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OBJECTIVE: To determine the association between neighborhood disadvantage (ND) and functional brain development of in utero fetuses. STUDY DESIGN: We conducted an observational study using Social Vulnerability Index (SVI) scores to assess the impact of ND on a prospectively recruited sample of healthy pregnant women from Washington, DC. Using 79 functional magnetic resonance imaging scans from 68 healthy pregnancies at a mean gestational age of 33.12 weeks, we characterized the overall functional brain network structure using a graph metric approach. We used linear mixed effects models to assess the relationship between SVI and gestational age on 5 graph metrics, adjusting for multiple scans. RESULTS: Exposure to greater ND was associated with less well integrated functional brain networks, as observed by longer characteristic path lengths and diminished global efficiency (GE), as well as diminished small world propensity (SWP). Across gestational ages, however, the association between SVI and network integration diminished to a negligible relationship in the third trimester. Conversely, SWP was significant across pregnancy, but the relationship changed such that there was a negative association with SWP earlier in the second trimester that inverted around the transition to the third trimester to a positive association. CONCLUSIONS: These data directly connect ND and altered functional brain maturation in fetuses. Our results suggest that, even before birth, proximity to environmental stressors in the wider neighborhood environment are associated with altered brain development.

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Motor intention is a high-level brain function related to planning for movement. Although studies have shown that motor intentions can be decoded from brain signals before movement execution, it is unclear whether intentions relating to mental imagery of movement can be decoded. Here, we investigated whether differences in spatial and temporal patterns of brain activation were elicited by intentions to perform different types of motor imagery and whether the patterns could be used by a multivariate pattern classifier to detect such differential intentions. The results showed that it is possible to decode intentions before the onset of different types of motor imagery from functional MR signals obtained from fronto-parietal brain regions, such as the premotor cortex and posterior parietal cortex, while controlling for eye movements and for muscular activity of the hands. These results highlight the critical role played by the aforementioned brain regions in covert motor intentions. Moreover, they have substantial implications for rehabilitating patients with motor disabilities.

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Background: Language disturbances are a core feature of schizophrenia, often studied as a formal thought disorder. The neurobiology of language in schizophrenia has been addressed within the same framework, that language and thought are equivalents considering symptoms and not signs. This review aims to systematically examine published peer-reviewed studies that employed neuroimaging techniques to investigate aberrant brain-language networks in individuals with schizophrenia in relation to linguistic signs. Methods: We employed a language model for automatic data extraction. We selected our studies according to the PRISMA recommendations, and we conducted the quality assessment of the selected studies according to the STROBE guidance. Results: We analyzed the findings from 37 studies, categorizing them based on patient characteristics, brain measures, and language task types. The inferior frontal gyrus (IFG) and superior temporal gyrus (STG) exhibited the most significant differences among these studies and paradigms. Conclusions: We propose guidelines for future research in this field based on our analysis. It is crucial to investigate larger networks involved in language processing, and language models with brain metrics must be integrated to enhance our understanding of the relationship between language and brain abnormalities in schizophrenia.

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Functional magnetic resonance imaging (fMRI) studies on migraine with aura are challenging due to the rarity of patients with triggered cases. This study optimized methodologies to explore differences in ictal and interictal spatiotemporal activation patterns based on visual stimuli using fMRI in two patients with unique aura triggers. Both patients underwent separate fMRI sessions during the ictal and interictal periods. The Gaussian Process Classifier (GPC) was used to differentiate these periods by employing a machine learning temporal embedding approach and spatiotemporal activation patterns based on visual stimuli. When restricted to visual and occipital regions, GPC had an improved performance, with accuracy rates for patients A and B of roughly 86-90% and 77-81%, respectively (p < 0.01). The algorithm effectively differentiated visual stimulation and rest periods and identified times when aura symptoms manifested, as evident from the varying predicted probabilities in the GPC models. These findings contribute to our understanding of the role of visual processing and brain activity patterns in migraine with aura and the significance of temporal embedding techniques in examining aura phenomena. This finding has implications for diagnostic tools and therapeutic techniques, especially for patients suffering from aura symptoms.

