Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.

Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.

Revisiting the standard for modeling functional brain network activity: Application to consciousness.

Functional connectivity (FC) of resting-state fMRI time series can be estimated using methods that differ in their temporal sensitivity (static vs. dynamic) and the number of regions included in the connectivity estimation (derived from a prior atlas). This paper presents a novel framework for identifying and quantifying resting-state networks using resting-state fMRI recordings.

Neurodevelopmental predictors of treatment response in schizophrenia and bipolar disorder.

Background: Treatment resistance is a major challenge in psychiatric disorders. Early detection of potential future resistance would improve prognosis by reducing the delay to appropriate treatment adjustment and recovery. Here, we sought to determine whether neurodevelopmental markers can predict therapeutic response.

Brain growth until adolescence after a neonatal focal injury: sex related differences beyond lesion effect.

Introduction: Early focal brain injuries lead to long-term disabilities with frequent cognitive impairments, suggesting global dysfunction beyond the lesion. While plasticity of the immature brain promotes better learning, outcome variability across individuals is multifactorial. Males are more vulnerable to early injuries and neurodevelopmental disorders than females, but long-term sex differences in brain growth after an early focal lesion have not been described yet.

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.

Attention-gated 3D CapsNet for robust hippocampal segmentation.

Purpose: The hippocampus is organized in subfields (HSF) involved in learning and memory processes and widely implicated in pathologies at different ages of life, from neonatal hypoxia to temporal lobe epilepsy or Alzheimer's disease. Getting a highly accurate and robust delineation of sub-millimetric regions such as HSF to investigate anatomo-functional hypotheses is a challenge. One of the main difficulties encountered by those methodologies is related to the small size and anatomical variability of HSF, resulting in the scarcity of manual data labeling.

Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach.

Purpose: Post-traumatic stress disorder (PTSD) can affect family members of patients admitted to the intensive care unit (ICU). Easily accessible patient's and relative's information may help develop accurate risk stratification tools to direct relatives at higher risk of PTSD toward appropriate management.

Methods: PTSD was measured 90 days after ICU discharge using validated instruments (Impact of Event Scale and Impact of Event Scale-Revised) in 2374 family members.

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.

Longitudinal MicroRNA Signature of Conversion to Psychosis.

Background And Hypothesis: The emergence of psychosis in ultra-high-risk subjects (UHR) is influenced by gene-environment interactions that rely on epigenetic mechanisms such as microRNAs. However, whether they can be relevant pathophysiological biomarkers of psychosis' onset remains unknown.

Study Design: We present a longitudinal study of microRNA expression, measured in plasma by high-throughput sequencing at baseline and follow-up, in a prospective cohort of 81 UHR, 35 of whom developed psychosis at follow-up (converters).

A fast and robust hippocampal subfields segmentation: HSF revealing lifespan volumetric dynamics.

The hippocampal subfields, pivotal to episodic memory, are distinct both in terms of cyto- and myeloarchitectony. Studying the structure of hippocampal subfields is crucial to understand volumetric trajectories across the lifespan, from the emergence of episodic memory during early childhood to memory impairments found in older adults. However, segmenting hippocampal subfields on conventional MRI sequences is challenging because of their small size.

Enhancing fetal alcohol spectrum disorders diagnosis with a classifier based on the intracerebellar gradient of volumetric undersizing.

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).

Understanding the relationship between cerebellar structure and social abilities.

Background: The cerebellum contains more than 50% of all neurons in the brain and is involved in a broad range of cognitive functions, including social communication and social cognition. Inconsistent atypicalities in the cerebellum have been reported in individuals with autism compared to controls suggesting the limits of categorical case control comparisons. Alternatively, investigating how clinical dimensions are related to neuroanatomical features, in line with the Research Domain Criteria approach, might be more relevant.

Cerebellar Atypicalities in Autism?

Background: The cerebellum contains more than 50% of the brain's neurons and is involved in social cognition. Cerebellar anatomical atypicalities have repeatedly been reported in individuals with autism. However, studies have yielded inconsistent findings, likely because of a lack of statistical power, and did not capture the clinical and neuroanatomical diversity of autism.

OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing.

Prediction of chronological age from neuroimaging in the healthy population is an important issue because the deviations from normal brain age may highlight abnormal trajectories towards brain disorders. As a first step, ML models have emerged to predict chronological age from brain MRI, as a proxy measure of biological age. However, there is currently no consensus w.

Brain anomalies in early psychosis: From secondary to primary psychosis.

Brain anomalies are frequently found in early psychoses. Although they may remain undetected for many years, their interpretation is critical for differential diagnosis. In secondary psychoses, their identification may allow specific management.

Decoding Activity in Broca's Area Predicts the Occurrence of Auditory Hallucinations Across Subjects.

Background: Functional magnetic resonance imaging (fMRI) capture aims at detecting auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing efficient capture methods with low computational cost that easily generalize between patients remains a key objective in precision psychiatry. To address this issue, we developed a novel automatized fMRI-capture procedure for AVHs in patients with schizophrenia (SCZ).

Irritability Is Associated With Decreased Cortical Surface Area and Anxiety With Decreased Gyrification During Brain Development.

Brain development is of utmost importance for the emergence of psychiatric disorders, as the most severe of them arise before 25 years old. However, little is known regarding how early transdiagnostic symptoms, in a dimensional framework, are associated with cortical development. Anxiety and irritability are central vulnerability traits for subsequent mood and anxiety disorders.

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.

Accumulation of Lithium in the Hippocampus of Patients With Bipolar Disorder: A Lithium-7 Magnetic Resonance Imaging Study at 7 Tesla.

Background: Lithium (Li) is a first-line treatment for bipolar disorder (BD). To study its cerebral distribution and association with plasma concentrations, we used Li magnetic resonance imaging at 7T in euthymic patients with BD treated with Li carbonate for at least 2 years.

Methods: Three-dimensional Li magnetic resonance imaging scans (N = 21) were acquired with an ultra-short echo-time sequence using a non-Cartesian k-space sampling scheme.

Neocortical morphometry in Huntington's disease: Indication of the coexistence of abnormal neurodevelopmental and neurodegenerative processes.

Huntington's disease (HD) is an inherited, autosomal dominant disorder that is characteristically thought of as a degenerative disorder. Despite cellular and molecular grounds suggesting HD could also impact normal development, there has been scarce systems-level data obtained from in vivo human studies supporting this hypothesis. Sulcus-specific morphometry analysis may help disentangle the contribution of coexisting neurodegenerative and neurodevelopmental processes, but such an approach has never been used in HD.

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.

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).