Brain Activation Patterns Research Articles

Correlation and underlying brain mechanisms between rapid eye movement sleep behavior disorder and executive functions in Parkinson's disease: an fNIRS study.

Rapid eye movement sleep behavior disorder (RBD) affects 30%-40% of patients with Parkinson's disease (PD) and has been linked to a higher risk of cognitive impairment, especially executive dysfunction. The aim of this study was to investigate the brain activation patterns in PD patients with RBD (PD-RBD+) compared to those without RBD (PD-RBD-) and healthy controls (HCs), and to analyze the correlation between changes in cerebral cortex activity and the severity of RBD. We recruited 50 PD patients, including 30 PD-RBD+, 20 PD-RBD-, and 20 HCs. We used functional near infrared spectroscopy during a verbal fluency task (VFT-fNIRS) and clinical neuropsychological assessment to explore the correlation between PD-RBD+ and executive function and changes in neural activity. The VFT-fNIRS analysis revealed a significant reduction in activation among PD-RBD+ patients across multiple channels when compared to both the PD-RBD- and HC groups. Specifically, PD-RBD+ patients exhibited diminished activation in the bilateral dorsolateral prefrontal cortex (DLPFC) and the right ventrolateral prefrontal cortex (VLPFC) relative to their PD-RBD- counterparts. Furthermore, compared to the HC group, PD-RBD+ patients displayed reduced activation specifically in the right DLPFC. Significantly, a noteworthy negative correlation was identified between the average change in oxygenated hemoglobin concentration (ΔHbO2) in the right DLPFC of PD-RBD+ patients and the severity of their RBD. Our study offers compelling evidence that RBD exacerbates cognitive impairment in PD, manifested as executive dysfunction, primarily attributed to reduced prefrontal activation. These aberrations in brain activation may potentially correlate with the severity of RBD.

Empowering EEG motor imagery classification with deep transfer learning approach

AbstractThe brain‐computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared to traditional methods integrated with machine learning (ML) approaches that were initially explored in the past decade, there is a relatively limited number of studies utilizing deep learning (DL) approaches in BCI applications. A novel approach has been developed for the automatic recognition of motor imagery (MI) tasks. This article introduces the improved sparrow search algorithm with deep transfer learning‐based EEG motor imagery classification (ISSADTL‐EEGMIC) algorithm for BCIs. The presented ISSADTL‐EEGMIC algorithm primarily focuses on the automated classification of motor imagery tasks. To achieve this, the ISSADTL‐EEGMIC method first preprocesses EEG signals using the wavelet packet decomposition (WPD) technique. Additionally, it employs the neural architecture search network (NASNet) technique for feature extraction. Moreover, the ISSADTL‐EEGMIC model performs the classification process using the stacked sparse autoencoder (SSAE) model. Furthermore, hyperparameter optimization of the SSAE model is performed using the ISSA technique, which helps achieve maximum classification performance. The results from the simulation of the ISSADTL‐EEGMIC algorithm have been examined using two benchmark datasets. The experimental findings suggest that it outperforms recent approaches in terms of performance.

Isolating Neural Signatures of Conscious Speech Perception with a No-Report Sine-Wave Speech Paradigm.

Identifying neural correlates of conscious perception is a fundamental endeavor of cognitive neuroscience. Most studies so far have focused on visual awareness along with trial-by-trial reports of task-relevant stimuli, which can confound neural measures of perceptual awareness with postperceptual processing. Here, we used a three-phase sine-wave speech paradigm that dissociated between conscious speech perception and task relevance while recording EEG in humans of both sexes. Compared with tokens perceived as noise, physically identical sine-wave speech tokens that were perceived as speech elicited a left-lateralized, near-vertex negativity, which we interpret as a phonological version of a perceptual awareness negativity. This response appeared between 200 and 300 ms after token onset and was not present for frequency-flipped control tokens that were never perceived as speech. In contrast, the P3b elicited by task-irrelevant tokens did not significantly differ when the tokens were perceived as speech versus noise and was only enhanced for tokens that were both perceived as speech and relevant to the task. Our results extend the findings from previous studies on visual awareness and speech perception and suggest that correlates of conscious perception, across types of conscious content, are most likely to be found in midlatency negative-going brain responses in content-specific sensory areas.

