Neural bases of reading fluency: A systematic review and meta-analysis.

Reading fluency, the ability to read quickly and accurately, is a critical marker of successful reading and is notoriously difficult to improve in reading disabled populations. Despite its importance to functional literacy, fluency is a relatively under-studied aspect of reading, and the neural correlates of reading fluency are not well understood. Here, we review the literature of the neural correlates of reading fluency as well as rapid automatized naming (RAN), a task that is robustly related to reading fluency. In a qualitative review of the neuroimaging literature, we evaluated structural and functional MRI studies of reading fluency in readers from a range of skill levels. This was followed by a quantitative activation likelihood estimate (ALE) meta-analysis of fMRI studies of reading speed and RAN measures. We anticipated that reading speed, relative to untimed reading and reading-related tasks, would harness ventral reading pathways that are thought to enable the fast, visual recognition of words. The qualitative review showed that speeded reading taps the entire canonical reading network. The meta-analysis indicated a stronger role of the ventral reading pathway in rapid reading and rapid naming. Both reviews identified regions outside the canonical reading network that contribute to reading fluency, such as the bilateral insula and superior parietal lobule. We suggest that fluent reading engages both domain-specific reading pathways as well as domain-general regions that support overall task performance and discuss future avenues of research to expand our understanding of the neural bases of fluent reading.

Improved brain effective connectivity modelling by dynamic Bayesian networks.

BACKGROUND: If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. NEW METHOD: This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. RESULTS: The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. COMPARISON WITH EXISTING METHODS: The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. CONCLUSIONS: Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

fMRI and gene therapy in adults with CNGB3 mutation.

Achromatopsia is an inherited retinal disease that affects 1 in 30,000-50,000 individuals and is characterised by an absence of functioning cone photoreceptors from birth. This results in severely reduced visual acuity, no colour vision, marked sensitivity to light and involuntary oscillations of the eyes (nystagmus). In most cases, a single gene mutation prevents normal development of cone photoreceptors, with mutations in the CNGB3 or CNGA3 gene being responsible for ∼80 % of all patients with achromatopsia. There are a growing number of studies investigating recovery of cone function after targeted gene therapy. These studies have provided some promise for patients with the CNGA3 mutation, but thus far have found limited or no recovery for patients with the CNGB3 mutation. Here, we developed colour-calibrated visual stimuli designed to isolate cone photoreceptor responses. We combined these with adapted fMRI techniques and pRF mapping to identify if cortical responses to cone-driven signals could be detected in 9 adult patients with the CNGB3 mutation after receiving gene therapy. We did not detect any change in brain activity after gene therapy when the 9 patients were analysed as a group. However, on an individual basis, one patient self-reported a change in colour perception, corroborated by improved performance on a psychophysical task designed to selectively identify cone function. This suggests a level of cone sensitivity that was lacking pre-treatment, further supported by a subtle but reliable change in cortical activity within their primary visual cortex.

Investigating the neural mechanisms of transcranial direct current stimulation effects on human cognition: current issues and potential solutions.

Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future.

Effects of Pollution Burden on Neural Function During Implicit Emotion Regulation and Longitudinal Changes in Depressive Symptoms in Adolescents.

Background: Exposure to environmental pollutants early in life has been associated with increased prevalence and severity of depression in adolescents; however, the neurobiological mechanisms underlying this association are not well understood. In the current longitudinal study, we investigated whether pollution burden in early adolescence (9-13 years) was associated with altered brain activation and connectivity during implicit emotion regulation and changes in depressive symptoms across adolescence. Methods: One hundred forty-five participants (n = 87 female; 9-13 years) provided residential addresses, from which we determined their relative pollution burden at the census tract level, and performed an implicit affective regulation task in the scanner. Participants also completed questionnaires assessing depressive symptoms at 3 time points, each approximately 2 years apart, from which we calculated within-person slopes of depressive symptoms. We conducted whole-brain activation and connectivity analyses to examine whether pollution burden was associated with alterations in brain function during implicit emotion regulation of positively and negatively valenced stimuli and how these effects were related to slopes of depressive symptoms across adolescence. Results: Greater pollution burden was associated with greater bilateral medial prefrontal cortex activation and stronger bilateral medial prefrontal cortex connectivity with regions within the default mode network (e.g., temporoparietal junction, posterior cingulate cortex, precuneus) during implicit regulation of negative emotions, which was associated with greater increases in depressive symptoms across adolescence in those exposed to higher pollution burden. Conclusions: Adolescents living in communities characterized by greater pollution burden showed altered default mode network functioning during implicit regulation of negative emotions that was associated with increases in depressive symptoms across adolescence.

