Neurodevelopmental subtypes of bipolar disorder are related to cortical folding patterns: An international multicenter study

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders

BACKGROUND

Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine.

METHODS

We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort.

RESULTS

Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable.

CONCLUSIONS

Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Advances in the understanding of the pathophysiology of schizophrenia and bipolar disorder through induced pluripotent stem cell models

The pathophysiology of schizophrenia and bipolar disorder involves a complex interaction between genetic and environmental factors that begins in the early stages of neurodevelopment. Recent advancements in the field of induced pluripotent stem cells (iPSCs) offer a promising tool for understanding the neurobiological alterations involved in these disorders and, potentially, for developing new treatment options. In this review, we summarize the results of iPSC-based research on schizophrenia and bipolar disorder, showing disturbances in neurodevelopmental processes, imbalance in glutamatergic-GABAergic transmission and neuromorphological alterations. The limitations of the reviewed literature are also highlighted, particularly the methodological heterogeneity of the studies, the limited number of studies developing iPSC models of both diseases simultaneously, and the lack of in-depth clinical characterization of the included samples. Further studies are needed to advance knowledge on the common and disease-specific pathophysiological features of schizophrenia and bipolar disorder and to promote the development of new treatment options.

Development of cortical folds in the human brain: An attempt to review biological hypotheses, early neuroimaging investigations and functional correlates

The folding of the human brain mostly takes place in utero, making it challenging to study. After a few pioneer studies looking into it in post-mortem foetal specimen, modern approaches based on neuroimaging have allowed the community to investigate the folding process in vivo, its normal progression, its early disturbances, and its relationship to later functional outcomes. In this review article, we aimed to first give an overview of the current hypotheses on the mechanisms governing cortical folding. After describing the methodological difficulties raised by its study in fetuses, neonates and infants with magnetic resonance imaging (MRI), we reported our current understanding of sulcal pattern emergence in the developing brain. We then highlighted the functional relevance of early sulcal development, through recent insights about hemispheric asymmetries and early factors influencing this dynamic such as prematurity. Finally, we outlined how longitudinal studies have started to relate early folding markers and the child's sensorimotor and cognitive outcome. Through this review, we hope to raise awareness on the potential of studying early sulcal patterns both from a fundamental and clinical perspective, as a window into early neurodevelopment and plasticity in relation to growth in utero and postnatal environment of the child.

Global and local cortical folding alterations are associated with neurodevelopmental subtype in bipolar disorders: a sulcal pits analysis

BACKGROUND

Analyzing cortical folding may provide insight into the biological underpinnings of neurodevelopmental diseases. A neurodevelopmental subtype of bipolar disorders (BD-ND) has been characterized by the combination of early age of onset and psychotic features. We investigate potential cortical morphology differences associated with this subtype. We analyze, for the first time in bipolar disorders, the sulcal pits, the deepest points in each fold of the cerebral cortex.

METHODS

We extracted the sulcal pits from anatomical MRI among 512 participants gathered from 7 scanning sites. We compared the number of sulcal pits in each hemisphere as well as their regional occurrence and depth between the BD-ND subgroup (N = 184), a subgroup without neurodevelopmental features (BD, N = 77) and a group of healthy controls (HC, N = 251).

RESULTS

In whole brain analysis, BD-ND group have a higher number of sulcal pits in comparison to the BD group. The local analysis revealed, after correction for multiple testing, a higher occurrence of sulcal pits in the left premotor cortex among the BD-ND subgroup compared to the BD and the HC groups.

CONCLUSION

Our findings confirm that BD-ND is associated with a specific brain morphology revealed by the analysis of sulcal pits. These markers may help to better understand neurodevelopment in mood disorder and stratify patients according to a pathophysiological hypothesis.

Increased cortical surface area but not altered cortical thickness or gyrification in bipolar disorder following stabilisation from a first episode of mania

BACKGROUND

Despite reports of altered brain morphology in established bipolar disorder (BD), there is limited understanding of when these morphological abnormalities emerge. Assessment of patients during the early course of illness can help to address this gap, but few studies have examined surface-based brain morphology in patients at this illness stage.

METHODS

We completed a secondary analysis of baseline data from a randomised control trial of BD individuals stabilised after their first episode of mania (FEM). The magnetic resonance imaging scans of n = 35 FEM patients and n = 29 age-matched healthy controls were analysed. Group differences in cortical thickness, surface area and gyrification were assessed at each vertex of the cortical surface using general linear models. Significant results were identified at p < 0.05 using cluster-wise correction.

