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Episodic autobiographical memory involves the ability to travel along the mental timeline, so that events of our own life can be recollected and re-experienced. In the present study, we tested the neural underpinnings of mental travel across past and future autobiographical events by using a spatiotemporal interference task. Participants were instructed to mentally travel across past and future personal (Episodic Autobiographical Memories; EAMs) and Public Events (PEs) during Functional Magnetic Resonance Imaging (fMRI). We found that a distributed network of brain regions (i.e., occipital, temporal, parietal, frontal, and subcortical regions) is implicated in mental projection across past and future independently from the memory category (EAMs or PEs). Interestingly, we observed that most of these regions exhibited a neural modulation as a function of the lifetime period and/or as a function of the compatibility with a back-to-front mental timeline, specifically for EAMs, indicating the key role of these regions in representing the temporal organization of personal but not public events. Present findings provide insights into how personal events are temporally organized within the human brain.

•We present convolutional neural network (MU-Net) for segmentation of mouse brain MRI.•MU-Net yielded an excellent performance when tested on 1782 mouse MRI volumes.•MU-Net generalized to different mouse genotypes and gender.•MU-Net is extremely fast, requiring less than a second to segment a single image. Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

Several laboratories have consistently reported small concentration changes in lactate, glutamate, aspartate, and glucose in the human cortex during prolonged stimuli. However, whether such changes correlate with blood oxygenation level—dependent functional magnetic resonance imaging (BOLD-fMRI) signals have not been determined. The present study aimed at characterizing the relationship between metabolite concentrations and BOLD-fMRI signals during a block-designed paradigm of visual stimulation. Functional magnetic resonance spectroscopy (fMRS) and fMRI data were acquired from 12 volunteers. A short echo-time semi-LASER localization sequence optimized for 7 Tesla was used to achieve full signal-intensity MRS data. The group analysis confirmed that during stimulation lactate and glutamate increased by 0.26±0.06 μmol/g (∼30%) and 0.28±0.03 μmol/g (∼3%), respectively, while aspartate and glucose decreased by 0.20±0.04 μmol/g (∼5%) and 0.19±0.03 μmol/g (∼16%), respectively. The single-subject analysis revealed that BOLD-fMRI signals were positively correlated with glutamate and lactate concentration changes. The results show a linear relationship between metabolic and BOLD responses in the presence of strong excitatory sensory inputs, and support the notion that increased functional energy demands are sustained by oxidative metabolism. In addition, BOLD signals were inversely correlated with baseline γ-aminobutyric acid concentration. Finally, we discussed the critical importance of taking into account linewidth effects on metabolite quantification in fMRS paradigms.

•Systematic review on automated MRI segmentation for Alzheimer's diagnosis.•Focus on studies integrating MRI with neuropsychological assessments.•Literature search conducted on PubMed for relevant studies.•Seven studies showed significant links between MRI changes and cognitive decline.•Neuropsychological tests improved diagnostic accuracy for MCI and mild AD. This systematic review investigates the integration of automatic segmentation techniques of magnetic resonance imaging (MRI) with neuropsychological assessments for early diagnosis and prognosis of Alzheimer’s Disease (AD). Focus on studies that utilise automated MRI segmentation and neuropsychological evaluations across the AD spectrum. A literature search was conducted on the PubMed database on 7 November 2024, using key terms related to MRI, segmentation, brain structures, AD, and cognitive decline. Studies including individuals with AD, mild cognitive impairment (MCI), or subjective cognitive decline (SCD), utilising structural MRI, focusing on grey matter and automatic segmentation, and reporting cognitive assessments were included. Data were extracted and synthesised focusing on associations between MRI measures and cognitive tests, and discriminative values for diagnosis or prognosis. Seven studies were included, showing a significant association between structural changes in the medial temporal lobe and cognitive decline. The combination of MRI volumetric measures and neuropsychological scores enhanced diagnostic accuracy. Neuropsychological measures demonstrated superiority in the identification of patients with MCI and mild AD in comparison to MRI measures alone. Heterogeneity across studies, selection and measurement bias, and lack of non-response data were noted. This review emphasises the necessity of integrating automated MRI segmentation with neuropsychological assessments for the diagnosis and prognosis of AD. While MRI is valuable, neuropsychological testing remains essential for early detection. Future studies should focus on developing integrated predictive models and refining neuroimaging techniques.

