Paternal Prenatal and Lactation Exposure to a High-Calorie Diet Shapes Transgenerational Brain Macro- and Microstructure Defects, Impacting Anxiety-Like Behavior in Male Offspring Rats.

Prenatal exposure to high-energy diets (HED) increases the susceptibility to behavioral alterations in the male offspring. We addressed whether prenatal HED primes the transgenerational inheritance of structural brain changes impacting anxiety/depression-like behavior in the offspring. For this, we used female Wistar rats exposed to a HED [cafeteria (CAF) diet, n = 6] or chow [control (CON) n = 6] during development. Anxiety and depression-like behavior were evaluated in filial 1 (F1), filial 2 (F2), and filial 3 (F3) male offspring using the open field (OFT), elevated plus maze, novelty suppressed feeding (NSFT), tail suspension (TST), and forced swimming tests. Structural brain changes were identified by deformation-based morphometry (DBM) and diffusion tensor imaging using ex vivo MRI. We found that the F1, F2, and F3 offspring exposed to CAF diet displayed higher anxious scores including longer feeding latency during the NSFT, and in the closed arms, only F1 offspring showed longer stay on edges during the OFT versus control offspring. DBM analysis revealed that CAF offspring exhibited altered volume in the cerebellum, hypothalamus, amygdala, and hippocampus preserved up to the F3 generation of anxious individuals. Also, F3 CAF anxious exhibited greater fractional anisotropy and axial diffusivity (AD) in the amygdala, greater apparent diffusion coefficient in the corpus callosum, and greater AD in the hippocampus with respect to the control. Our results suggest that prenatal and lactation exposure to HED programs the transgenerational inheritance of structural brain changes related to anxiety-like behavior in the male offspring.

The Mexican dataset of a repetitive transcranial magnetic stimulation clinical trial on cocaine use disorder patients: SUDMEX TMS.

Cocaine use disorder (CUD) is a global health problem with severe consequences, leading to behavioral, cognitive, and neurobiological disturbances. While consensus on treatments is still ongoing, repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising approach for medication-resistant disorders, including substance use disorders. In this context, here we present the SUDMEX-TMS, a Mexican dataset from an rTMS clinical trial involving CUD patients. This longitudinal dataset comprises 54 CUD patients (including 8 females) with data collected at five time points: baseline (T0), two weeks (T1), three months (T2), six months (T3) follow-up, and twelve months (T4) follow-up. The clinical rTMS treatment followed a double-blinded randomized clinical trial design (n = 24 sham/30 active) for 2 weeks, followed by an open-label phase. The dataset includes demographic, clinical, and cognitive measures, as well as magnetic resonance imaging (MRI) data collected at all time points, encompassing structural (T1-weighted), functional (resting-state fMRI), and multishell diffusion-weighted (DWI-HARDI) sequences. This dataset offers the opportunity to investigate the impact of rTMS on CUD participants, considering clinical, cognitive, and multimodal MRI metrics in a longitudinal framework.

Prenatal Cafeteria Diet Primes Anxiety-like Behavior Associated to Defects in Volume and Diffusion in the Fimbria-fornix of Mice Offspring.

Prenatal exposure to high-energy diets primes brain alterations that increase the risk of developing behavioral and cognitive failures. Alterations in the structure and connectivity of brain involved in learning and memory performance are found in adult obese murine models and in humans. However, the role of prenatal exposure to high-energy diets in the modulation of the brain's structure and function during cognitive decline remains unknown. We used female C57BL6 mice (n = 10) exposed to a high-energy diets (Cafeteria diet (CAF)) or Chow diet for 9 weeks (before, during and after pregnancy) to characterize their effect on brain structural organization and learning and memory performance in the offspring at two-month-old (n = 17). Memory and learning performance were evaluated using the Y-maze test including forced and spontaneous alternation, novel object recognition (NORT), open field and Barnes maze tests. We found no alterations in the short- or long-time spatial memory performance in male offspring prenatally exposed to CAF diet when compared to the control, but they increased time spent in the edges resembling anxiety-like behavior. By using deformation-based morphometry and diffusion tensor imaging analysis we found that male offspring exposed to CAF diet showed increased volume in primary somatosensory cortex and a reduced volume of fimbria-fornix, which correlate with alterations in its white matter integrity. Biological modeling revealed that prenatal exposure to CAF diet predicts low volume in the fimbria-fornix, which was associated with anxiety in the offspring. The findings suggest that prenatal exposure to high-energy diets prime brain structural alterations related to anxiety in the offspring.

