Alcohol-induced damage to the fimbria/fornix reduces hippocampal-prefrontal cortex connection during early abstinence.

INTRODUCTION: Alcohol dependence is characterized by a gradual reduction in cognitive control and inflexibility to contingency changes. The neuroadaptations underlying this aberrant behavior are poorly understood. Using an animal model of alcohol use disorders (AUD) and complementing diffusion-weighted (dw)-MRI with quantitative immunohistochemistry and electrophysiological recordings, we provide causal evidence that chronic intermittent alcohol exposure affects the microstructural integrity of the fimbria/fornix, decreasing myelin basic protein content, and reducing the effective communication from the hippocampus (HC) to the prefrontal cortex (PFC). Using a simple quantitative neural network model, we show how disturbed HC-PFC communication may impede the extinction of maladaptive memories, decreasing flexibility. Finally, combining dw-MRI and psychometric data in AUD patients, we discovered an association between the magnitude of microstructural alteration in the fimbria/fornix and the reduction in cognitive flexibility. Overall, these findings highlight the vulnerability of the fimbria/fornix microstructure in AUD and its potential contribution to alcohol pathophysiology. Fimbria vulnerability to alcohol underlies hippocampal-prefrontal cortex dysfunction and correlates with cognitive impairment.

MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models.

Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.

Brain Network Allostasis after Chronic Alcohol Drinking Is Characterized by Functional Dedifferentiation and Narrowing.

Alcohol use disorder (AUD) causes complex alterations in the brain that are poorly understood. The heterogeneity of drinking patterns and the high incidence of comorbid factors compromise mechanistic investigations in AUD patients. Here we used male Marchigian Sardinian alcohol-preferring (msP) rats, a well established animal model of chronic alcohol drinking, and a combination of longitudinal resting-state fMRI and manganese-enhanced MRI to provide objective measurements of brain connectivity and activity, respectively. We found that 1 month of chronic alcohol drinking changed the correlation between resting-state networks. The change was not homogeneous, resulting in the reorganization of pairwise interactions and a shift in the equilibrium of functional connections. We identified two fundamentally different forms of network reorganization. First is functional dedifferentiation, which is defined as a regional increase in neuronal activity and overall correlation, with a concomitant decrease in preferential connectivity between specific networks. Through this mechanism, occipital cortical areas lost their specific interaction with sensory-insular cortex, striatal, and sensorimotor networks. Second is functional narrowing, which is defined as an increase in neuronal activity and preferential connectivity between specific brain networks. Functional narrowing strengthened the interaction between striatal and prefrontocortical networks, involving the anterior insular, cingulate, orbitofrontal, prelimbic, and infralimbic cortices. Importantly, these two types of alterations persisted after alcohol discontinuation, suggesting that dedifferentiation and functional narrowing rendered persistent network states. Our results support the idea that chronic alcohol drinking, albeit at moderate intoxicating levels, induces an allostatic change in the brain functional connectivity that propagates into early abstinence.SIGNIFICANCE STATEMENT Excessive consumption of alcohol is positioned among the top five risk factors for disease and disability. Despite this priority, the transformations that the nervous system undergoes from an alcohol-naive state to a pathologic alcohol drinking are not well understood. In our study, we use an animal model with proven translational validity to study this transformation longitudinally. The results show that shortly after chronic alcohol consumption there is an increase in redundant activity shared by brain structures, and the specific communication shrinks to a set of pathways. This functional dedifferentiation and narrowing are not reversed immediately after alcohol withdrawal but persist during early abstinence. We causally link chronic alcohol drinking with an early and abstinence-persistent retuning of the functional equilibrium of the brain.

Increased network centrality of the anterior insula in early abstinence from alcohol.

