Functional connectivity of the amygdala subnuclei in various mood states of bipolar disorder.

Amygdala functional dysconnectivity lies at the heart of the pathophysiology of bipolar disorder (BD). Recent preclinical studies suggest that the amygdala is a heterogeneous group of nuclei, whose specific connectivity could drive positive or negative emotional valence. We investigated functional connectivity (FC) changes within these circuits emerging from each amygdala's subdivision in 127 patients with BD in different mood states and 131 healthy controls (HC), who underwent resting-state functional MRI. FC was evaluated between lateral and medial nuclei of amygdalae, and key subcortical regions of the emotion processing network: anterior and posterior parts of the hippocampus, and core and shell parts of the nucleus accumbens. FC was compared across groups, and subgroups of patients depending on their mood states, using linear mixed models. We also tested correlations between FC and depression (MADRS) and mania (YMRS) scores. We found no difference between the whole sample of BD patients vs. HC but a significant correlation between MADRS and right lateral amygdala /right anterior hippocampus, right lateral amygdala/right posterior hippocampus and right lateral amygdala/left anterior hippocampus FC (r = -0.44, r = -0.32, r = -0.27, respectively, all pFDR<0.05). Subgroup analysis revealed decreased right lateral amygdala/right anterior hippocampus and right lateral amygdala/right posterior hippocampus FC in depressed vs. non-depressed patients and increased left medial amygdala/shell part of the left nucleus accumbens FC in manic vs non-manic patients. These results demonstrate that acute mood states in BD concur with FC changes in individual nuclei of the amygdala implicated in distinct emotional valence processing. Overall, our data highlight the importance to consider the amygdala subnuclei separately when studying its FC patterns including patients in distinct homogeneous mood states.

Cerebellum and social abilities: A structural and functional connectivity study in a transdiagnostic sample.

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.

Childhood trauma moderates schizotypy-related brain morphology: analyses of 1182 healthy individuals from the ENIGMA schizotypy working group.

BACKGROUND: Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy. METHODS: We addressed this question using data from a total of 1182 healthy adults (age range: 18-65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined. RESULTS: A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure. CONCLUSIONS: These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.

The Cerebellum and Cognitive Function: Anatomical Evidence from a Transdiagnostic Sample.

Multiple lines of evidence across human functional, lesion, and animal data point to a cerebellar role, in particular of crus I, crus II, and lobule VIIB, in cognitive function. However, a mapping of distinct facets of cognitive function to cerebellar structure is missing. We analyzed structural neuroimaging data from the Healthy Brain Network (HBN). Cerebellar parcellation was performed with a validated automated segmentation pipeline (CERES) and stringent visual quality check (n = 662 subjects retained from initial n = 1452). Canonical correlation analyses (CCA) examined regional gray matter volumetric (GMV) differences in association to cognitive function (quantified with NIH Toolbox Cognition domain, NIH-TB), accounting for psychopathology severity, age, sex, scan location, and intracranial volume. Multivariate CCA uncovered a significant correlation between two components entailing a latent cognitive canonical (NIH-TB subscales) and a brain canonical variate (cerebellar GMV and intracranial volume, ICV), surviving bootstrapping and permutation procedures. The components correspond to partly shared cerebellar-cognitive function relationship with a first map encompassing cognitive flexibility (r = 0.89), speed of processing (r = 0.65), and working memory (r = 0.52) associated with regional GMV in crus II (r = 0.57) and lobule X (r = 0.59) and a second map including the crus I (r = 0.49) and lobule VI (r = 0.49) associated with working memory (r = 0.51). We show evidence for a structural subspecialization of the cerebellum topography for cognitive function in a transdiagnostic sample.

Progressive grey matter alterations in bipolar disorder across the life span - A systematic review.

OBJECTIVES: To elucidate the relationship between the course of bipolar disorder (BD) and structural brain changes across the life span, we conducted a systematic review of longitudinal imaging studies in adolescent and adult BD patients. METHODS: Eleven studies with 329 BD patients and 277 controls met our PICOS criteria (participants, intervention, comparison, outcome and study design): BD diagnosis based on DSM criteria, natural course of disease, comparison of grey matter changes in BD individuals over ≥1-year interval between scans. RESULTS: The selected studies yielded heterogeneous findings, partly due to varying patient characteristics, data acquisition and statistical models. Mood episodes were associated with greater grey matter loss in frontal brain regions over time. Brain volume decreased or remained stable in adolescent patients, whereas it increased in healthy adolescents. Adult BD patients showed increased cortical thinning and brain structural decline. In particular, disease onset in adolescence was associated with amygdala volume reduction, which was not reported in adult BD. CONCLUSIONS: The evidence collected suggests that the progression of BD impairs adolescent brain development and accelerates structural brain decline across the lifespan. Age-specific changes in amygdala volume in adolescent BD suggest that reduced amygdala volume is a correlate of early onset BD. Clarifying the role of BD in brain development across the lifespan promises a deeper understanding of the progression of BD patients through different developmental episodes.