A consensus protocol for functional connectivity analysis in the rat brain.

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Focal electrical stimulation on an alcohol disorder model using magnetic resonance imaging-compatible chronic neural monopolar carbon fiber electrodes.

Neuromodulation interventions, such as Deep Brain Stimulation (DBS) and repeated transcranial magnetic stimulation (rTMS), are proposed as possible new complementary therapies to treat substance use disorders (SUD) such as alcohol use disorder (AUD). It is hypothesized that neuromodulation may induce neural plasticity in the reward and frontostriatal systems via electrical field induction, possibly reducing symptoms. Preclinical self-administration rodent models of AUD may help us gain insight into the effects of neuromodulation therapies on different pathology, as well as the neural mechanisms behind the positive effects. DBS, or any type of brain stimulation using intracranial electrodes in rodents, would benefit from the use of magnetic resonance imaging (MRI) to study the longitudinal effects and mechanisms of stimulation as well as novel targets, as it is a non-invasive technique that allows the analysis of structural and functional changes in the brain. To do this, there is a need for MRI-compatible electrodes that allow for MRI acquisition with minimal distortion of the magnetic field. In this protocol, we present a method for the construction and surgery of chronically implantable monopolar carbon electrodes for use in rats. Unlike conventional electrodes, carbon electrodes are resistant to high temperatures, flexible, and generate fewer artifacts in MRI compared to conventional ones. We validated its use by using a focal electrical stimulation high-frequency (20 Hz) protocol that lasted ∼10 sessions. We propose that this technique can also be used for the research of the neurophysiological bases of the neuromodulatory treatment in other preclinical substance use disorders (SUD) models.

Social closeness modulates brain dynamics during trust anticipation.

Anticipation of trust from someone with high social closeness is expected. However, if there is uncertainty in the interaction because a person is a stranger or because he has distrusted us on another occasion, we need to keep track of his behavior and intentions. Using functional Magnetic Resonance Imaging (fMRI) we wanted to find the brain regions related to trust anticipation from partners who differ in their level of social closeness. We designed an experiment in which 30 participants played an adapted trust game with three trustors: A computer, a stranger, and a real friend. We covertly manipulated their decisions in the game, so they trusted 75% of the trials and distrusted in remaining trials. Using a psychophysiological interaction analysis, we found increases in functional coupling between the anterior insula (AIns) and intra parietal sulcus (IPS) during trust anticipation between a high versus low social closeness partner. Also, the right parietal cortex was coupled with the fusiform gyrus (FG) and the inferior/middle temporal gyrus during trust anticipation of a friend versus a stranger. These results suggest that brain regions involved in encoding the intentions of others are recruited during trust anticipation from a friend compared to a stranger.

Machine learning detects predictors of symptom severity and impulsivity after dialectical behavior therapy skills training group in borderline personality disorder.

Only 50% of the patients with Borderline Personality Disorder (BPD) respond to psychotherapies, such as Dialectical Behavioral Therapy (DBT), this might be increased by identifying baseline predictors of clinical change. We use machine learning to detect clinical features that could predict improvement/worsening for severity and impulsivity of BPD after DBT skills training group. To predict illness severity, we analyzed data from 125 patients with BPD divided into 17 DBT psychotherapy groups, and for impulsiveness we analyzed 89 patients distributed into 12 DBT groups. All patients were evaluated at baseline using widely self-report tests; ∼70% of the sample were randomly selected and two machine learning models (lasso and Random forest [Rf]) were trained using 10-fold cross-validation and compared to predict the post-treatment response. Models' generalization was assessed in ∼30% of the remaining sample. Relevant variables for DBT (i.e. the mindfulness ability "non-judging", or "non-planning" impulsiveness) measured at baseline, were robust predictors of clinical change after six months of weekly DBT sessions. Using 10-fold cross-validation, the Rf model had significantly lower prediction error than lasso for the BPD severity variable, Mean Absolute Error (MAE) lasso - Rf = 1.55 (95% CI, 0.63-2.48) as well as for impulsivity, MAE lasso - Rf = 1.97 (95% CI, 0.57-3.35). According to Rf and the permutations method, 34/613 significant predictors for severity and 17/613 for impulsivity were identified. Using machine learning to identify the most important variables before starting DBT could be fundamental for personalized treatment and disease prognosis.