The psychological, computational, and neural foundations of indebtedness

Receiving a favor from another person may induce a negative feeling of indebtedness for the beneficiary. In this study, we explore these hidden costs by developing and validating a conceptual model of indebtedness across three studies that combine a large-scale online questionnaire, an interpersonal game, computational modeling, and neuroimaging. Our model captures how individuals perceive the altruistic and strategic intentions of the benefactor. These inferences produce distinct feelings of guilt and obligation that together comprise indebtedness and motivate reciprocity. Perceived altruistic intentions convey care and communal concern and are associated with activity in insula, ventromedial prefrontal cortex and dorsolateral prefrontal cortex, while inferred strategic intentions convey expectations of future reciprocity and are associated with activation in temporal parietal junction and dorsomedial prefrontal cortex. We further develop a neural utility model of indebtedness using multivariate patterns of brain activity that captures the tradeoff between these feelings and reliably predicts reciprocity behavior.

Exploring the relationship between physical activity and cognitive function: an fMRI pilot study in young and older adults.

There are limited studies exploring the relationship between physical activity (PA), cognitive function, and the brain processing characteristics in healthy older adults. A total of 41 participants (42.7 ± 20.5 years, 56.1% males) were included in the data analysis. The International Physical Activity Questionnaire Short Form was used to assess PA levels, and the Chinese version of the Montreal Cognitive Assessment-Basic and the Flanker task were employed to evaluate cognitive function. Furthermore, fMRI technology was utilized to examine brain activation patterns. The cognitive function of the older adults was found to be significantly lower compared to the young adults. Within the older adults, those with high levels of PA exhibited significantly higher cognitive function than those with low and medium PA levels. The fMRI data showed significant differences in brain activation patterns among young adults across the different PA levels. However, such difference was not observed among older adults. A decline in cognitive function was observed among older adults. There was a significant correlation between the levels of PA and cognitive function in healthy older adults. The study demonstrated significant effects of PA levels on brain activation patterns in inhibitory control-related regions among young adults, while not significant among older adults. The findings suggest that neurological mechanisms driving the relationship between PA and cognitive function may differ between older and young adults.

Patterns of Brain Activity in Aggressors: A Meta-Analysis Study of Functional Magnetic Resonance Imaging on Decision-Making and Emotional Regulation

The cognitive and emotional processes involved in criminal behavior are varied and complex. Decision-making, inhibitory control, emotional processing, empathy and theory of mind, and emotional regulation are some of the key processes involved. Studies using functional neuroimaging have identified distinct patterns of brain activity in regions associated with these processes, providing clues about the mechanisms underlying criminal behavior. For example, differences in activation of the prefrontal cortex and amygdala have been observed in aggressors compared to nonaggressors during tasks involving moral judgment and impulsivity. These findings underscore the importance of neuroscience in investigating the cognitive and emotional processes underlying criminal behavior, offering valuable insights for theory and practice in the fields of criminology and criminal justice.

Examining the influence of self-care practices on brain activity in healthy older adults.

Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models. 77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC). Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident. The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.

Wearing a KN95/FFP2 facemask has no measureable effect on functional activity in a challenging working memory n-back task.

Wide use of facemasks is one of the many consequences of the COVID-19 pandemic. We used an established working memory n-back task in functional magnetic resonance imaging (fMRI) to explore whether wearing a KN95/FFP2 facemask affects overall performance and brain activation patterns. We provide here a prospective crossover design 3 T fMRI study with/without wearing a tight FFP2/KN95 facemask, including 24 community-dwelling male healthy control participants (mean age ± SD = 37.6 ± 12.7 years) performing a 2-back task. Data analysis was performed using the FSL toolbox, performing both task-related and functional connectivity independent component analyses. Wearing an FFP2/KN95 facemask did not impact behavioral measures of the 2-back task (response time and number of errors). The 2-back task resulted in typical activations in working-memory related areas in both MASK and NOMASK conditions. There were no statistically significant differences in MASK versus NOMASK while performing the 2-back task in both task-related and functional connectivity fMRI analyses. The effect of wearing a tight FFP2/KN95 facemasks did not significantly affect working memory performance and brain activation patterns of functional connectivity.