RESULTS

The FEM group did not differ from healthy controls with regards to cortical thickness or gyrification. However, there were two clusters of increased surface area in the left hemisphere of FEM patients, with peak coordinates falling within the lateral occipital cortex and pars triangularis.

CONCLUSIONS

Cortical thickness and gyrification appear to be intact in the aftermath of a first manic episode, whilst cortical surface area in the inferior/middle prefrontal and occipitoparietal cortex is increased compared to age-matched controls. It is possible that increased surface area in the FEM group is the outcome of abnormalities in a premorbidly occurring process. In contrast, the findings raise the hypothesis that cortical thickness reductions seen in past studies of individuals with more established BD may be more attributable to post-onset factors.

Definition of early age at onset in bipolar disorder according to distinctive neurodevelopmental pathways: insights from the FACE-BD study

BACKGROUND

Converging evidence suggests that a subgroup of bipolar disorder (BD) with an early age at onset (AAO) may develop from aberrant neurodevelopment. However, the definition of early AAO remains unprecise. We thus tested which age cut-off for early AAO best corresponds to distinguishable neurodevelopmental pathways.

METHODS

We analyzed data from the FondaMental Advanced Center of Expertise-Bipolar Disorder cohort, a naturalistic sample of 4421 patients. First, a supervised learning framework was applied in binary classification experiments using neurodevelopmental history to predict early AAO, defined either with Gaussian mixture models (GMM) clustering or with each of the different cut-offs in the range 14 to 25 years. Second, an unsupervised learning approach was used to find clusters based on neurodevelopmental factors and to examine the overlap between such data-driven groups and definitions of early AAO used for supervised learning.

RESULTS

A young cut-off, i.e. 14 up to 16 years, induced higher separability [mean nested cross-validation test AUROC = 0.7327 (± 0.0169) for ⩽16 years]. Predictive performance deteriorated increasing the cut-off or setting early AAO with GMM. Similarly, defining early AAO below 17 years was associated with a higher degree of overlap with data-driven clusters (Normalized Mutual Information = 0.41 for ⩽17 years) relatively to other definitions.

CONCLUSIONS

Early AAO best captures distinctive neurodevelopmental patterns when defined as ⩽17 years. GMM-based definition of early AAO falls short of mapping to highly distinguishable neurodevelopmental pathways. These results should be used to improve patients' stratification in future studies of BD pathophysiology and biomarkers.

The effect of early trauma on suicidal vulnerability depends on fronto-insular sulcation

Improving our understanding of pathophysiology of suicidal behavior (SB) is an important step for prevention. Assessment of suicide risk is based on socio-demographic and clinical risk factors with a poor predictivity. Current understanding of SB is based on a stress-vulnerability model, whereby early-life adversities are predominant. SB may thus result from a cascade of developmental processes stemming from early-life abuse and/or neglect. Some cerebral abnormalities, particularly in fronto-limbic regions, might also provide vulnerability to develop maladaptive responses to stress, leading to SB. We hypothesized that SB is associated with interactions between early trauma and neurodevelopmental deviations of the frontal and insular cortices. We recruited 86 euthymic women, including 44 suicide attempters (history of depression and SB) and 42 affective controls (history of depression without SB). The early development of prefrontal cortex (PFC) and insula was inferred using 3D magnetic resonance imaging-derived regional sulcation indices, which are indirect markers of early neurodevelopment. The insula sulcation index was higher in emotional abused subjects; among those patients, PFC sulcation index was reduced in suicide attempters, but not in affective controls. Such findings provide evidence that SB likely traced back to early stages of brain development in interaction with later environmental factors experienced early in life.