The Mexican magnetic resonance imaging dataset of patients with cocaine use disorder: SUDMEX CONN.

Cocaine use disorder (CUD) is a substance use disorder (SUD) characterized by compulsion to seek, use and abuse of cocaine, with severe health and economic consequences for the patients, their families and society. Due to the lack of successful treatments and high relapse rate, more research is needed to understand this and other SUD. Here, we present the SUDMEX CONN dataset, a Mexican open dataset of 74 CUD patients (9 female) and matched 64 healthy controls (6 female) that includes demographic, cognitive, clinical, and magnetic resonance imaging (MRI) data. MRI data includes: 1) structural (T1-weighted), 2) multishell high-angular resolution diffusion-weighted (DWI-HARDI) and 3) functional (resting state fMRI) sequences. The repository contains unprocessed MRI data available in brain imaging data structure (BIDS) format with corresponding metadata available at the OpenNeuro data sharing platform. Researchers can pursue brain variability between these groups or use a single group for a larger population sample.

Identifying cognitive deficits in cocaine dependence using standard tests and machine learning.

There is a growing need to address the variability in detecting cognitive deficits with standard tests in cocaine dependence (CD). The aim of the current study was to identify cognitive deficits by means of Machine Learning (ML) algorithms: Generalized Linear Model (Glm), Random forest (Rf) and Elastic Net (GlmNet), to allow more effective categorization of CD and Non-dependent controls (NDC and to address common methodological problems. For our validation, we used two independent datasets, the first consisted of 87 participants (53 CD and 34 NDC) and the second of 40 participants (20 CD and 20 NDC). All participants were evaluated with neuropsychological tests that included 40 variables assessing cognitive domains. Using results from the cognitive evaluation, the three ML algorithms were trained in the first dataset and tested on the second to classify participants into CD and NDC. While the three algorithms had a receiver operating curve (ROC) performance over 50%, the GlmNet was superior in both the training (ROC = 0.71) and testing datasets (ROC = 0.85) compared to Rf and Glm. Furthermore, GlmNet was capable of identifying the eight main predictors of group assignment (CD or NCD) from all the cognitive domains assessed. Specific variables from each cognitive test resulted in robust predictors for accurate classification of new cases, such as those from cognitive flexibility and inhibition domains. These findings provide evidence of the effectiveness of ML as an approach to highlight relevant sections of standard cognitive tests in CD, and for the identification of generalizable cognitive markers.

Borderline Personality Disorder With Cocaine Dependence: Impulsivity, Emotional Dysregulation and Amygdala Functional Connectivity.

Background: Borderline personality disorder is present in 19% of cocaine dependence cases; however, this dual pathology is poorly understood. We wished to characterize the dual pathology and find its functional connectivity correlates to better understand it. Methods: We recruited 69 participants divided into 4 groups: dual pathology (n = 20), cocaine dependence without borderline personality disorder (n = 19), borderline personality without cocaine dependence (n = 10) and healthy controls (n = 20). We used self-reported instruments to measure impulsivity and emotional dysregulation. We acquired resting state fMRI and performed seed-based analyses of the functional connectivity of bilateral amygdala. Results: Borderline personality disorder and cocaine dependence as factors had opposing effects in impulsivity and emotional dysregulation, as well as on functional connectivity between left amygdala and medial prefrontal cortex. On the other hand, in the functional connectivity between right amygdala and left insula, the effect of having both disorders was instead additive, reducing functional connectivity strength. The significant functional connectivity clusters were correlated with impulsivity and emotional dysregulation. Conclusions: In this study, we found that clinical scores of dual pathology patients were closer to those of borderline personality disorder without cocaine dependence than to those of cocaine dependence without borderline personality disorder, while amygdala-medial prefrontal cortex functional connectivity patterns in dual pathology patients were closer to healthy controls than expected.