Brain Activation Pattern Caused by Soft Rehabilitation Glove and Virtual Reality Scenes: A Pilot fNIRS Study.

Clinical studies have proved significant improvements in hand motor function in stroke patients when assisted by robotic devices. However, there were few studies on neural activity changes in the brain during execution. This study aimed to investigate the brain activation pattern caused by soft rehabilitation glove and virtual reality scenes. Twenty healthy subjects and twenty stroke patients were recruited to complete three controlled trials: grasping passively with robotic glove assistance (RA), watching grasping movement video in virtual reality (VR), and the joint use of robotic glove and virtual reality (VRA). Neural activity in the prefrontal cortex, motor cortex and occipital lobe was synchronously collected by the functional near-infrared spectroscopy (fNIRS) device. Activation level and functional connectivity of these brain regions were subsequently calculated and statistically analyzed. For both groups, the VR and VRA tasks induced activation of larger cortical areas. Stroke group had higher average cortical activation in all three tasks compared to healthy group, especially in the prefrontal cortex (P < 0.05). Functional connectivity was weaker in the stroke group than in the healthy group across most regions, but was significantly stronger across some regions of the right hemisphere. These findings suggest significant differences in activation patterns across three tasks. In addition, multi-sensory stimulation can promote functional communication between more brain regions in patients. It has potential for neuromodulation in rehabilitation training by setting up different sensory stimulation modalities.

Immediate and long-term brain activation of acupuncture on ischemic stroke patients: an ALE meta-analysis of fMRI studies.

Acupuncture, as an alternative and complementary therapy recommended by the World Health Organization for stroke treatment, holds potential in ameliorating neurofunctional deficits induced by ischemic stroke (IS). Understanding the immediate and long-term effects of acupuncture and their interrelation would contribute to a better comprehension of the mechanisms underlying acupuncture efficacy. Activation likelihood estimation (ALE) meta-analysis was used to analyze the brain activation patterns reported in 21 relevant functional neuroimaging studies. Among these studies, 12 focused on the immediate brain activation and 9 on the long-term activation. Single dataset analysis were employed to identify both immediate and long-term brain activation of acupuncture treatment in IS patients, while contrast and conjunction analysis were utilized to explore distinctions and connections between the two. According to the ALE analysis, immediately after acupuncture treatment, IS patients exhibited an enhanced cluster centered around the right precuneus (PCUN) and a reduced cluster centered on the left middle frontal gyrus (MFG). After long-term acupuncture treatment, IS patients showed an enhanced cluster in the left PCUN, along with two reduced clusters in the right insula (INS) and hippocampus (HIP), respectively. Additionally, in comparison to long-term acupuncture treatment, the right angular gyrus (ANG) demonstrated higher ALE scores immediately after acupuncture, whereas long-term acupuncture resulted in higher scores in the left superior parietal gyrus (SPG). The intersecting cluster activated by both of them was located in the left cuneus (CUN). The findings provide initial insights into both the immediate and long-term brain activation patterns of acupuncture treatment for IS, as well as the intricate interplay between them. Both immediate and long-term acupuncture treatments showed distinct patterns of brain activation, with the left CUN emerging as a crucial regulatory region in their association. https://www.crd.york.ac.uk/prospero/, CRD42023480834.

S-TBN: A New Neural Decoding Model to Identify Stimulus Categories From Brain Activity Patterns.

Neural decoding is still a challenging and a hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatiotemporal structural information represent the brain's activation information under external stimuli. In the traditional method, brain network features are directly obtained using the standard machine learning method and provide to a classifier, subsequently decoding external stimuli. However, this method cannot effectively extract the multidimensional structural information hidden in the brain network. Furthermore, studies on tensors have show that the tensor decomposition model can fully mine unique spatiotemporal structural characteristics of a spatiotemporal structure in data with a multidimensional structure. This research proposed a stimulus-constrained Tensor Brain Network (s-TBN) model that involves the tensor decomposition and stimulus category-constraint information. The model was verified on real neuroimaging data obtained via magnetoencephalograph and functional mangetic resonance imaging). Experimental results show that the s-TBN model achieve accuracy matrices of greater than 11.06% and 18.46% on the accuracy matrix compared with other methods on two modal datasets. These results prove the superiority of extracting discriminative characteristics using the STN model, especially for decoding object stimuli with semantic information.

