Brain Functional Connectivity Analysis Research Articles

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

The Involvement of the Ventral Tegmental Area in the Electroacupuncture Alleviation of Anxiety-Like Behaviors Induced by Chronic Restraint Stress in Mice.

Emotional stress is a significant environmental risk factor for various mental health disabilities, such as anxiety. Electroacupuncture (EA) has been demonstrated to have pronounced anxiolytic effects. However, the neural mechanisms underlying these effects and their contribution to behavioral deficits remain poorly understood. Here, we addressed these issues using a classical mouse anxiety model induced by chronic restraint stress (CRS).Anxiety-like behaviors were evaluated with the open field test and elevated plus maze. Neuronal activation in various brain regions was marked using c-Fos, followed by calculations of interregional correlation to characterize a network that became functionally active following EA at the HT7 acupoint (EA-HT7). We selected the hub regions and further investigated their functions and connections in regulating anxiety-like behaviors by using a combination of chemogenetic manipulations and behavioral testing. CRS exposure induced anxiety-like behaviors. Interestingly, EA-HT7 mitigated these behavioral abnormalities. The c-Fos expression in 30 brain areas revealed a vital brain network for acupuncture responsiveness in naïve mice. Neural activity in the NAcSh (nucleus accumbens shell), BNST (bed nucleus of the stria terminalis), VMH (Ventromedial Hypothalamus), ARC (arcuate nucleus), dDG (dorsal dentate gyrus), and VTA (ventral tegmental area) was significantly altered following acupuncture. Notably, both c-Fos immunostaining and brain functional connectivity analysis revealed the significant activation of VTA following EA-HT7. Interestingly, blocking the VTA eliminated the anxiolytic effects of EA-HT7, whereas chemogenetic activation of the VTA replicated the therapeutic effects of EA-HT7. EA-HT7 has demonstrated benefits in treating anxiety and enhances brain functional connectivity. The VTA is functionally associated with the anxiolytic effects of EA-HT7.

Contrastive voxel clustering for multiscale modeling of brain network

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Distinct roles of excitatory and inhibitory neurons in the medial prefrontal cortex in the expression and reconsolidation of methamphetamine-associated memory in male mice.

Methamphetamine, a commonly abused drug, is known for its high relapse rate. The persistence of addictive memories associated with methamphetamine poses a significant challenge in preventing relapse. Memory retrieval and subsequent reconsolidation provide an opportunity to disrupt addictive memories. However, the key node in the brain network involved in methamphetamine-associated memory retrieval has not been clearly defined. In this study, using the conditioned place preference in male mice, whole brain c-FOS mapping and functional connectivity analysis, together with chemogenetic manipulations of neural circuits, we identified the medial prefrontal cortex (mPFC) as a critical hub that integrates inputs from the retrosplenial cortex and the ventral tegmental area to support both the expression and reconsolidation of methamphetamine-associated memory during its retrieval. Surprisingly, with further cell-type specific analysis and manipulation, we also observed that methamphetamine-associated memory retrieval activated inhibitory neurons in the mPFC to facilitate memory reconsolidation, while suppressing excitatory neurons to aid memory expression. These findings provide novel insights into the neural circuits and cellular mechanisms involved in the retrieval process of addictive memories. They suggest that targeting the balance between excitation and inhibition in the mPFC during memory retrieval could be a promising treatment strategy to prevent relapse in methamphetamine addiction.

Resting-state amygdala subregion and precuneus connectivity provide evidence for a dimensional approach to studying social anxiety disorder

Social anxiety disorder (SAD) is a prevalent and disabling mental health condition, characterized by excessive fear and anxiety in social situations. Resting-state functional magnetic resonance imaging (fMRI) paradigms have been increasingly used to understand the neurobiological underpinnings of SAD in the absence of threat-related stimuli. Previous studies have primarily focused on the role of the amygdala in SAD. However, the amygdala consists of functionally and structurally distinct subregions, and recent studies have highlighted the importance of investigating the role of these subregions independently. Using multiband fMRI, we analyzed resting-state data from 135 participants (42 SAD, 93 healthy controls). By employing voxel-wise permutation testing, we examined group differences of fMRI connectivity and associations between fMRI connectivity and social anxiety symptoms to further investigate the classification of SAD as a categorical or dimensional construct. Seed-to-whole brain functional connectivity analysis using multiple ‘seeds’ including the amygdala and its subregions and the precuneus, revealed no statistically significant group differences. However, social anxiety severity was significantly negatively correlated with functional connectivity of the precuneus - perigenual anterior cingulate cortex and positively correlated with functional connectivity of the amygdala (specifically the superficial subregion) - parietal/cerebellar areas. Our findings demonstrate clear links between symptomatology and brain connectivity in the absence of diagnostic differences, with evidence of amygdala subregion-specific alterations. The observed brain-symptom associations did not include disturbances in the brain’s fear circuitry (i.e., disturbances in connectivity between amygdala - prefrontal regions) likely due to the absence of threat-related stimuli.

PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user’s intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

Transfer Learning Enabled Imagined Speech Interpretation Using Phase-Based Brain Functional Connectivity and Power Analysis

Transfer Learning Enabled Imagined Speech Interpretation Using Phase-Based Brain Functional Connectivity and Power Analysis

An Explainable Unified Framework of Spatio-Temporal Coupling Learning with Application to Dynamic Brain Functional Connectivity Analysis.

Time-series data such as fMRI and MEG carry a wealth of inherent spatio-temporal coupling relationship, and their modeling via deep learning is essential for uncovering biological mechanisms. However, current machine learning models for mining spatio-temporal information usually overlook this intrinsic coupling association, in addition to poor explainability. In this paper, we present an explainable learning framework for spatio-temporal coupling. Specifically, this framework constructs a deep learning network based on spatio-temporal correlation, which can well integrate the time-varying coupled relationships between node representation and inter-node connectivity. Furthermore, it explores spatio-temporal evolution at each time step, providing a better explainability of the analysis results. Finally, we apply the proposed framework to brain dynamic functional connectivity (dFC) analysis. Experimental results demonstrate that it can effectively capture the variations in dFC during brain development and the evolution of spatio-temporal information at the resting state. Two distinct developmental functional connectivity (FC) patterns are identified. Specifically, the connectivity among regions related to emotional regulation decreases, while the connectivity associated with cognitive activities increases. In addition, children and young adults display notable cyclic fluctuations in resting-state brain dFC.

Features of the speech processing network in post- and prelingually deaf cochlear implant users.

The onset of hearing loss can lead to altered brain structure and functions. However, hearing restoration may also result in distinct cortical reorganization. A differential pattern of functional remodeling was observed between post- and prelingual cochlear implant users, but it remains unclear how these speech processing networks are reorganized after cochlear implantation. To explore the impact of language acquisition and hearing restoration on speech perception in cochlear implant users, we conducted assessments of brain activation, functional connectivity, and graph theory-based analysis using functional near-infrared spectroscopy. We examined the effects of speech-in-noise stimuli on three groups: postlingual cochlear implant users (n = 12), prelingual cochlear implant users (n = 10), and age-matched individuals with hearing controls (HC) (n = 22). The activation of auditory-related areas in cochlear implant users showed a lower response compared with the HC group. Wernicke's area and Broca's area demonstrated differences network attributes in speech processing networks in post- and prelingual cochlear implant users. In addition, cochlear implant users maintain a high efficiency of the speech processing network to process speech information. Taken together, our results characterize the speech processing networks, in varying noise environments, in post- and prelingual cochlear implant users and provide new insights for theories of how implantation modes impact remodeling of the speech processing functional networks.

Regression models for partially localized fMRI connectivity analyses.

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated. In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches. We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information. While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Application of brain functional connectivity and nonlinear dynamic analysis in brain function assessment for infants with controlled infantile spasm

To investigate the role of brain functional connectivity and nonlinear dynamic analysis in brain function assessment for infants with controlled infantile spasm (IS). A retrospective analysis was performed on 14 children with controlled IS (IS group) who were admitted to the Department of Neurology, Anhui Provincial Children's Hospital, from January 2019 to January 2023. Twelve healthy children, matched for sex and age, were enrolled as the control group. Electroencephalogram (EEG) data were analyzed for both groups to compare the features of brain network, and nonlinear dynamic indicators were calculated, including approximate entropy, sample entropy, permutation entropy, and permutation Lempel-Ziv complexity. Brain functional connectivity showed that compared with the control group, the IS group had an increase in the strength of functional connectivity, and there was a significant difference between the two groups in the connection strength between the Fp2 and F8 channels (P<0.05). The network stability analysis showed that the IS group had a significantly higher network stability than the control group at different time windows (P<0.05). The nonlinear dynamic analysis showed that compared with the control group, the IS group had a significantly lower sample entropy of Fz electrode (P<0.05). Abnormalities in brain network and sample entropy may be observed in some children with controlled IS, and it is suggested that quantitative EEG analysis parameters can serve as neurological biomarkers for evaluating brain function in children with IS.