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It has been reported that cannabis consumption affects the anterior cingulate cortex (ACC), a structure with a central role in mediating the empathic response. In this study, we compared psychometric scores of empathy subscales, between a group of regular cannabis users (85, users) and a group of non-consumers (51, controls). We found that users have a greater Emotional Comprehension, a cognitive empathy trait involving the understanding of the "other" emotional state. Resting state functional MRI in a smaller sample (users = 46, controls = 34) allowed to identify greater functional connectivity (FC) of the ACC with the left somatomotor cortex (SMC), in users when compared to controls. These differences were also evident within the empathy core network, where users showed greater within network FC. The greater FC showed by the users is associated with emotional representational areas and empathy-related regions. In addition, the differences in psychometric scores suggest that users have more empathic comprehension. These findings suggest a potential association between cannabis use, a greater comprehension of the other's affective state and the functional brain organization of the users. However, further research is needed to explore such association, since many other factors may be at play.

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All experiences preserved within episodic memory contain information on the space and time of events. The hippocampus is the main brain region involved in processing spatial and temporal information for incorporation within episodic memory representations. However, the other brain regions involved in the encoding and retrieval of spatial and temporal information within episodic memory are unclear, because a systematic review of related studies is lacking and the findings are scattered. The present study was designed to integrate the results of functional magnetic resonance imaging and positron emission tomography studies by means of a systematic review and meta-analysis to provide converging evidence. In particular, we focused on identifying the brain regions involved in the retrieval of spatial and temporal information. We identified a spatial retrieval network consisting of the inferior temporal gyrus, parahippocampal gyrus, superior parietal lobule, angular gyrus, and precuneus. Temporal context retrieval was supported by the dorsolateral prefrontal cortex. Thus, the retrieval of spatial and temporal information is supported by different brain regions, highlighting their different natures within episodic memory.

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Fibromyalgia, a condition characterized by chronic pain, is frequently accompanied by emotional disturbances. Here we aimed to study brain activation and functional connectivity (FC) during processing of emotional stimuli in fibromyalgia. Thirty female patients with fibromyalgia and 31 female healthy controls (HC) were included. Psychometric tests were administered to measure alexithymia, affective state, and severity of depressive and anxiety symptoms. Next, participants performed an emotion processing and regulation task during functional magnetic resonance imaging (fMRI). We performed a 2 × 2 ANCOVA to analyze main effects and interactions of the stimuli valence (positive or negative) and group (fibromyalgia or HC) on brain activation. Generalized psychophysiological interaction analysis was used to assess task-dependent FC of brain regions previously associated with emotion processing and fibromyalgia (i.e., hippocampus, amygdala, anterior insula, and pregenual anterior cingulate cortex [pACC]). The left superior lateral occipital cortex showed more activation in fibromyalgia during emotion processing than in HC, irrespective of valence. Moreover, we found an interaction effect (valence x group) in the FC between the left pACC and the precentral and postcentral cortex, and central operculum, and premotor cortex. These results suggest abnormal brain activation and connectivity underlying emotion processing in fibromyalgia, which could help explain the high prevalence of psychopathological symptoms in this condition.

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INTRODUCTION: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects 3% of children in the world. OBJECTIVE: In this work, we seek to compare the different brain activations of pediatric patients with and without ADHD. METHODS: A functional resonance examination with BOLD contrast was applied using the MOXO-CPT test (Continuous Performance test with single and double visual-auditory distractors). RESULTS: Differences in BOLD activation were observed indicating that control children regularly presented negative BOLD activations that were not found in children with ADHD. Inhibitory activity in audiovisual association zones in control patients was greater than in patients with ADHD. The inhibition in the frontal and motor regions in the controls contrasted with the overactivation of the motor areas in patients with ADHD, this, together with the detection of cerebellar activation which attempted to modulate the responses of the different areas that lead to executive failure in patients with ADHD. CONCLUSIONS: In view of these results, it can be argued that the lack of inhibition of ADHD patients in their executive functions led to a disorganization of the different brain systems.