Abnormal resting-state functional connectivity, as measured by functional magnetic resonance imaging (MRI), has been reported in alcohol use disorders (AUD), but findings are so far inconsistent. Here, we exploited recent developments in graph-theoretical analyses, enabling improved resolution and fine-grained representation of brain networks, to investigate functional connectivity in 35 recently detoxified alcohol dependent patients versus 34 healthy controls. Specifically, we focused on the modular organization, that is, the presence of tightly connected substructures within a network, and on the identification of brain regions responsible for network integration using an unbiased approach based on a large-scale network composed of more than 600 a priori defined nodes. We found significant reductions in global connectivity and region-specific disruption in the network topology in patients compared with controls. Specifically, the basal brain and the insular-supramarginal cortices, which form tightly coupled modules in healthy subjects, were fragmented in patients. Further, patients showed a strong increase in the centrality of the anterior insula, which exhibited stronger connectivity to distal cortical regions and weaker connectivity to the posterior insula. Anterior insula centrality, a measure of the integrative role of a region, was significantly associated with increased risk of relapse. Exploratory analysis suggests partial recovery of modular structure and insular connectivity in patients after 2 weeks. These findings support the hypothesis that, at least during the early stages of abstinence, the anterior insula may drive exaggerated integration of interoceptive states in AUD patients with possible consequences for decision making and emotional states and that functional connectivity is dynamically changing during treatment.

Neuroimaging reveals functionally distinct neuronal networks associated with high-level alcohol consumption in two genetic rat models.

Human imaging data suggest that the motivational processes associated with alcohol reward are reflected in the patterns of neural activation after alcohol or alcohol-related cues. In animal models of alcohol drinking, however, the changes in brain activation during voluntary alcohol ingestion are poorly known. In order to improve the translational utility of animal models, we examined alcohol-induced functional brain activation in Alko Alcohol (AA) and Marchigian-Sardinian alcohol-preferring (msP) rats that drink voluntarily high levels of alcohol, but exhibit widely different neurochemical and behavioral traits cosegregated with alcohol preference. Brain imaging was performed using manganese-enhanced MRI (MEMRI), which is based on accumulation of Mn2+ ions in activated neurons, allowing the identification of functional neuronal networks recruited during specific behaviors in awake animals during a subsequent imaging session under anesthesia. MEMRI was performed following 4 weeks of voluntary alcohol drinking, using water drinking as the control. Despite similar levels of alcohol drinking, strikingly different alcohol-induced neuronal activity patterns were observed in AA and msP rats. Overall, functional activation in the AA rats was more widespread, involving large cortical areas and subcortical structures, such as the bed nucleus of the stria terminalis, preoptic area, hypothalamus, periaqueductal grey, and substantia nigra. In the msP rats, however, alcohol-related activation was largely confined to prefrontal cortical regions and insular cortex, and olfactory areas. Overlapping areas of activation found in both rat lines included the nucleus accumbens, prelimbic, orbital, and insular cortex. In conclusion, our data reveal strikingly different brain circuits associated with alcohol drinking in two genetically different rat lines and suggest innately different motivational and behavioral processes driving alcohol drinking. These findings have important implications for the use of these lines in translational alcohol research.

Incubation of neural alcohol cue reactivity after withdrawal and its blockade by naltrexone.

During the first weeks of abstinence, alcohol craving in patients may increase or "incubate." We hypothesize that Naltrexone (NTX) blocks this incubation effect. Here, we compared NTX effects on neural alcohol cue reactivity (CR) over the first weeks of abstinence and on long-term clinical outcomes to standard treatment. Male alcohol-dependent patients (n = 55) and healthy controls (n = 35) were enrolled. Participants underwent baseline psychometric testing and functional magnetic resonance imaging (fMRI) assessment of mesolimbic alcohol CR. Patients participated in a standard treatment program with the option of adjuvant NTX. They received another scan after 2 weeks of treatment. We found higher CR in several brain regions in patients versus healthy controls. CR significantly increased over 2 weeks in the standard treatment group (n = 13) but not in the NTX group (n = 22). NTX significantly attenuated CR in the left putamen and reduced relapse risk to heavy drinking within 3 months of treatment. Additionally, increased CR in the left putamen and its course over time predicted both NTX response and relapse risk. Carrier status for the functional OPRM1 variant rs1799971:A > G was considered but had no effect on NTX efficacy. In conclusion, NTX was most effective in patients with high CR in the left putamen. While the results from our naturalistic study await further confirmation from prospective randomized trials, they support a potential role of neural CR as a biomarker in the development of precision medicine approaches with NTX.

Link-Level Functional Connectivity Neuroalterations in Autism Spectrum Disorder: A Developmental Resting-State fMRI Study.