Enhanced lower-limb motor imagery by kinesthetic illusion.

Brain-computer interface (BCI) based on lower-limb motor imagery (LMI) enables hemiplegic patients to stand and walk independently. However, LMI ability is usually poor for BCI-illiterate (e.g., some stroke patients), limiting BCI performance. This study proposed a novel LMI-BCI paradigm with kinesthetic illusion(KI) induced by vibratory stimulation on Achilles tendon to enhance LMI ability. Sixteen healthy subjects were recruited to carry out two research contents: (1) To verify the feasibility of induced KI by vibrating Achilles tendon and analyze the EEG features produced by KI, research 1 compared the subjective feeling and brain activity of participants during rest task with and without vibratory stimulation (V-rest, rest). (2) Research 2 compared the LMI-BCI performance with and without KI (KI-LMI, no-LMI) to explore whether KI enhances LMI ability. The analysis methods of both experiments included classification accuracy (V-rest vs. rest, no-LMI vs. rest, KI-LMI vs. rest, KI-LMI vs. V-rest), time-domain features, oral questionnaire, statistic analysis and brain functional connectivity analysis. Research 1 verified that induced KI by vibrating Achilles tendon might be feasible, and provided a theoretical basis for applying KI to LMI-BCI paradigm, evidenced by oral questionnaire (Q1) and the independent effect of vibratory stimulation during rest task. The results of research 2 that KI enhanced mesial cortex activation and induced more intensive EEG features, evidenced by ERD power, topographical distribution, oral questionnaire (Q2 and Q3), and brain functional connectivity map. Additionally, the KI increased the offline accuracy of no-LMI/rest task by 6.88 to 82.19% (p < 0.001). The simulated online accuracy was also improved for most subjects (average accuracy for all subjects: 77.23% > 75.31%, and average F1_score for all subjects: 76.4% > 74.3%). The LMI-BCI paradigm of this study provides a novel approach to enhance LMI ability and accelerates the practical applications of the LMI-BCI system.

Altered amygdala functional connectivity after real-time functional MRI emotion self-regulation training.

Real-time functional MRI neurofeedback (rtfMRI-NF) is a noninvasive technique that extracts concurrent brain states and provides feedback to subjects in an online method. Our study aims to investigate the effect of rtfMRI-NF on amygdala-based emotion self-regulation by analyzing resting-state functional connectivity. We conducted a task experiment to train subjects in self-regulating amygdala activity in response to emotional stimuli. Twenty subjects were divided into two groups. The up-regulate group (URG) viewed positive stimulus, while the down-regulate group (DRG) viewed negative stimulus. The rtfMRI-NF experiment paradigm consisted of three conditions. The URG's percent amplitude fluctuation (PerAF) scores are significant, indicating that positive emotions may be a partial side effect, with increased activity in the left hemisphere. Resting-state functional connectivity was analyzed via a paired-sample t-test before and after neurofeedback training. Brain network properties and functional connectivity analysis showed a significant difference between the default mode network (DMN) and the brain region associated with the limbic system. These results reveal to some extent the mechanism of neurofeedback training to improve individuals' emotional regulate regulation ability. Our study has shown that rtfMRI-neurofeedback training can effectively enhance the ability to voluntarily control brain responses. Furthermore, the results of the functional analysis have revealed distinct changes in the amygdala functional connectivity circuits following rtfMRI-neurofeedback training. These findings may suggest the potential clinical applications of rtfMRI-neurofeedback as a new therapy for emotionally related mental disorders.

EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy

Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body’s immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.

A Comparative Study of Correlation Methods in Functional Connectivity Analysis Using fMRI Data of Alzheimer's Patients.