Lifetime and point prevalence of psychotic symptoms in adults with bipolar disorders: a systematic review and meta-analysis

Psychotic symptoms, that we defined as delusions or hallucinations, are common in bipolar disorders (BD). This systematic review and meta-analysis aims to synthesise the literature on both lifetime and point prevalence rates of psychotic symptoms across different BD subtypes, including both BD type I (BDI) and BD type II (BDII). We performed a systematic search of Medline, PsycINFO, Embase and Cochrane Library until 5 August 2021. Fifty-four studies (N = 23 461) of adults with BD met the predefined inclusion criteria for evaluating lifetime prevalence, and 24 studies (N = 6480) for evaluating point prevalence. Quality assessment and assessment of publication bias were performed. Prevalence rates were calculated using random effects meta-analysis, here expressed as percentages with a 95% confidence interval (CI). In studies of at least moderate quality, the pooled lifetime prevalence of psychotic symptoms in BDI was 63% (95% CI 57.5-68) and 22% (95% CI 14-33) in BDII. For BDI inpatients, the pooled lifetime prevalence was 71% (95% CI 61-79). There were no studies of community samples or inpatient BDII. The pooled point prevalence of psychotic symptoms in BDI was 54% (95 CI 41-67). The point prevalence was 57% (95% CI 47-66) in manic episodes and 13% (95% CI 7-23.5) in depressive episodes. There were not enough studies in BDII, BDI depression, mixed episodes and outpatient BDI. The pooled prevalence of psychotic symptoms in BDI may be higher than previously reported. More studies are needed for depressive and mixed episodes and community samples.Prospero registration number: CRD 42017052706.

Decreased cortical gyrification in patients with bipolar disorder

BACKGROUND

An aberrant neural connectivity has been known to be associated with bipolar disorder (BD). Local gyrification may reflect the early neural development of cortical connectivity and has been studied as a possible endophenotype of psychiatric disorders. This study aimed to investigate differences in the local gyrification index (LGI) in each cortical region between patients with BD and healthy controls (HCs).

METHODS

LGI values, as measured using FreeSurfer software, were compared between 61 patients with BD and 183 HCs. The values were also compared between patients with BD type I and type II as a sub-group analysis. Furthermore, we evaluated whether there was a correlation between LGI values and illness duration or depressive symptom severity in patients with BD.

RESULTS

Patients with BD showed significant hypogyria in various cortical regions, including the left inferior frontal gyrus (pars opercularis), precentral gyrus, postcentral gyrus, superior temporal cortex, insula, right entorhinal cortex, and both transverse temporal cortices, compared to HCs after the Bonferroni correction (p < 0.05/66, 0.000758). LGI was not associated with clinical factors such as illness duration, depressive symptom severity, and lithium treatment. No significant differences in cortical gyrification according to the BD subtype were found.

CONCLUSIONS

BD appears to be characterized by a significant regionally localized hypogyria, in various cortical areas. This abnormality may be a structural and developmental endophenotype marking the risk for BD, and it might help to clarify the etiology of BD.

Neuroimaging Studies of Brain Structure in Older Adults with Bipolar Disorder: A Review

Bipolar disorder (BD) is a common mood disorder that can have severe consequences during later life, including suffering and impairment due to mood and cognitive symptoms, elevated risk for dementia and an especially high risk for suicide. Greater understanding of the brain circuitry differences involved in older adults with BD (OABD) in later life and their relationship to aging processes is required to improve outcomes of OABD. The current literature on gray and white matter findings, from high resolution structural and diffusion-weighted magnetic resonance imaging (MRI) studies, has shown that BD in younger age groups is associated with gray matter reductions within cortical and subcortical brain regions that subserve emotion processing and regulation, as well as reduced structural integrity of white matter tracts connecting these brain regions. While fewer neuroimaging studies have focused on OABD, it does appear that many of the structural brain differences found in younger samples are present in OABD. There is also initial suggestion that there are additional brain differences, for at least a subset of OABD, that may result from more pronounced gray and white matter declines with age that may contribute to adverse outcomes. Preclinical and clinical data supporting neuro-plastic and -protective effects of mood-stabilizing medications, suggest that treatments may reverse and/or prevent the progression of brain changes thereby reducing symptoms. Future neuroimaging research implementing longitudinal designs, and large-scale, multi-site initiatives with detailed clinical and treatment data, holds promise for reducing suffering, cognitive dysfunction and suicide in OABD.

Reaching into the toolbox: Stem cell models to study neuropsychiatric disorders

Recent advances in genetics, molecular biology, and stem cell biology have accelerated our understanding of neuropsychiatric disorders, like autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). This progress highlights the incredible complexity of both the human brain and mental illnesses from the biochemical to the cellular level. Contributing to the complexity of neuropsychiatric disorders are their polygenic nature, cellular and brain region interconnectivity, and dysregulation of human-specific neurodevelopmental processes. Here, we discuss available tools, including CRISPR-Cas9, and the applications of these tools to develop cell-based two-dimensional (2D) models and 3D brain organoid models that better represent and unravel the intricacies of neuropsychiatric disorder pathophysiology.