Autism spectrum disorder (ASD) is a neurological and developmental disorder whose late diagnosis is based on subjective tests. In seeking for earlier diagnosis, we aimed to find objective biomarkers via analysis of resting-state functional MRI (rs-fMRI) images obtained from the Autism Brain Image Data Exchange (ABIDE) database. Thus, we estimated brain functional connectivity (FC) between pairs of regions as the statistical dependence between their neural-related blood-oxygen-level-dependent (BOLD) signals. We compared FC of individuals with ASD and healthy controls, matched by age and intelligence quotient (IQ), and split into three age groups (50 children, 98 adolescents, and 32 adults), from a developmental perspective. After estimating the correlation, we observed hypoconnectivities in children and adolescents with ASD between regions belonging to the default mode network (DMN). Concretely, in children, FC decreased between the left middle temporal gyrus and right frontal pole (p = 0.0080), and between the left orbitofrontal cortex and right superior frontal gyrus (p = 0.0144). In adolescents, this decrease was observed between bilateral postcentral gyri (p = 0.0012), and between the right precuneus and right middle temporal gyrus (p = 0.0236). These results help to gain a better understanding of the involved regions on autism and its connection with the affected superior cognitive brain functions.

Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory.

The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read 'Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to as "fu'. The correct version states 'referred to as "functional connectivity"2-6' in place of 'referred to as "fu'. This has been corrected in both the PDF and HTML versions of the Article.

Finding influential nodes for integration in brain networks using optimal percolation theory.

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.

Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic.

The use of functional magnetic resonance imaging (fMRI) to measure spontaneous fluctuations in blood oxygen level dependent (BOLD) signals has become an indispensable tool to investigate how brain regions interact and form long-range networks. Statistical dependency measures between brain regions obtained from BOLD signals can inform about brain functional states in longitudinal studies of neurological and psychiatric disorders. Furthermore, its non-invasive nature allows comparable measurements in clinical and animal studies, providing excellent translational capabilities. In the present study, we apply Network-Based Statistic (NBS) to investigate alterations in the functional connectivity (FC) of the rat brain in a post-dependent (PD) state, an established animal model of clinical relevant features in alcoholism. In contrast to mass-univariate tests, in which comparisons are performed at single link-level, NBS enhances the statistical power by assuming that the connections comprising the effect of interest are interconnected. Brain-wide resting-state fMRI signals were collected in 14 controls and 13 PD rats, and Pearson correlations computed between 47 brain regions of interest (ROIs). The NBS analysis revealed statistically significant differences in a connected network of structures including hippocampus, amygdala, lateral hypothalamus and the raphe nucleus, all regions with known relevance for addictive behaviors. In contrast, no individual connection could be found significant by univariate comparisons with false discovery rate (FDR) correction. Correlations between the structures in the identified subnetwork tend to decrease or become negative (anti-correlated) in the PD state compared to controls. We interpret this result as evidence for a disconnected subnetwork in the PD state.

Brain functional connectivity alterations in a rat model of excessive alcohol drinking: A resting-state network analysis.

Alcohol use disorders (AUD) are a major public health concern. Understanding the brain network alterations is of the utmost importance to diagnose and develop treatment strategies. Employing resting-state functional magnetic resonance imaging, we have performed a longitudinal study in a rat model of chronic excessive alcohol consumption, to identify functional alterations in brain networks triggered by alcohol drinking. Two time points were considered: 1) before alcohol consumption (control condition) and 2) after 30 days of alcohol drinking (alcohol condition). We first identified nine resting-state networks with group independent component analysis. Afterwards, dual regression was applied to obtain subject-specific time courses and spatial maps. L2-regularized partial correlation analysis between pairs of networks showed that functional connectivity (FC) between the retrosplenial-visual and striatal networks decreases due to alcohol consumption, whereas FC between the prefrontal-cingulate and striatal networks increases. Analysis of subject-specific spatial maps revealed FC decreases within networks after alcohol drinking, including the striatal, motor-parietal, prefrontal-cingulate, retrosplenial-visual and left motor-parietal networks. Overall, our results unveil a generalized decrease in brain FC induced by alcohol drinking in genetically predisposed animals, even after a relatively short period of exposure (1 month). The only exception to this hypo-connectivity state is the functional association between the striatal and prefrontal-cingulate networks, which increases after drinking, supporting evidence in human alcoholics.