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging tool, used in brain function research and is also a low-frequency signal, showing brain activation by means of Oxygen consumption. One of the reliable methods in brain functional connectivity analysis is the correlation method. In correlation analysis, the relationship between two time-series has been investigated. In fMRI analysis, the Pearson correlation is used while there are other methods. This study aims to investigate the different correlation methods in functional connectivity analysis. In this analytical research, based on fMRI signals of Alzheimer's Disease (AD) and healthy individuals from the ADNI database, brain functional networks were generated using correlation techniques, including Pearson, Kendall, and Spearman. Then, the global and nodal measures were calculated in the whole brain and in the most important resting-state network called Default Mode Network (DMN). The statistical analysis was performed using non-parametric permutation test. Results show that although in nodal analysis, the performance of correlation methods was almost similar, in global features, the Spearman and Kendall were better in distinguishing AD subjects. Note that, nodal analysis reveals that the functional connectivity of the posterior areas in the brain was more damaged because of AD in comparison to frontal areas. Moreover, the functional connectivity of the dominant hemisphere was disrupted more. Although the Pearson method has limitations in capturing non-linear relationships, it is the most prevalent method. To have a comprehensive analysis, investigating non-linear methods such as distance correlation is recommended.

Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach

BackgroundFunctional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption. One of the main challenges in the analysis of fNIRS signals is the signal degradation due to the interference from noise and artifacts from multiple sources. MethodsIn this context, this research aims to analyze the connectivity between different regions of the brain using graph theory and hence the geometrical association of brain networks in terms of functional parameters. In this study, the impact of two noise removal processes (CBSI and TDDR), along with two types of correlation fNIRS such as Pearson's Correlation (PC), and Cross Correlation (CC) and various whole-brain network architectures on the reproducibility of graph measurements for individual participants has been carefully examined for different densities ranging from 5% to 50%.The graph measures' repeatability at the individual level was studied using the test-retest variability (TRT) ResultsThe test-retest variability for global measurements in binary networks was substantially large at low densities, regardless of the noise removal method or the kind of correlation. Very low test –reset values are observed for weighted networks and great reproducibility for measures of the entire graph. When comparing the test-retest values for various methods, the kind of correlation, the absolute value of the correlation, and the weight calculation method on the raw correlation value all had significant major effects. ConclusionBased on a weighted network with the absolute cross correlation functioning as the weight, this study revealed that normalized global graph measurements were reliable. The node definition techniques that were utilized to remove noise were not essential for the normalized graph measures to be reproducible.

Alterations in regional homogeneity and multiple frequency amplitudes of low-frequency fluctuation in patients with new daily persistent headache: a resting-state functional magnetic resonance imaging study

BackgroundNew daily persistent headache (NPDH) is a rare primary headache that is highly disabling. The pathophysiology of NDPH is still unclear, and we aimed to reveal the underlying mechanism of NDPH through functional magnetic resonance imaging (fMRI) analysis.MethodsIn this cross-sectional study, thirty patients with NDPH and 30 healthy controls (HCs) were recruited. The blood oxygen level-dependent (BOLD) sequences of all participants were obtained using the GE 3.0 T system. We performed ReHo, ALFF (conventional band: 0.01–0.08 Hz, slow-5: 0.01–0.027 Hz, slow-4: 0.027–0.073 Hz) and seed-based to the whole brain functional connectivity (FC) analysis in the NDPH and HC groups. The sex difference analysis of ReHo, ALFF, and FC values was conducted in the NDPH group. We also conducted Pearson’s correlation analysis between ReHo, ALFF, FC values and clinical characteristics (pain intensity, disease duration, HIT-6, GAD-7, PHQ-9, and PSQI scores).ResultsBoth increased ReHo (PFWE-corr = 0.012) and ALFF values (0.01–0.08 Hz, PFWE-corr = 0.009; 0.027–0.073 Hz, PFWE-corr =0.044) of the left middle occipital gyrus (MOG_L) were found in the NDPH group compared to the HC group. There was no significant difference in FC maps between the two groups. Compared to the HC group, no difference was found in ReHo (p = 0.284), ALFF (p = 0.246), and FC (p = 0.118) z scores of the MOG_L in the NDPH group. There was also no sex difference in ReHo (p = 0.288), ALFF (p = 0.859), or FC z score (p = 0.118) of the MOG_L in patients with NDPH. There was no correlation between ReHo, ALFF, FC z scores and clinical characteristics after Bonferroni correction (p < 0.05/18).ConclusionsPatients with NDPH may have abnormal activation of the visual system. Abnormal visual activation may occur mainly in higher frequency band of the classical band. No sex differences in brain activity were found in patients with NDPH.