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BACKGROUND: The present study aimed to apply multivariate pattern recognition methods to predict posttraumatic stress symptoms from whole-brain activation patterns during two contexts where the aversiveness of unpleasant pictures was manipulated by the presence or absence of safety cues. METHODS: Trauma-exposed participants were presented with neutral and mutilation pictures during functional magnetic resonance imaging (fMRI) collection. Before the presentation of pictures, a text informed the subjects that the pictures were fictitious ("safe context") or real-life scenes ("real context"). We trained machine learning regression models (Gaussian process regression (GPR)) to predict PTSD symptoms in real and safe contexts. RESULTS: The GPR model could predict PTSD symptoms from brain responses to mutilation pictures in the real context but not in the safe context. The brain regions with the highest contribution to the model were the occipito-parietal regions, including the superior parietal gyrus, inferior parietal gyrus, and supramarginal gyrus. Additional analysis showed that GPR regression models accurately predicted clusters of PTSD symptoms, nominal intrusion, avoidance, and alterations in cognition. As expected, we obtained very similar results as those obtained in a model predicting PTSD total symptoms. CONCLUSION: This study is the first to show that machine learning applied to fMRI data collected in an aversive context can predict not only PTSD total symptoms but also clusters of PTSD symptoms in a more aversive context. Furthermore, this approach was able to identify potential biomarkers for PTSD, especially in occipitoparietal regions.

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OBJECTIVE: Patients with multifocal or generalized epilepsies manifesting with drop attacks have severe refractory seizures and significant cognitive and behavioural abnormalities. It is unclear to what extent these features relate to network abnormalities and how networks in sensorimotor cortex differ from those in patients with refractory focal epilepsies. Thus, in this study we sought to provide preliminary data on connectivity of sensorimotor cortex in patients with epileptic drop attacks, in comparison to patients with focal refractory epilepsies. METHODS: Resting-state fMRI (rs-fMRI) data was available for 5 patients with epileptic drop attacks and 15 with refractory focal epilepsies undergoing presurgical evaluation. Functional connectivity was analyzed with a seed-based protocol, with primary seeds placed at the precentral gyrus, the postcentral gyrus and the premotor cortex. For each seed, the subjects' timeseries were extracted and transformed to Z scores. Between-group analysis was then performed using the 3dttest+ + AFNI program. RESULTS: Two clusters of reduced connectivity in the group with drop attacks (DA group) in relation to those with focal epilepsies were found in the between-group analysis: the precentral seed showed reduced connectivity in the surrounding motor area, and the postcentral seed, reduced connectivity with the ipsilateral posterior cingulate gyrus. In the intra-group analyses, sensorimotor and premotor networks were abnormal in the DA group, whereas patients with focal epilepsies had the usual connectivity maps with each seed. CONCLUSION: This pilot study shows differences in the cerebral connectivity in the sensorimotor cortex of patients with generalized epilepsies and drop attacks which should be further explored to better understand the biological bases of the seizure generation and cognitive changes in these people.

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Objective. Schizophrenia(SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals.Approach.For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature.Main results.When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ.Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

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Introduction: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation. Methods: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning. Results: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning. Discussion: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.

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The belief that learning can be modulated by social context is mainly supported by high-level value-based learning studies. However, whether social context can even modulate low-level learning such as visual perceptual learning (VPL) is still unknown. Unlike traditional VPL studies in which participants were trained singly, here, we developed a novel dyadic VPL paradigm in which paired participants were trained with the same orientation discrimination task and could monitor each other's performance. We found that the social context (i.e., dyadic training) led to a greater behavioral performance improvement and a faster learning rate compared with the single training. Interestingly, the facilitating effects could be modulated by the performance difference between paired participants. Functional magnetic resonance imaging (fMRI) results showed that, compared with the single training, social cognition areas including bilateral parietal cortex and dorsolateral prefrontal cortex displayed a different activity pattern and enhanced functional connectivities to early visual cortex (EVC) during the dyadic training. Furthermore, the dyadic training resulted in more refined orientation representation in primary visual cortex (V1), which was closely associated with the greater behavioral performance improvement. Taken together, we demonstrate that the social context, learning with a partner, can remarkably augment the plasticity of low-level visual information process by means of reshaping the neural activities in EVC and social cognition areas, as well as their functional interplays.