Brain metastases detection on MR by means of three-dimensional tumor-appearance template matching.

PURPOSE: To develop and evaluate a method for an automatic detection of brain metastases in MR images. MATERIALS AND METHODS: Nineteen patients were scanned using a 1.5 Tesla MR scanner. Two radiologists and a radiation oncologist marked the location of the brain metastases. The training group consisted of eight patients harboring 20 metastases. First, three-dimensional (3D) tumor-appearance templates were cross-correlated with MR brain images to evaluate their similarity, and a correlation threshold was established for metastasis candidates. Afterward, a method to reduce false positive rate (FPR) was applied: each detected object was segmented and its degree of anisotropy (DA) was obtained, removing the elongated structures with a DA above the optimal value from the receiver operating characteristic curve. Finally, the method was statistically validated in two groups: 11 patients with 42 brain metastases and 11 patients without metastases. RESULTS: The method led to a sensitivity of 80% and an FPR per slice of 0.023 and 2.75 per patient in the training group. In the first validation group, a sensitivity of 88.10% and an FPR per slice of 0.05 corresponding to 6.91 false positives per patient were obtained. DA implementation decreased 3.5 times FPR compared with templates alone. It improved the radiologist's performance in metastases less than 10 mm from 89-93% to 100%. In the second validation group the FPR was 0.04 per slice and 5.18 per patient. CONCLUSION: This method demonstrates that 3D template matching applying DA technique has high sensitivity and low FPR for detecting brain metastases in MR images. J. Magn. Reson. Imaging 2016;44:642-652.

Agreement in Metastatic Spinal Cord Compression.

BACKGROUND: Metastatic epidural spinal cord compression (ESCC) is a devastating medical emergency. The purpose of this study was to determine the reliability of the 6-point ESCC scoring system and the identification of the spinal level presenting ESCC. METHODS: Clinical data and imaging from 90 patients with biopsy-proven spinal metastases were provided to 83 specialists from 44 hospitals. The spinal levels presenting metastases and the ESCC scores for each case were calculated twice by each clinician, with a minimum of 6 weeks' interval. Clinicians were blinded to assessments made by other specialists and their own previous assessment. Fleiss kappa (κ) statistic was used to assess intraobserver and interobserver agreement. Subgroup analyses were performed according to clinicians' specialty (medical oncology, neurosurgery, radiology, orthopedic surgery, and radiation oncology), years of experience, and type of hospital. RESULTS: Intraobserver and interobserver agreement on the location of ESCC was substantial (κ>0.61). Intraobserver agreement on the ESCC score was "excellent" (κ=0.82), whereas interobserver agreement was substantial (κ=0.64). Overall agreement with the tumor board classification was substantial (κ=0.71). Results were similar across specialties, years of experience and hospital category. CONCLUSIONS: The ESCC score can help improve communication among clinicians involved in oncology care.

Spine Instability Neoplastic Score: agreement across different medical and surgical specialties.

BACKGROUND CONTEXT: Spinal instability is an acknowledged complication of spinal metastases; in spite of recent suggested criteria, it is not clearly defined in the literature. PURPOSE: This study aimed to assess intra and interobserver agreement when using the Spine Instability Neoplastic Score (SINS) by all physicians involved in its management. STUDY DESIGN: Independent multicenter reliability study for the recently created SINS, undertaken with a panel of medical oncologists, neurosurgeons, radiologists, orthopedic surgeons, and radiation oncologists, was carried out. PATIENT SAMPLE: Ninety patients with biopsy-proven spinal metastases and magnetic resonance imaging, reviewed at the multidisciplinary tumor board of our institution, were included. OUTCOME MEASURES: Intraclass correlation coefficient (ICC) was used for SINS score agreement. Fleiss kappa statistic was used to assess agreement on the location of the most affected vertebral level; agreement on the SINS category ("stable," "potentially stable," or "unstable"); and overall agreement with the classification established by tumor board. METHODS: Clinical data and imaging were provided to 83 specialists in 44 hospitals across 14 Spanish regions. No assessment criteria were pre-established. Each clinician assessed the SINS score twice, with a minimum 6-week interval. Clinicians were blinded to assessments made by other specialists and to their own previous assessment. Subgroup analyses were performed by clinicians' specialty, experience (≤7, 8-13, ≥14 years), and hospital category (four levels according to size and complexity). This study was supported by Kovacs Foundation. RESULTS: Intra and interobserver agreement on the location of the most affected levels was "almost perfect" (κ>0.94). Intra-observer agreement on the SINS score was "excellent" (ICC=0.77), whereas interobserver agreement was "moderate" (ICC=0.55). Intra-observer agreement in SINS category was "substantial" (k=0.61), whereas interobserver agreement was "moderate" (k=0.42). Overall agreement with the tumor board classification was "substantial" (κ=0.61). Results were similar across specialties, years of experience, and hospital category. CONCLUSIONS: Agreement on the assessment of metastatic spine instability is moderate. The SINS can help improve communication among clinicians in oncology care.