Distinct brain networks for remote episodic memory depending on content and emotional experience

Memories of life episodes are the heart of individual stories. However, modelling episodic memory is a major challenge in both humans and animals when considering all its characteristics. As a consequence, the mechanisms that underlie the storage of old nontraumatic episodic memories remain enigmatic. Here, using a new task in rodents that models human episodic memory including odour/place/context components and applying advances behavioural and computational analyses, we show that rats form and recollect integrated remote episodic memories of two occasionally encountered complex episodes occurring in their daily life. Similar to humans, the information content and accuracy of memories vary across individuals and depend on the emotional relationship with odours experienced during the very first episode. We used cellular brain imaging and functional connectivity analyses, to find out the engrams of remote episodic memories for the first time. Activated brain networks completely reflect the nature and content of episodic memories, with a larger cortico-hippocampal network when the recollection is complete and with an emotional brain network related to odours that is critical in maintaining accurate and vivid memories. The engrams of remote episodic memories remain highly dynamic since synaptic plasticity processes occur during recall related to memory updates and reinforcement.

Effects of Chronic Caffeine Consumption on Synaptic Function, Metabolism and Adenosine Modulation in Different Brain Areas.

Adenosine receptors mainly control synaptic function, and excessive activation of adenosine receptors may worsen the onset of many neurological disorders. Accordingly, the regular intake of moderate doses of caffeine antagonizes adenosine receptors and affords robust neuroprotection. Although caffeine intake alters brain functional connectivity and multi-omics analyses indicate that caffeine intake modifies synaptic and metabolic processes, it is unclear how caffeine intake affects behavior, synaptic plasticity and its modulation by adenosine. We now report that male mice drinking caffeinated water (0.3 g/L) for 2 weeks were behaviorally indistinguishable (locomotion, mood, memory) from control mice (drinking water) and displayed superimposable synaptic plasticity (long-term potentiation) in different brain areas (hippocampus, prefrontal cortex, amygdala). Moreover, there was a general preservation of the efficiency of adenosine A1 and A2A receptors to control synaptic transmission and plasticity, although there was a tendency for lower levels of endogenous adenosine ensuring A1 receptor-mediated inhibition. In spite of similar behavioral and neurophysiological function, caffeine intake increased the energy charge and redox state of cortical synaptosomes. This increased metabolic competence likely involved a putative increase in the glycolytic rate in synapses and a prospective greater astrocyte-synapse lactate shuttling. It was concluded that caffeine intake does not trigger evident alterations of behavior or of synaptic plasticity but increases the metabolic competence of synapses, which might be related with the previously described better ability of animals consuming caffeine to cope with deleterious stimuli triggering brain dysfunction.

Orbitofrontal cortex functional connectivity changes in patients with binge eating disorder and bulimia nervosa.

We aimed to define the shared and unshared functional neurobiological underpinnings of binge eating disorder (BED) and bulimia nervosa (BN). These disorders both involve loss of control over binge eating, but differ based on purging behavior and body image distortion. BED and BN have also been found to show differences in brain activation patterns in reward sensitivity. We enrolled 13 and 12 drug-naive and medication-free women with BED and BN, respectively, and 22 age- and sex-matched healthy controls. We performed an orbitofrontal cortex (OFC)-seeded resting-state whole brain functional connectivity (FC) analysis among the groups. In this study, BED patients exhibited significantly higher impulsivity than controls, whereas the difference in impulsivity between BN and controls was not significant. Participants with BED and BN showed weaker FC between the left lateral OFC and the right precuneus than controls. In the BED only group, the FC strength between these regions was negatively correlated with self-reported impulsivity. In both BED and BN, FC between the left lateral OFC and the right dorsolateral prefrontal cortex was weaker than that in controls. In BED, FC between the left medial OFC and the right cerebellar lobule IV was stronger than that of other groups. Our current results suggest similarities and differences between BED and BN in OFC-seeded FC with respect to reward processing. In particular, FC of the OFC in BED patients showed a significant correlation with their high impulsivity, which may reflect a decline in executive control over binge eating.