Exposure to environmental pollution is related to increased risk for depression in youth; however, the neurobiological mechanisms underlying this association are unknown. We found that adolescents living in neighborhoods with greater census tract­level pollution burden had stronger functional connectivity between the medial prefrontal cortex and regions within the default mode network during implicit regulation of negative emotions, which in turn was associated with greater increases in depressive symptoms across adolescence in these pollution-exposed youths.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Smoking affects global and regional brain entropy in depression patients regardless of depression: Preliminary findings.

OBJECTIVE: This study examines the effect of smoking on global and regional brain entropy in patients with Major Depressive Disorder (MDD), aiming to elucidate the relationship between smoking habits and brain network complexity in depression. METHODS: The study enrolled 24 MDD patients, divided into smokers and non-smokers, from Alanya Alaaddin Keykubat University and Istanbul Medipol University. Resting-state fMRI data were acquired and processed. The complexity of neuronal activity was assessed using dispersion entropy, with statistical significance determined by a suite of tests including Kolmogorov-Smirnov, Student's t-test, and Mann-Whitney U test. RESULTS: The smoking cohort exhibited higher global brain entropy compared to the non-smoking group (p = 0.033), with significant differences in various brain networks, indicating that smoking may alter global brain activity and network dynamics in individuals with MDD. CONCLUSION: The study provides evidence that smoking is associated with increased brain entropy in MDD patients, suggesting that chronic smoking may influence cognitive and emotional networks. This underscores the importance of considering smoking history in the treatment and prognosis of MDD. The findings call for further research to understand the mechanistic links between smoking, brain entropy, and depression.

Conscious vision in blindness: A new perceptual phenomenon implemented on the "wrong" side of the brain.

Patients with lesions in the visual cortex are blind in corresponding regions of the visual field, but they still may process visual information, a phenomenon referred to as residual vision or "blindsight". Here we report behavioral and fMRI observations with a patient who reports conscious vision across an extended area of blindness for moving, but not for stationary stimuli. This completion effect is shown to be of perceptual and not of conceptual origin, most likely mediated by spared representations of the visual field in the striate cortex. The neural output to extra-striate areas from regions of the deafferented striate cortex is apparently still intact; this is, for instance, indicated by preserved size constancy of visually completed stimuli. Neural responses as measured with fMRI reveal an activation only for moving stimuli, but importantly on the ipsilateral side of the brain. In a conceptual model this shift of activation to the "wrong" hemisphere is explained on the basis of an imbalance of excitatory and inhibitory interactions within and between the striate cortices due to the brain injury. The observed neuroplasticity indicated by this shift together with the behavioral observations provide important new insights into the functional architecture of the human visual system and provide new insight into the concept of consciousness.

Neuroinflammation Parallels 18F-PI-2620 Positron Emission Tomography Patterns in Primary 4-Repeat Tauopathies.