Agreement in the assessment of metastatic spine disease using scoring systems.

PURPOSE: To assess variability in the use of Tomita and modified Bauer scores in spine metastases. MATERIALS AND METHODS: Clinical data and imaging from 90 patients with biopsy-proven spinal metastases, were provided to 83 specialists from 44 hospitals. Spinal levels involved and the Tomita and modified Bauer scores for each case were determined twice by each clinician, with a minimum of 6-week interval. Clinicians were blinded to every evaluation. Kappa statistic was used to assess intra and inter-observer agreement. Subgroup analyses were performed according to clinicians' specialty (medical oncology, neurosurgery, radiology, orthopedic surgery and radiation oncology), years of experience (⩽7, 8-13, ⩾14), and type of hospital (four levels). RESULTS: For metastases identification, intra-observer agreement was "substantial" (0.600.80) at the other levels. Inter-observer agreement was "almost perfect" at lumbar spine, and "substantial" at the other levels. Intra-observer agreement for the Tomita and Bauer scores was almost perfect. Inter-observer agreement was almost perfect for the Tomita score and substantial for the Bauer one. Results were similar across specialties, years of experience and type of hospital. CONCLUSION: Agreement in the assessment of metastatic spine disease is high. These scoring systems can improve communication among clinicians involved in oncology care.

Micro-computed tomography image-based evaluation of 3D anisotropy degree of polymer scaffolds.

Anisotropy is one of the most meaningful determinants of biomechanical behaviour. This study employs micro-computed tomography (µCT) and image techniques for analysing the anisotropy of regenerative medicine polymer scaffolds. For this purpose, three three-dimensional anisotropy evaluation image methods were used: ellipsoid of inertia (EI), mean intercept length (MIL) and tensor scale (t-scale). These were applied to three patterns (a sphere, a cube and a right prism) and to two polymer scaffold topologies (cylindrical orthogonal pore mesh and spherical pores). For the patterns, the three methods provided good results. Regarding the scaffolds, EI mistook both topologies (0.0158, [-0.5683; 0.6001]; mean difference and 95% confidence interval), and MIL showed no significant differences (0.3509, [0.0656; 0.6362]). T-scale is the preferable method because it gave the best capability (0.3441, [0.1779; 0.5102]) to differentiate both topologies. This methodology results in the development of non-destructive tools to engineer biomimetic scaffolds, incorporating anisotropy as a fundamental property to be mimicked from the original tissue and permitting its assessment by means of µCT image analysis.

Computer-aided detection of brain metastases using a three-dimensional template-based matching algorithm.

The purpose of this work was to develop an algorithm for detecting brain metastases in magnetic resonance imaging (MRI), emphasizing the reduction of false positives. Firstly, three-dimensional templates were cross-correlated with the brain volume. Afterwards, each lesion candidate was segmented in the three orthogonal views as a previous step to remove elongated structures such as blood vessels. In a database containing 19 patients and 62 brain metastases, detection algorithm showed a sensitivity of 93.55%. After applying the method for false positive reduction, encouraging results were obtained: false positive rate per slice decreased from 0.64 to 0.15 and only one metastasis was removed, leading to a sensitivity of 91.94%.