Age-dependent brain morphometry in Major Depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a complex disorder that affects nearly 264 million people worldwide. Structural brain abnormalities in multiple neuroanatomical networks have been implicated in the etiology of MDD, but the degree to which MDD affects brain structure during early to late adulthood is unclear.

METHODS

We examined morphometry of brain regions commonly implicated in MDD, including the amygdala, hippocampus, anterior cingulate gyrus, lateral orbitofrontal gyrus, subgenual cortex, and insular cortex subregions, from early to late adulthood. Harmonized measures for gray matter (GM) volume and cortical thickness of each region were estimated cross-sectionally for 305 healthy controls (CTLs) and 247 individuals with MDD (MDDs), collated from four research cohorts. We modeled the nonlinear associations of age with GM volume and cortical thickness using generalized additive modeling and tested for age-dependent group differences.

RESULTS

Overall, all investigated regions exhibited smaller GM volume and thinner cortical measures with increasing age. Compared to age matched CTLs, MDDs had thicker cortices and greater GM volume from early adulthood until early middle age (average 35 years), but thinner cortices and smaller GM volume during and after middle age in the lateral orbital gyrus and all insular subregions. Deviations of the MDD and CTL models for both GM volume and cortical thickness in these regions started as early as age 18.

CONCLUSIONS

The analyses revealed that brain morphometry differences between MDDs and CTLs are dependent on age and brain region. The significant age-by-group interactions in the lateral orbital frontal gyrus and insular subregions make these regions potential targets for future longitudinal studies of MDD.

Computational models of cortical folding: A review of common approaches

The process of gyrification, by which the brain develops the intricate pattern of gyral hills and sulcal valleys, is the result of interactions between biological and mechanical processes during brain development. Researchers have developed a vast array of computational models in order to investigate cortical folding. This review aims to summarize these studies, focusing on five essential elements of the brain that affect development and gyrification and how they are represented in computational models: (i) the constraints of skull, meninges, and cerebrospinal fluid; (ii) heterogeneity of cortical layers and regions; (iii) anisotropic behavior of subcortical fiber tracts; (iv) material properties of brain tissue; and (v) the complex geometry of the brain. Finally, we highlight areas of need for future simulations of brain development.

Towards Deciphering the Fetal Foundation of Normal Cognition and Cognitive Symptoms From Sulcation of the Cortex

Growing evidence supports that prenatal processes play an important role for cognitive ability in normal and clinical conditions. In this context, several neuroimaging studies searched for features in postnatal life that could serve as a proxy for earlier developmental events. A very interesting candidate is the sulcal, or sulco-gyral, patterns, macroscopic features of the cortex anatomy related to the fold topology-e.g., continuous vs. interrupted/broken fold, present vs. absent fold-or their spatial organization. Indeed, as opposed to quantitative features of the cortical sheet (e.g., thickness, surface area or curvature) taking decades to reach the levels measured in adult, the qualitative sulcal patterns are mainly determined before birth and stable across the lifespan. The sulcal patterns therefore offer a window on the fetal constraints on specific brain areas on cognitive abilities and clinical symptoms that manifest later in life. After a global review of the cerebral cortex sulcation, its mechanisms, its ontogenesis along with methodological issues on how to measure the sulcal patterns, we present a selection of studies illustrating that analysis of the sulcal patterns can provide information on prenatal dispositions to cognition (with a focus on cognitive control and academic abilities) and cognitive symptoms (with a focus on schizophrenia and bipolar disorders). Finally, perspectives of sulcal studies are discussed.

Pharmacological treatment profiles in the FACE-BD cohort: An unsupervised machine learning study, applied to a nationwide bipolar cohort✰

BACKGROUND

Despite thorough and validated clinical guidelines based on bipolar disorders subtypes, large pharmacological treatment heterogeneity remains in these patients. There is limited knowledge about the different treatment combinations used and their influence on patient outcomes. We attempted to determine profiles of patients based on their treatments and to understand the clinical characteristics associated with these treatment profiles.

METHODS

This multicentre longitudinal study was performed on a French nationwide bipolar cohort database. We performed hierarchical agglomerative clustering to search for clusters of individuals based on their treatments during the first year following inclusion. We then compared patient clinical characteristics according to these clusters.