Aberrant Subnetwork and Hub Dysconnectivity in Adult Bipolar Disorder: A Multicenter Graph Theory Analysis.

Neuroimaging evidence implicates structural network-level abnormalities in bipolar disorder (BD); however, there remain conflicting results in the current literature hampered by sample size limitations and clinical heterogeneity. Here, we set out to perform a multisite graph theory analysis to assess the extent of neuroanatomical dysconnectivity in a large representative study of individuals with BD. This cross-sectional multicenter international study assessed structural and diffusion-weighted magnetic resonance imaging data obtained from 109 subjects with BD type 1 and 103 psychiatrically healthy volunteers. Whole-brain metrics, permutation-based statistics, and connectivity of highly connected nodes were used to compare network-level connectivity patterns in individuals with BD compared with controls. The BD group displayed longer characteristic path length, a weakly connected left frontotemporal network, and increased rich-club dysconnectivity compared with healthy controls. Our multisite findings implicate emotion and reward networks dysconnectivity in bipolar illness and may guide larger scale global efforts in understanding how human brain architecture impacts mood regulation in BD.

Asynchronous neural maturation predicts transition to psychosis.

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.

Disruption of Conscious Access in Psychosis Is Associated with Altered Structural Brain Connectivity.

According to global neuronal workspace (GNW) theory, conscious access relies on long-distance cerebral connectivity to allow a global neuronal ignition coding for conscious content. In patients with schizophrenia and bipolar disorder, both alterations in cerebral connectivity and an increased threshold for conscious perception have been reported. The implications of abnormal structural connectivity for disrupted conscious access and the relationship between these two deficits and psychopathology remain unclear. The aim of this study was to determine the extent to which structural connectivity is correlated with consciousness threshold, particularly in psychosis. We used a visual masking paradigm to measure consciousness threshold, and diffusion MRI tractography to assess structural connectivity in 97 humans of either sex with varying degrees of psychosis: healthy control subjects (n = 46), schizophrenia patients (n = 25), and bipolar disorder patients with (n = 17) and without (n = 9) a history of psychosis. Patients with psychosis (schizophrenia and bipolar disorder with psychotic features) had an elevated masking threshold compared with control subjects and bipolar disorder patients without psychotic features. Masking threshold correlated negatively with the mean general fractional anisotropy of white matter tracts exclusively within the GNW network (inferior frontal-occipital fasciculus, cingulum, and corpus callosum). Mediation analysis demonstrated that alterations in long-distance connectivity were associated with an increased masking threshold, which in turn was linked to psychotic symptoms. Our findings support the hypothesis that long-distance structural connectivity within the GNW plays a crucial role in conscious access, and that conscious access may mediate the association between impaired structural connectivity and psychosis.

Superficial white matter bundle atlas based on hierarchical fiber clustering over probabilistic tractography data.

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, we present a multi-subject atlas of short association fibers, which was computed using a superficial white matter bundle identification method based on fiber clustering. To create the atlas, we used probabilistic tractography from one hundred subjects from the HCP database, aligned with non-linear registration. The method starts with an intra-subject clustering of short fibers (30-85 mm). Based on a cortical atlas, the intra-subject cluster centroids from all subjects are segmented to identify the centroids connecting each region of interest (ROI) of the atlas. To reduce computational load, the centroids from each ROI group are randomly separated into ten subgroups. Then, an inter-subject hierarchical clustering is applied to each centroid subgroup, followed by a second level of clustering to select the most-reproducible clusters across subjects for each ROI group. Finally, the clusters are labeled according to the regions that they connect, and clustered to create the final bundle atlas. The resulting atlas is composed of 525 bundles of superficial short association fibers along the whole brain, with 384 bundles connecting pairs of different ROIs and 141 bundles connecting portions of the same ROI. The reproducibility of the bundles was verified using automatic segmentation on three different tractogram databases. Results for deterministic and probabilistic tractography data show high reproducibility, especially for probabilistic tractography in HCP data. In comparison to previous work, our atlas features a higher number of bundles and greater cortical surface coverage.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Lithium prevents grey matter atrophy in patients with bipolar disorder: an international multicenter study.

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.

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

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.

ENIGMA-Sleep: Challenges, opportunities, and the road map.

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

FFClust: Fast fiber clustering for large tractography datasets for a detailed study of brain connectivity.

Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases. The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies-Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8.6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

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.