Functional magnetic resonance imaging alternations in suicide attempts individuals and their association with gene expression

BackgroundFunctional Magnetic Resonance Imaging (fMRI) has shown brain activity alterations in individuals with a history of attempted suicide (SA) who are diagnosed with depression disorder (DD) or bipolar disorder (BD). However, patterns of spontaneous brain activity and their genetic correlations need further investigation. MethodsA voxel-based meta-analysis of 19 studies including 26 datasets, involving 742 patients with a history of SA and 978 controls (both nonsuicidal patients and healthy controls) was conducted. We examined fMRI changes in SA patients and analyzed the association between these changes and gene expression profiles using data from the Allen Human Brain Atlas by partial least squares regression analysis. ResultsSA patients demonstrated increased spontaneous brain activity in several brain regions including the bilateral inferior temporal gyrus, hippocampus, fusiform gyrus, and right insula, and decreased activity in areas like the bilateral paracentral lobule and inferior frontal gyrus. Additionally, 5,077 genes were identified, exhibiting expression patterns associated with SA-related fMRI alterations. Functional enrichment analyses demonstrated that these SA-related genes were enriched for biological functions including glutamatergic synapse and mitochondrial structure. Concurrently, specific expression analyses showed that these genes were specifically expressed in the brain tissue, in neurons cells, and during early developmental periods. ConclusionOur findings suggest a neurobiological basis for fMRI abnormalities in SA patients with DD or BD, potentially guiding future genetic and therapeutic research.

Variation in the distribution of large-scale spatiotemporal patterns of activity across brain states.

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

Exploring the technological dimension of Autonomous sensory meridian response-induced physiological responses.

In recent years, the scientific community has been captivated by the intriguing Autonomous sensory meridian response (ASMR), a unique phenomenon characterized by tingling sensations originating from the scalp and propagating down the spine. While anecdotal evidence suggests the therapeutic potential of ASMR, the field has witnessed a surge of scientific interest, particularly through the use of neuroimaging techniques including functional magnetic resonance imaging (fMRI) as well as electroencephalography (EEG) and physiological measures such as eye tracking (Pupil Diameter), heart rate (HR), heartbeat-evoked potential (HEP), blood pressure (BP), pulse rates (PR), finger photoplethysmography (PPG), and skin conductance (SC). This article is intended to provide a comprehensive overview of technology's contributions to the scientific elucidation of ASMR mechanisms. A meticulous literature review was undertaken to identify studies that have examined ASMR using EEG and physiological measurements. The comprehensive search was conducted across databases such as PUBMED, SCOPUS, and IEEE, using a range of relevant keywords such as 'ASMR', 'Autonomous sensory meridian response', 'EEG', 'fMRI', 'electroencephalography', 'physiological measures', 'heart rate', 'skin conductance', and 'eye tracking'. This rigorous process yielded a substantial number of 63 PUBMED and 166 SCOPUS-related articles, ensuring the inclusion of a wide range of high-quality research in this review. The review uncovered a body of research utilizing EEG and physiological measures to explore ASMR's effects. EEG studies have revealed distinct patterns of brain activity associated with ASMR experiences, particularly in regions implicated in emotional processing and sensory integration. In physiological measurements, a decrease in HR and an increase in SC and pupil diameter indicate relaxation and increased attention during ASMR-triggered stimuli. The findings of this review underscore the significance of EEG and physiological measures in unraveling the psychological and physiological effects of ASMR. ASMR experiences have been associated with unique neural signatures, while physiological measures provide valuable insights into the autonomic responses elicited by ASMR stimuli. This review not only highlights the interdisciplinary nature of ASMR research but also emphasizes the need for further investigation to elucidate the mechanisms underlying ASMR and explore its potential therapeutic applications, thereby paving the way for the development of novel therapeutic interventions.