RESULTS

Four groups were identified among the 1795 included patients: group 1 ("heterogeneous" n = 1099), group 2 ("lithium" n = 265), group 3 ("valproate" n = 268), and group 4 ("lamotrigine" n = 163). Proportion of bipolar 1 disorder, in groups 1 to 4 were: 48.2%, 57.0%, 48.9% and 32.5%. Groups 1 and 4 had greater functional impact at baseline and a less favorable clinical and functioning evolution at one-year follow-up, especially on GAF and FAST scales.

LIMITATIONS

The one-year period used for the analysis of mood stabilizing treatments remains short in the evolution of bipolar disorder.

CONCLUSIONS

Treatment profiles are associated with functional evolution of patients and were not clearly determined by bipolar subtypes. These profiles seem to group together common patient phenotypes. These findings do not seem to be influenced by the duration of disease prior to inclusion and neither by the number of treatments used during the follow-up period.

Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

BACKGROUND

MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood.

METHODS

We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness.

RESULTS

The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing.

CONCLUSIONS

Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.

White Matter Microstructure in Individuals With and At Risk for Bipolar Disorder: Evidence for an Endophenotype From a Voxel-Based Meta-analysis

BACKGROUND

Aberrant white matter (WM) microstructure has been proposed as a mechanism underlying bipolar disorder (BD). Given the strong genetic underpinnings of both WM microstructure and BD, such WM aberrations may be not only a disease marker, but also an endophenotype of BD. If so, they should be observable in individuals at risk for BD (AR) (i.e., first-degree relatives). This meta-analysis integrates evidence on perturbed WM microstructure in individuals with or at risk for BD.

METHODS

A comprehensive search of literature published through April 2020 identified diffusion tensor imaging studies that used a voxel-based approach to compare fractional anisotropy (FA) and radial diffusivity between individuals with BD and/or AR individuals and healthy volunteers. Effect size comparison and conjunction analysis allowed identification of endophenotypes and disease markers of BD. Effects of age, sex, mood state, and psychotropic medication were explored using meta-regressions.

RESULTS

We included 57 studies in individuals with BD (N = 4631) and 10 in AR individuals (N = 753). Both individuals with and at risk for BD were associated with lower FA in the body and splenium of the corpus callosum. In the BD group, decreased FA and increased radial diffusivity comprised the entire corpus callosum, anterior thalamic radiation, fronto-orbito-polar tracts, and superior longitudinal fasciculus, and were influenced by age, sex, and mood state. Studies with higher proportions of individuals taking lithium or antipsychotics reported smaller FA reductions in BD.

CONCLUSIONS

Findings suggest that abnormalities in the body and splenium of the corpus callosum may be an endophenotype for BD, and they associate BD with WM tracts relevant for working memory performance, attention, and reward processing.

Glial and neuronal markers in bipolar disorder: A meta-analysis testing S100B and NSE peripheral blood levels

S100 calcium-binding protein B (S100B) and neuron-specific enolase (NSE) might be peripheral markers reflecting glia and neuronal abnormalities in subjects with bipolar disorder. We carried out a systematic review and meta-analysis, searching for studies indexed in main electronic databases, to clarify whether S100B and NSE blood levels might be increased in bipolar disorder. Eleven studies met eligibility criteria, with data on S100B levels and/or NSE levels in subjects with bipolar disorder and healthy controls, respectively. Random-effects meta-analysis estimated higher levels of S100B in bipolar disorder (standardized mean difference [SMD] = 0.81; p < .001), with some inconsistency across studies (I² = 81.7%). Findings were confirmed by relevant sensitivity analyses. Meta-regression analyses did not estimate any effect for tested covariates. On the other hand, no differences in NSE levels between individuals with bipolar disorder and healthy controls were estimated (SMD = -0.32; p = .374), with high heterogeneity across studies (I² = 89.9%). Meta-regression analyses showed that the effect size was influenced by both mean age (p < .001) and illness duration (p = .001) of subjects with bipolar disorders. Our findings support the hypothesis of a possible role of glial abnormalities in the pathophysiology of bipolar disorder.