Automatic group-wise whole-brain short association fiber bundle labeling based on clustering and cortical surface information.

BACKGROUND: Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-subject variability and artifacts, the resulting clusters are difficult to process for finding common connections across subjects, specially for superficial white matter. METHODS: We present an automatic method for labeling of short association bundles on a group of subjects. The method is based on an intra-subject fiber clustering that generates compact fiber clusters. Posteriorly, the clusters are labeled based on the cortical connectivity of the fibers, taking as reference the Desikan-Killiany atlas, and named according to their relative position along one axis. Finally, two different strategies were applied and compared for the labeling of inter-subject bundles: a matching with the Hungarian algorithm, and a well-known fiber clustering algorithm, called QuickBundles. RESULTS: Individual labeling was executed over four subjects, with an execution time of 3.6 min. An inspection of individual labeling based on a distance measure showed good correspondence among the four tested subjects. Two inter-subject labeling were successfully implemented and applied to 20 subjects and compared using a set of distance thresholds, ranging from a conservative value of 10 mm to a moderate value of 21 mm. Hungarian algorithm led to a high correspondence, but low reproducibility for all the thresholds, with 96 s of execution time. QuickBundles led to better correspondence, reproducibility and short execution time of 9 s. Hence, the whole processing for the inter-subject labeling over 20 subjects takes 1.17 h. CONCLUSION: We implemented a method for the automatic labeling of short bundles in individuals, based on an intra-subject clustering and the connectivity of the clusters with the cortex. The labels provide useful information for the visualization and analysis of individual connections, which is very difficult without any additional information. Furthermore, we provide two fast inter-subject bundle labeling methods. The obtained clusters could be used for performing manual or automatic connectivity analysis in individuals or across subjects.

Higher in vivo Cortical Intracellular Volume Fraction Associated with Lithium Therapy in Bipolar Disorder: A Multicenter NODDI Study.

BACKGROUND: MRI studies in patients with bipolar disorder have suggested that lithium is associated with grey matter increases that may underlie its therapeutic effects. However, the relationship between grey matter volume and cellular microstructural changes is not straightforward, as modifications of different cellular compartments of grey matter may be involved. OBJECTIVES: Our aim was to test the hypothesis that dendritic density is higher in patients undergoing lithium therapy than in patients without lithium, using advanced modelling of water diffusion investigated with MRI. METHOD: We included 41 patients and 40 controls matched for age and gender from two sites. All subjects underwent 3T MRI with 3 shells of diffusion. We used neurite orientation dispersion and density imaging to compare the grey matter neurite density between patients undergoing lithium therapy or not and control subjects. RESULTS: We found a significant group effect in the left prefrontal region (p = 0.001, Bonferroni corrected): patients without lithium had a lower frontal neurite density than controls (p = 0.009), while those on lithium had a higher mean neurite density than those without (p < 0.001). Patients on lithium were not different from controls (p = 0.08). CONCLUSIONS: This is the first study to report in vivo evidence of preserved neurite density of the prefrontal cortex in humans associated with lithium intake. Changes of intracellular volume fraction are thought to reflect changes of grey matter microstructural organization. This reinforces the hypothesis of lithium having a positive effect on the neuronal compartment in humans.

Local structural connectivity is associated with social cognition in autism spectrum disorder.

The current theory implying local, short-range overconnectivity in autism spectrum disorder, contrasting with long-range underconnectivity, is based on heterogeneous results, on limited data involving functional connectivity studies, on heterogeneous paediatric populations and non-specific methodologies. In this work, we studied short-distance structural connectivity in a homogeneous population of males with high-functioning autism spectrum disorder and used a novel methodology specifically suited for assessing U-shaped short-distance tracts, including a recently developed tractography-based atlas of the superficial white matter fibres. We acquired diffusion-weighted MRI for 58 males (27 subjects with high-functioning autism spectrum disorder and 31 control subjects) and extracted the mean generalized fractional anisotropy of 63 short-distance tracts. Neuropsychological evaluation included Wechsler Adult Intelligence Scale IV (WAIS-IV), Communication Checklist-Adult, Empathy Quotient, Social Responsiveness Scale and Behaviour Rating Inventory of Executive Function-Adult (BRIEF-A). In contradiction with the models of short-range over-connectivity in autism spectrum disorder, we found that patients with autism spectrum disorder had a significantly decreased anatomical connectivity in a component comprising 13 short tracts compared to controls. Specific short-tract atypicalities in temporal lobe and insula were significantly associated with clinical manifestations of autism spectrum disorder such as social awareness, language structure, pragmatic skills and empathy, emphasizing their importance in social dysfunction. Short-range decreased anatomical connectivity may thus be an important substrate of social deficits in autism spectrum disorder, in contrast with current models.