRACT FROM AUTHOR]

ABSTRACT The dopamine (DA) system is known to be involved in the reward and dependence mechanisms of addiction. However, modifications in dopaminergic neurotransmission associated with long-term tobacco and cannabis use have been poorly documented in vivo. In order to assess striatal and extrastriatal dopamine transporter (DAT) availability in tobacco and cannabis addiction, three groups of male age-matched subjects were compared: 11 healthy non-smoker subjects, 14 tobacco-dependent smokers (17.6 ± 5.3 cigarettes/day for 12.1 ± 8.5 years) and 13 cannabis and tobacco smokers (CTS) (4.8 ± 5.3 cannabis joints/day for 8.7 ± 3.9 years). DAT availability was examined in positron emission tomography (HRRT) with a high resolution research tomograph after injection of [11C]PE2I, a selective DAT radioligand. Region of interest and voxel-by-voxel approaches using a simplified reference tissue model were performed for the between-group comparison of DAT availability. Measurements in the dorsal striatum from both analyses were concordant and showed a mean 20% lower DAT availability in drug users compared with controls. Whole-brain analysis also revealed lower DAT availability in the ventral striatum, the midbrain, the middle cingulate and the thalamus (ranging from −15 to −30%). The DAT availability was slightly lower in all regions in CTS than in subjects who smoke tobacco only, but the difference does not reach a significant level. These results support the existence of a decrease in DAT availability associated with tobacco and cannabis addictions involving all dopaminergic brain circuits. These findings are consistent with the idea of a global decrease in cerebral DA activity in dependent subjects. [ABSTRACT FROM AUTHOR]

Objectives: Cerebral abnormalities have been detected in patients with bipolar disorder (BD). In comparison to BD with a later onset, early-onset BD has been found to have a poorer outcome. However, it is yet unknown whether neuroanatomical abnormalities differ between age-at-onset subgroups of the illness. We searched for cortical folding differences between early-onset (before 25 years) and intermediate-onset (between 25 and 45 years) BD patients. Methods: Magnetic resonance images of 22 early-onset BD patients, 14 intermediate-onset BD patients, and 50 healthy participants were analyzed using a fully automated method to extract, label, and measure the sulcal area in the whole cortex. Cortical folding was assessed by computing global sulcal indices (the ratio between total sulcal area and total outer cortex area) for each hemisphere, and local sulcal indices for 12 predefined regions in both hemispheres. Results: Intermediate-onset BD patients had a significantly reduced local sulcal index in the right dorsolateral prefrontal cortex in comparison to both early-onset BD patients and healthy subjects, and lower global sulcal indices in both hemispheres in comparison to healthy subjects (p < 0.05, Bonferroni corrected). Brain tissue volumes did not differ between groups. Conclusions: This study provided the first evidence of a neuroanatomic difference between intermediate-onset and early-onset BD, which lends further support to the existence of different age-at-onset subgroups of BD. [ABSTRACT FROM AUTHOR]

Normal cognitive development in infants follows a well-known temporal sequence, which is assumed to be correlated with the structural maturation of underlying functional networks. Postmortem studies and, more recently, structural MR imaging studies have described qualitatively the heterogeneous spatiotemporal progression of white matter myelination. However, in vivo quantification of the maturation phases of fiber bundles is still lacking. We used noninvasive diffusion tensor MR imaging and tractography in twenty-three 1-4-month-old healthy infants to quantify the early maturation of the main cerebral fascicles. A specific maturation model, based on the respective roles of different maturational processes on the diffusion phenomena, was designed to highlight asynchronous maturation across bundles by evaluating the time-course of mean diffusivity and anisotropy changes over the considered developmental period. Using an original approach, a progression of maturation in four relative stages was determined in each tract by estimating the maturation state and speed, from the diffusion indices over the infants group compared with an adults group on one hand, and in each tract compared with the average over bundles on the other hand. Results were coherent with, and extended previous findings in 8 of 11 bundles, showing the anterior limb of the internal capsule and cingulum as the most immature, followed by the optic radiations, arcuate and inferior longitudinal fascicles, then the spinothalamic tract and fornix, and finally the corticospinal tract as the most mature bundle. Thus, this approach provides new quantitative landmarks for further noninvasive research on brain-behavior relationships during normal and abnormal development. Hum Brain Mapp, 2008. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]