Adversarially Trained Persistent Homology Based Graph Convolutional Network for Disease Identification Using Brain Connectivity.

Brain disease propagation is associated with characteristic alterations in the structural and functional connectivity networks of the brain. To identify disease-specific network representations, graph convolutional networks (GCNs) have been used because of their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks. However, existing GCNs generally focus on learning the discriminative region of interest (ROI) features, often ignoring important topological information that enables the integration of connectome patterns of brain activity. In addition, most methods fail to consider the vulnerability of GCNs to perturbations in network properties of the brain, which considerably degrades the reliability of diagnosis results. In this study, we propose an adversarially trained persistent homology-based graph convolutional network (ATPGCN) to capture disease-specific brain connectome patterns and classify brain diseases. First, the brain functional/structural connectivity is constructed using different neuroimaging modalities. Then, we develop a novel strategy that concatenates the persistent homology features from a brain algebraic topology analysis with readout features of the global pooling layer of a GCN model to collaboratively learn the individual-level representation. Finally, we simulate the adversarial perturbations by targeting the risk ROIs from clinical prior, and incorporate them into a training loop to evaluate the robustness of the model. The experimental results on three independent datasets demonstrate that ATPGCN outperforms existing classification methods in disease identification and is robust to minor perturbations in network architecture. Our code is available at https://github.com/CYB08/ATPGCN.

An fMRI Study of How Deaf Children Process the Two Tones (Second Tone and Third Tone) in Mandarin Chinese

The objective of this study is to investigate the brain activity patterns of deaf children and hearing children during the processing of two different tones (second tone and third tone) using resting-state functional Magnetic Resonance Imaging (fMRI). Furthermore, the study aims to identify the differences in brain activation regions between deaf children and hearing children during the tone processing task. Five deaf children and two hearing children were selected as participants. Resting-state functional Magnetic Resonance Imaging (fMRI) scans were conducted on the subjects using an fMRI scanner. The acquired fMRI data were then preprocessed and analyzed to examine the patterns of brain activity. Deaf children and hearing children exhibit differences in brain activation regions during the execution of tone recognition tasks. These differences can be observed in various areas such as the pre-central gyrus, superior temporal gyrus, middle occipital gyrus, supplementary motor area, superior parietal lobe, and interior frontal gyrus, among others. Through comparisons, the brains of deaf and mute children, although they exhibit relatively reduced activation in certain areas of the auditory cortex, may show enhanced activation in additional regions such as the middle occipital gyrus. This suggests that the brain of deaf children may undergo a reorganization of its functional networks, allowing for improved spatial perception, visual abilities, and other skills. This adaptation enables them to better process tonal information despite the absence of auditory input. These findings contribute to the understanding of the neural basis of tone processing and may help in refining intervention strategies.

EEG microstates are associated with music training experience.

Music training facilitates the development of individual cognitive functions and influences brain plasticity. A comprehensive understanding of the pathways and processes through which music affects the human brain, as well as the neurobiological mechanisms underlying human brain perception of music, is necessary to fully harness the plasticity that music offers for brain development. To investigate the resting-state electroencephalogram (EEG) activity of individuals with and without music training experience, and explore the microstate patterns of EEG signals. In this study, an analysis of electroencephalogram (EEG) microstates from 57 participants yielded temporal parameters(mean duration, time coverage, occurrence, and transition probability)of four classic microstate categories (Categories A, B, C, and D) for two groups: those with music training experience and those without. Statistical analysis was conducted on these parameters between groups. The results indicate that compared to individuals without music training experience, participants with music training experience exhibit significantly longer mean durations of microstate A, which is associated with speech processing. Additionally, they show a greater time coverage of microstate B, which is associated with visual processing. Transition probabilities from microstate A to microstate B were greater in participants with music training experience compared to those without. Conversely, transition probabilities from microstate A to microstate C and from microstate C to microstate D were greater in participants without music training experience. Our study found differences in characteristic parameters of certain microstates between individuals with and without music training experience. This suggests distinct brain activity patterns during tasks related to speech, vision, and attention regulation among individuals with varying levels of music training experience. These findings support an association between music training experience and specific neural activities. Furthermore, they endorse the hypothesis of music training experience influencing brain activity during resting states. Additionally, they imply a facilitative role of music training in tasks related to speech, vision, and attention regulation, providing initial evidence for further empirical investigation into the cognitive processes influenced by music training.