BACKGROUND: Preclinical, postmortem, and positron emission tomography (PET) imaging studies have pointed to neuroinflammation as a key pathophysiological hallmark in primary 4-repeat (4R) tauopathies and its role in accelerating disease progression. OBJECTIVE: We tested whether microglial activation (1) progresses in similar spatial patterns as the primary pathology tau spreads across interconnected brain regions, and (2) whether the degree of microglial activation parallels tau pathology spreading. METHODS: We examined in vivo associations between tau aggregation and microglial activation in 31 patients with clinically diagnosed 4R tauopathies, using 18F-PI-2620 PET and 18F-GE180 (translocator protein [TSPO]) PET. We determined tau epicenters, defined as subcortical brain regions with highest tau PET signal, and assessed the connectivity of tau epicenters to cortical regions of interest using a 3-T resting-state functional magnetic resonance imaging template derived from age-matched healthy elderly controls. RESULTS: In 4R tauopathy patients, we found that higher regional tau PET covaries with elevated TSPO-PET across brain regions that are functionally connected to each other (ß = 0.414, P < 0.001). Microglial activation follows similar distribution patterns as tau and distributes primarily across brain regions strongly connected to patient-specific tau epicenters (ß = -0.594, P < 0.001). In these regions, microglial activation spatially parallels tau distribution detectable with 18F-PI-2620 PET. CONCLUSIONS: Our findings indicate that the spatial expansion of microglial activation parallels tau distribution across brain regions that are functionally connected to each other, suggesting that tau and inflammation are closely interrelated in patients with 4R tauopathies. The combination of in vivo tau and inflammatory biomarkers could therefore support the development of immunomodulatory strategies for disease-modifying treatments in these conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Two fundamentally different mechanisms by which unconscious information impairs behavioral performance: Evidence from fMRI and computational modeling.

It is increasingly clear that unconscious information impairs the performance of the corresponding action when the instruction to act is delayed. However, whether this impairment occurs at the response level or at the perceptual level remains controversial. This study used fMRI and a computational model with a pre-post design to address this elusive issue. The fMRI results showed that when the unconscious information containing strong stimulus-response associations was irrelevant to subsequent stimuli, the precuneus in the parietal lobe, which is thought to be involved in sensorimotor processing, was activated. In contrast, when the unconscious information was relevant to subsequent stimuli, regardless of the strength of the stimulus-response associations, some regions in the occipital and temporal cortices, which are thought to be involved in visual perceptual processing, were activated. In addition, the percent signal change in the regions of interest associated with motor inhibition was modulated by compatibility in the irrelevant but not in the relevant stimuli conditions. Modeling of behavioral data further supported that the irrelevant and relevant stimuli conditions involved fundamentally different mechanisms. Our finding reconciles the debate about the mechanism by which unconscious information impairs action performance and has important implications for understanding of unconscious cognition.

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

DeepRetroMoCo: deep neural network-based retrospective motion correction algorithm for spinal cord functional MRI.

Background and purpose: There are distinct challenges in the preprocessing of spinal cord fMRI data, particularly concerning the mitigation of voluntary or involuntary movement artifacts during image acquisition. Despite the notable progress in data processing techniques for movement detection and correction, applying motion correction algorithms developed for the brain cortex to the brainstem and spinal cord remains a challenging endeavor. Methods: In this study, we employed a deep learning-based convolutional neural network (CNN) named DeepRetroMoCo, trained using an unsupervised learning algorithm. Our goal was to detect and rectify motion artifacts in axial T2*-weighted spinal cord data. The training dataset consisted of spinal cord fMRI data from 27 participants, comprising 135 runs for training and 81 runs for testing. Results: To evaluate the efficacy of DeepRetroMoCo, we compared its performance against the sct_fmri_moco method implemented in the spinal cord toolbox. We assessed the motion-corrected images using two metrics: the average temporal signal-to-noise ratio (tSNR) and Delta Variation Signal (DVARS) for both raw and motion-corrected data. Notably, the average tSNR in the cervical cord was significantly higher when DeepRetroMoCo was utilized for motion correction, compared to the sct_fmri_moco method. Additionally, the average DVARS values were lower in images corrected by DeepRetroMoCo, indicating a superior reduction in motion artifacts. Moreover, DeepRetroMoCo exhibited a significantly shorter processing time compared to sct_fmri_moco. Conclusion: Our findings strongly support the notion that DeepRetroMoCo represents a substantial improvement in motion correction procedures for fMRI data acquired from the cervical spinal cord. This novel deep learning-based approach showcases enhanced performance, offering a promising solution to address the challenges posed by motion artifacts in spinal cord fMRI data.