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INTRODUCTION: Seed-based analysis has shown that transcutaneous auricular vagus nerve stimulation (taVNS) can modulate the dysfunctional brain network in patients with major depressive disorder (MDD). However, the voxel-based neuropsychological mechanism of taVNS on patients with first-episode MDD is poorly understood. The objective of this study was to assess the effects of an 8-week course of taVNS on patients with first-episode MDD. METHODS: Twenty-two patients with first-episode MDD accepted an 8-week course of taVNS treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed before and after treatment. Voxel-based analyses were performed to characterize spontaneous brain activity. Healthy controls (n=23) were recruited to minimize test-retest effects. Analysis of covariance (ANCOVA) was performed to ascertain treatment-related changes. Then, correlations between changes in brain activity and the Hamilton Depression Rating Scale (HAM-D)/Hamilton Anxiety Scale (HAM-A) remission rate were estimated. RESULTS: Significant group-by-time interactions on voxel-based analyses were observed in the inferior ventral striatum (VSi) and precuneus. Post-hoc analyses showed that taVNS inhibited higher brain activity in the VSi, while upregulating it in the precuneus. Functional connectivity (FC) between the VSi and precuneus decreased. Positive correlations were found between the HAM-D remission rate and changes in brain activity in the VSi. CONCLUSION: taVNS reduced the FC between VSi and precuneus by normalizing the abnormal spontaneous brain activity of VSi in first-episode MDD patients.

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Introduction: Seed-based analysis has shown that transcutaneous auricular vagus nerve stimulation (taVNS) can modulate the dysfunctional brain network in patients with major depressive disorder (MDD). However, the voxel-based neuropsychological mechanism of taVNS on patients with first-episode MDD is poorly understood. The objective of this study was to assess the effects of an 8-week course of taVNS on patients with first-episode MDD. Methods: Twenty-two patients with first-episode MDD accepted an 8-week course of taVNS treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed before and after treatment. Voxel-based analyses were performed to characterize spontaneous brain activity. Healthy controls (n=23) were recruited to minimize test-retest effects. Analysis of covariance (ANCOVA) was performed to ascertain treatment-related changes. Then, correlations between changes in brain activity and the Hamilton Depression Rating Scale (HAM-D)/Hamilton Anxiety Scale (HAM-A) remission rate were estimated. Results: Significant group-by-time interactions on voxel-based analyses were observed in the inferior ventral striatum (VSi) and precuneus. Post-hoc analyses showed that taVNS inhibited higher brain activity in the VSi, while upregulating it in the precuneus. Functional connectivity (FC) between the VSi and precuneus decreased. Positive correlations were found between the HAM-D remission rate and changes in brain activity in the VSi. Conclusion: taVNS reduced the FC between VSi and precuneus by normalizing the abnormal spontaneous brain activity of VSi in first-episode MDD patients.

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The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

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Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

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Social emotions are critical to successfully navigate in a complex social world because they promote self-regulation of behaviour. Difficulties in social behaviour are at the core of autism spectrum disorder (ASD). However, social emotions and their neural correlates have been scarcely investigated in this population. In particular, the experience of envy has not been addressed in ASD despite involving neurocognitive processes crucially compromised in this condition. Here, we used an fMRI adapted version of a well-validated task to investigate the subjective experience of envy and its neural correlates in adults with ASD (n = 30) in comparison with neurotypical controls (n = 28). Results revealed that both groups reported similarly intense experience of envy in association with canonical activation in the anterior cingulate cortex and the anterior insula, among other regions. However, in participants with ASD, the experience of envy was accompanied by overactivation of the posterior insula, the postcentral gyrus and the posterior superior temporal gyrus, regions subserving the processing of painful experiences and mentalizing. This pattern of results suggests that individuals with ASD may use compensatory strategies based on the embodied amplification of pain and additional mentalizing efforts to shape their subjective experience of envy. Results have relevant implications to better understand the heterogeneity of this condition and to develop new intervention targets.