Functional network centrality indicates interactions between APOE4 and age across the clinical spectrum of Alzheimer’s Disease

Advanced age is the most important risk factor for Alzheimer’s disease (AD), and carrier-status of the Apolipoprotein E4 (APOE4) allele is the strongest known genetic risk factor. Many studies have consistently shown a link between APOE4 and synaptic dysfunction, possibly reflecting pathologically accelerated biological aging in persons at risk for AD.To test the hypothesis that distinct functional connectivity patterns characterize APOE4 carriers across the clinical spectrum of AD, we investigated 128 resting state functional Magnetic Resonance Imaging (fMRI) datasets from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI), representing all disease stages from cognitive normal to clinical dementia. Brain region centralities within functional networks, computed as eigenvector centrality, were tested for multivariate associations with chronological age, APOE4 carrier status and clinical stage (as well as their interactions) by partial least square analysis (PLSC).By PLSC analysis two distinct brain activity patterns could be identified, which reflected interactive effects of age, APOE4 and clinical disease stage. A first component including sensorimotor regions and parietal regions correlated with age and AD clinical stage (p < 0.001). A second component focused on medial-frontal regions and was specifically related to the interaction between age and APOE4 (p = 0.032).Our findings are consistent with earlier reports on altered network connectivity in APOE4 carriers. Results of our study highlight promise of graph-theory based network centrality to identify brain connectivity linked to genetic risk, clinical stage and age. Our data suggest the existence of brain network activity patterns that characterize APOE4 carriers across clinical stages of AD.

Advancing Our Knowledge of Cognition in Disorders of Consciousness: A Critical Revisit

This narrative review aims to elucidate an emerging state of consciousness termed “cognitive-motor dissociation (CMD),” characterized by brain activity patterns closely resembling those observed in healthy individuals. This distinctive phenomenon is notably evident in patients diagnosed with comatose states, unresponsive wakefulness states, or minimally conscious states. Employing functional magnetic resonance imaging or electroencephalography holds promise for identifying CMD. The emergence of CMD not only introduces a fresh conceptual framework but also provides valuable predictive insights into outcomes and prognoses for individuals with disorders of consciousness. However, the recognition of CMD raises ethical considerations, particularly in evaluating the decision-making capacity regarding life-or-death choices for individuals in this state. Therefore, it is crucial to advocate for further scientific and comprehensive research to enhance our understanding of CMD and navigate its nuanced complexities.

Neuroanatomical mapping of inhibitory attention and working memory with functional magnetic resonance imaging in healthy children

The objective is to produce an average brain activation mapping template in healthy children using functional magnetic resonance imaging (fMRI), with specific paradigms for activating inhibitory attention and working memory functions. A nutritional and neuropsychological evaluation was performed on 87 right-handed children. The inclusion criteria were met by 30 children (15 boys and 15 girls) between 9 and 11 years old, who were studied with fMRI in two inhibitory attention tests (Go/No Go), with food cues, a working memory test (Continuous Performance Test Identical Pairs) and measurement of anatomical volumes. These data were subsequently processed with the FSL-v5 program, with a threshold of p < 0.05 (cluster-wise). The brain areas activated were located using a standard Montreal Neurological Institute brain template and the Harvard-Oxford structural cortical atlas. The inhibitory attention tests showed activation frontal areas predominantly on the right, and the cingulate, parietal and occipital areas, with preponderance in occipital areas in the food cues test. In the Continuous Performance Test-Identical Pairs test, activation was obtained predominantly in the occipital, frontal and parietal areas. Brain activity mapping templates are obtained in healthy children with tests for inhibitory attention, food cues and working memory. The activation areas are mostly those reported in the literature. This provides baseline brain activation patterns for studying pathologies related to inhibitory attention, impulsivity and working memory.