Energy homeostasis in the brain is maintained by oxidative metabolism of glucose, primarily to fulfil the energy demand associated with ionic movements in neurons and astrocytes. In this contribution we review the experimental evidence that grounds a specific role of glycogen metabolism in supporting the functional energetic needs of astrocytes during the removal of extracellular potassium. Based on theoretical considerations, we further discuss the hypothesis that the mobilization of glycogen in astrocytes serves the purpose to enhance the availability of glucose for neuronal glycolytic and oxidative metabolism at the onset of stimulation. Finally, we provide an evolutionary perspective for explaining the selection of glycogen as carbohydrate reserve in the energy-sensing machinery of cell metabolism.

Parkinson’s disease (PD) is a multisystem disorder, with early changes extending beyond basal ganglia circuitries and involving non-dopaminergic pathways, including cerebellar networks. Whether cerebellar dysfunction reflects a compensatory mechanism or an intrinsic hallmark of disease progression remains unresolved. In this cross-sectional study, we examined how cerebellar γ-aminobutyric acid (GABA) and glutamate/glutamine (Glx) systems, as well as their excitatory/inhibitory (E/I) balance, are modulated along the disease course. As to ascertain how these mechanisms contribute to motor and non-motor features in the premotor and early stages of PD, 18 individuals with isolated REM sleep behavior disorder (iRBD), 20 de novo, drug-naïve PD (dnPD), and 18 matched healthy controls underwent clinical, cognitive, and neuropsychiatric assessments alongside cerebellar magnetic resonance spectroscopy (MRS, MEGA-PRESS, 3T). While cerebellar neurotransmitter levels did not differ significantly across groups, dnPD patients exhibited a shift toward hyperexcitability in the E/I ratio, without correlation to clinical or cognitive measures. In contrast, in iRBD, an inverse relationship between heightened GABAergic activity and neuropsychiatric symptoms emerged. These findings suggest an early, dynamic cerebellar involvement, potentially reflecting compensatory modulation of altered basal ganglia output. Our results support cerebellar GABA MRS as a promising biomarker and open perspectives for targeting non-dopaminergic pathways in PD.

•Spatial activity patterns evoked by the observation of natural movements occur at rest in the absence of any visual stimulus.•Although not represented at rest, uncommon movements evoke enhanced activation in parietal and premotor areas.•Spontaneous activity maintains cognitive representations for motor planning of frequent behaviors. Spontaneous brain activity forms correlated networks resembling task-evoked activation patterns, yet its functional relevance remains debated. The representational hypothesis suggests that resting-state networks (RSNs) encode frequent behaviors, but whether these representations are motor-based or cognitive is unclear. Here, we used fMRI to examine RSNs activity during the observation of reach-to-grasp movements with either regular (common) or perturbed (uncommon) kinematics. We found that the dorsal attention network (DAN) exhibited greater similarity between rest and task patterns for common movements, whereas sensory networks showed no significant effects. While DAN is classically associated with attention mechanisms, these results suggest that it may also contribute to tracking the location or motion of the hand. Furthermore, uncommon movements elicited stronger activation in parietal and premotor areas, likely reflecting adaptive updating of internal models. Our findings support the role of spontaneous brain activity in maintaining cognitive representations of frequent behaviors, optimizing motor planning and perception.