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Red wine (RW) consumption has been proposed to have a potential health benefit. However, the effect of RW consumption on the brain is not entirely known, mainly when associated with aging. Regular red wine consumers (n = 30) and abstainers (ABST; n = 27) without cognitive impairment were evaluated for brain structural characteristics (Fazekas score and voxel-based morphometry) and for functional adaptations assessed by fMRI (using the Word Tasks Color Stroop (WCST) and Two-Back (TBT)), as well as by neuropsychological tests in different domains. There were no significant differences regarding brain morphological features. RW consumers showed greater activation in the thalamus during WCST and in paracingulate/anterior cingulate cortices, left superior frontal gyrus and frontal pole during TBT. ABST required higher activation of different cortical areas in the left parietal lobe during WCST. Age and intelligence quotient influenced those activations. In Stroop and trail-making neuropsychological tests, RW consumers performed slightly better than ABST. This study should be viewed as hypothesis-generating rather than conclusive.HighlightsWhite matter hyperintensities and gray matter volume did not differ between the RW and ABST groups.RW consumers could depend more on right thalamus during WSCT due to its role in visual integration.ABST could depend more on left parietal lobe during WSCT due to its role in sensory and phonological encoding.RW consumers with inferior cognitive abilities could depend more on letter recognition to solve a TBT correctly.Younger abstainers could depend more on different areas involved in integrating cognitive processes and attention regulation to solve a TBT correctly.

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The eloquent cerebral cortices are involved in movement, sensation, speech, vision, and higher cortical functions. Functional magnetic resonance imaging (fMRI) allows the evaluation of brain function, aiding in neurosurgical planning by mapping eloquent cortical areas. Considering the high cost of the hardware involved, the purpose of this work is to present a more affordable, in-house alternative for these studies that can provide adequate results in a clinical setting. We also present some practical information on how to perform these exams. We describe an affordable in-house hardware solution used by an imaging center, and examples of fMRI paradigms used to evaluate motor and language tasks. The fMRI studies show robust activations in eloquent areas consistent with the tasks performed on the exam. Images of post-processed studies illustrate clinical cases. The fMRI have well-established applications, mapping eloquent cortical areas in patients with brain lesions. In the case of surgical planning, it allows the surgeon to maximize the resection area while minimizing sequelae. More affordable hardware can reduce the cost of these exams, making them more accessible to the general public.

O córtex cerebral eloquente está envolvido nas atividades motora, sensação, fala, visão e funções corticais superiores. A ressonância magnética funcional (RMf) permite a avaliação da função cerebral, ajudando no planejamento neurocirúrgico através do mapeamento de áreas corticais eloquentes. Considerando o elevado custo do hardware envolvido, o objetivo deste trabalho é apresentar uma alternativa mais acessível para estes estudos, que possa fornecer resultados adequados em um ambiente clínico. Também apresentamos algumas informações práticas sobre a realização destes exames. Descrevemos uma solução de hardware acessível utilizada por um centro de imagens, e exemplos de paradigmas de RMf usados para avaliar tarefas motoras e relacionadas à fala. Os estudos de RMf mostram ativações em áreas eloquentes, consistentes com as tarefas realizadas no exame com imagens de estudos pós-processados ilustrando casos clínicos. A RMf tem aplicações bem estabelecidas, mapeando áreas corticais eloquentes em pacientes com lesões cerebrais. No caso do planejamento cirúrgico, permite que o cirurgião maximize a área de ressecção enquanto minimiza potenciais sequelas. Equipamentos mais acessíveis podem reduzir o custo destes exames, podendo aumentar a disponibilização ao público em geral.

La corteza cerebral elocuente está implicada en las actividades motoras, la sensibilidad, el habla, la visión y las funciones corticales superiores. La resonancia magnética funcional (RMf) permite la evaluación de la función cerebral, ayudando en la planificación neuroquirúrgica mediante el mapeo de las áreas corticales elocuentes. Teniendo en cuenta el elevado coste del hardware implicado, el objetivo de este artículo es presentar una alternativa más asequible para estos estudios que pueda proporcionar resultados adecuados en un entorno clínico. También presentamos información práctica sobre cómo realizar estos exámenes. Describimos una solución de hardware asequible utilizada por un centro de diagnóstico por imagen, y ejemplos de paradigmas de RMf utilizados para evaluar tareas motoras y relacionadas con el habla. Los estudios de RMf muestran activaciones en áreas elocuentes, coherentes con las tareas realizadas en el examen, con imágenes de estudios postprocesados que ilustran casos clínicos. La RMf tiene aplicaciones bien establecidas en el mapeo de áreas corticales elocuentes en pacientes con lesiones cerebrales. En el caso de la planificación quirúrgica, permite al cirujano maximizar el área de resección minimizando las posibles secuelas. Un equipo más asequible puede reducir el coste de estas exploraciones, aumentando potencialmente su disponibilidad para el público en general.