Neurodevelopmental pathways in bipolar disorder

Aberrations in neurodevelopmental trajectories have been implicated in the neurobiology of several mental disorders and evidence indicates a pathophysiological and genetic overlap of schizophrenia and bipolar disorder (BD). In this narrative review, we summarize findings related to developmental and perinatal factors as well as epidemiological, clinical, neuropsychological, brain imaging, postmortem brain and genomic studies that provide evidence for a putative neurodevelopmental pathogenesis and etiology of BD. Overall, aberrations in neurodevelopmental pathways have been more consistently implicated in the pathophysiology of schizophrenia compared to BD. Nevertheless, an accumulating body of evidence indicates that dysfunctional neurodevelopmental pathways may be implicated in the underlying pathophysiology of at least a subset of individuals with BD particularly those with an early age of illness onset and those exhibiting psychotic symptoms. A heuristic neurodevelopmental model for the pathophysiology of BD based on the findings of this review is proposed. Furthermore, we critically discuss clinical and research implications of this model. Finally, further research directions for this emerging field are provided.

Strengths and Weaknesses of the Gray Mouse Lemur (Microcebus murinus) as a Model for the Behavioral and Psychological Symptoms and Neuropsychiatric Symptoms of Dementia

To face the load of the prevalence of Alzheimer’s disease in the aging population, there is an urgent need to develop more translatable animal models with similarities to humans in both the symptomatology and physiopathology of dementia. Due to their close evolutionary similarity to humans, non-human primates (NHPs) are of primary interest. Of the NHPs, to date, the gray mouse lemur (Microcebus murinus) has shown promising evidence of its translatability to humans. The present review reports the known advantages and limitations of using this species at all levels of investigation in the context of neuropsychiatric conditions. In this easily bred Malagasy primate with a relatively short life span (approximately 12 years), age-related cognitive decline, amyloid angiopathy, and risk factors (i.e., glucoregulatory imbalance) are congruent with those observed in humans. More specifically, analogous behavioral and psychological symptoms and neuropsychiatric symptoms of dementia (BPSD/NPS) to those in humans can be found in the aging mouse lemur. Aged mouse lemurs show typical age-related alterations of locomotor activity daily rhythms such as decreased rhythm amplitude, increased fragmentation, and increased activity during the resting-sleeping phase of the day and desynchronization with the light-dark cycle. In addition, sleep deprivation successfully induces cognitive deficits in adult mouse lemurs, and the effectiveness of approved cognitive enhancers such as acetylcholinesterase inhibitors or N-methyl-D-aspartate antagonists is demonstrated in sleep–deprived animals. This result supports the translational potential of this animal model, especially for unraveling the mechanisms underlying dementia and for developing novel therapeutics to prevent age-associated cognitive decline. In conclusion, actual knowledge of BPSD/NPS-like symptoms of age-related cognitive deficits in the gray mouse lemur and the recent demonstration of the similarity of these symptoms with those seen in humans offer promising new ways of investigating both the prevention and treatment of pathological aging.

Genetic Predisposition and Disease Expression of Bipolar Disorder Reflected in Shape Changes of the Anterior Limbic Network

Bipolar disorder (BD) is a genetically and phenotypically complex psychiatric disease. Although previous studies have suggested that the relatives of BD patients have an increased risk of experiencing affective disturbances, most relatives who have similar genotypes may not manifest the disorder. We aim to identify the neuroimaging alterations—specifically, the cortical folding structures of the anterior limbic network (ALN)—in BD patients and their siblings, compared to healthy controls. The shared alterations in patients and their siblings may indicate the hereditary predisposition of BD, and the altered cortical structures unique to BD patients may be a probe of BD expression. High-resolution, T1-weighted magnetic resonance images for 17 euthymic patients with BD, 17 unaffected siblings of BD patients, and 22 healthy controls were acquired. We categorized the cortical regions within the ALN into sulcal and gyral areas, based on the shape index, followed by the measurement of the folding degree, using the curvedness. Our results revealed that the changes in cortical folding in the orbitofrontal and temporal regions were associated with a hereditary predisposition to BD. Cortical folding structures in multiple regions of the ALN, particularly in the striatal–thalamic circuit and anterior cingulate cortex, could be used to differentiate BD patients from healthy controls and unaffected siblings. We concluded that the cortical folding structures of ALN can provide potential biomarkers for clinical diagnosis of BD and differentiation from the unaffected siblings.

Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method

BACKGROUND

Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls.

METHODS

In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty.

RESULTS

Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD.

LIMITATIONS

The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected.

CONCLUSIONS

The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.