A fundamental limitation of fMRI based on the BOLD effect is its limited spatial specificity. This is because the BOLD signal reflects neurovascular coupling, leading to macrovascular changes that are not strictly limited to areas of increased neural activity. However, neuronal activation also induces microstructural changes within the brain parenchyma by modifying the diffusion of extracellular biological water. Therefore, diffusion-weighted imaging (DWI) has been applied in fMRI to overcome BOLD limits and better explain the mechanisms of functional activation, but the results obtained so far are not clear. This is because a DWI signal depends on many experimental variables: instrumental, physiological, and microstructural. Here, we hypothesize that the γ parameter of the fractional diffusion representation could be of particular interest for DW-fMRI applications, due to its proven dependence on local magnetic susceptibility and diffusion multi-compartmentalization. BOLD fMRI and DW-fMRI experiments were performed at 3T using an exemplar application to task-based activation of the human visual cortex. The results, corroborated by simulation, highlight that γ provides complementary information to conventional diffusion fMRI and γ can quantify cellular morphology changes and neurovascular regulation during neuronal activation with higher sensitivity and specificity than conventional BOLD fMRI and DW-fMRI.

Phasic changes in eye's pupil diameter have been repeatedly observed during cognitive, emotional and behavioral activity in mammals. Although pupil diameter is known to be associated with noradrenergic firing in the pontine Locus Coeruleus (LC), thus far the causal chain coupling spontaneous pupil dynamics to specific cortical brain networks remains unknown. In the present study, we acquired steady-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data combined with eye-tracking pupillometry from fifteen healthy subjects that were trained to maintain a constant attentional load. Regression analysis revealed widespread visual and sensorimotor BOLD-fMRI deactivations correlated with pupil diameter. Furthermore, we found BOLD-fMRI activations correlated with pupil diameter change rate within a set of brain regions known to be implicated in selective attention, salience, error-detection and decision-making. These regions included LC, thalamus, posterior cingulate cortex (PCC), dorsal anterior cingulate and paracingulate cortex (dACC/PaCC), orbitofrontal cortex (OFC), and right anterior insular cortex (rAIC). Granger-causality analysis performed on these regions yielded a complex pattern of interdependence, wherein LC and pupil dynamics were far apart in the network and separated by several cortical stages. Functional connectivity (FC) analysis revealed the ubiquitous presence of the superior frontal gyrus (SFG) in the networks identified by the brain regions correlated to the pupil diameter change rate. No significant correlations were observed between pupil dynamics, regional activation and behavioral performance. Based on the involved brain regions, we speculate that pupil dynamics reflects brain processing implicated in changes between self- and environment-directed awareness.Phasic changes in eye's pupil diameter have been repeatedly observed during cognitive, emotional and behavioral activity in mammals. Although pupil diameter is known to be associated with noradrenergic firing in the pontine Locus Coeruleus (LC), thus far the causal chain coupling spontaneous pupil dynamics to specific cortical brain networks remains unknown. In the present study, we acquired steady-state blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) data combined with eye-tracking pupillometry from fifteen healthy subjects that were trained to maintain a constant attentional load. Regression analysis revealed widespread visual and sensorimotor BOLD-fMRI deactivations correlated with pupil diameter. Furthermore, we found BOLD-fMRI activations correlated with pupil diameter change rate within a set of brain regions known to be implicated in selective attention, salience, error-detection and decision-making. These regions included LC, thalamus, posterior cingulate cortex (PCC), dorsal anterior cingulate and paracingulate cortex (dACC/PaCC), orbitofrontal cortex (OFC), and right anterior insular cortex (rAIC). Granger-causality analysis performed on these regions yielded a complex pattern of interdependence, wherein LC and pupil dynamics were far apart in the network and separated by several cortical stages. Functional connectivity (FC) analysis revealed the ubiquitous presence of the superior frontal gyrus (SFG) in the networks identified by the brain regions correlated to the pupil diameter change rate. No significant correlations were observed between pupil dynamics, regional activation and behavioral performance. Based on the involved brain regions, we speculate that pupil dynamics reflects brain processing implicated in changes between self- and environment-directed awareness.