Multivariate pattern analysis of cooperation and competition in constructive action.

The neural underpinning of cooperative and competitive constructive activity has been investigated using mass-univariate approaches. In this study, we sought to compare the results of these approaches with the results of multivariate pattern analysis (MVPA). In particular, we wanted to test whether MVPA supports the claim made in previous studies that cooperation is associated with the activity of reward-related brain circuits. Participants were required to construct a pattern on the screen either individually or in cooperation or competition with another person during an fMRI scan. Both the MVPA classification methods and the representational similarity analysis indicated the involvement of orbitofrontal and ventromedial prefrontal areas in processes that distinguish between cooperation and competition, and activation analysis showed that these areas are more active during cooperation than during competition. However, a single trial analysis showed that the effect was reversed when only winning trials were considered. In these trials, activation of reward-related areas was higher during competition than during cooperation. Moreover, the contrast between won and lost trials in terms of reward circuits involvement was sharper under competition than under cooperation. Thus, although cooperation can be generally more rewarding than competition, it is associated with smaller difference between trials lost and trials won in terms of reward circuits activation. One may speculate that in cooperation, victory and defeat are shared with the partner and, contrary to competition, are not experienced as personal achievement or failure.

Identifying subgroups of urge suppression in Obsessive-Compulsive Disorder using machine learning.

Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous. While predominant models suggest fear and harm prevention drive compulsions, many patients also experience uncomfortable sensory-based urges ("sensory phenomena") that may be associated with heightened interoceptive sensitivity. Using an urge-to-blink eyeblink suppression paradigm to model sensory-based urges, we previously found that OCD patients as a group had more eyeblink suppression failures and greater activation of sensorimotor-interoceptive regions than controls. However, conventional approaches assuming OCD homogeneity may obscure important within-group variability, impeding precision treatment development. This study investigated the heterogeneity of urge suppression failure in OCD and examined relationships with clinical characteristics and neural activation. Eighty-two patients with OCD and 38 controls underwent an fMRI task presenting 60-s blocks of eyeblink suppression alternating with free-blinking blocks. Latent profile analysis identified OCD subgroups based on number of erroneous blinks during suppression. Subgroups were compared on behavior, clinical characteristics, and brain activation during task. Three patient subgroups were identified. Despite similar overall OCD severity, the subgroup with the most erroneous eyeblinks had the highest sensory phenomena severity, interoceptive sensitivity, and subjective urge intensity. Compared to other subgroups, this subgroup exhibited more neural activity in somatosensory and interoceptive regions during the early phase (first 30 s) of blink suppression and reduced activity in the middle frontal gyrus during the late phase (second 30 s) as the suppression period elapsed. Heterogeneity of urge suppression in OCD was associated with clinical characteristics and brain function. Our results reveal potential treatment targets that could inform personalized medicine.

Primary somatosensory cortical processing in tactile communication.

Touch is an essential form of non-verbal communication. While language and its neural basis are widely studied, tactile communication is less well understood. We used fMRI and multivariate pattern analyses in pairs of emotionally close adults to examine the neural basis of human-to-human tactile communication. In each pair, a participant was designated either as sender or as receiver. The sender was instructed to communicate specific messages by touching only the arm of the receiver, who was inside the scanner. The receiver then identified the message based on the touch expression alone. We designed two multivariate decoder algorithms-one based on the sender's intent (sender-decoder), and another based on the receiver's response (receiver-decoder). We identified several brain areas that significantly predicted behavioural accuracy of the receiver. Regarding our a priori region of interest, the receiver's primary somatosensory cortex (S1), both decoders were able to accurately differentiate the messages based on neural activity patterns here. The receiver-decoder, which relied on the receivers' interpretations of the touch expressions, outperformed the sender-decoder, which relied on the sender's intent. Our results identified a network of brain areas involved in human-to-human tactile communication and supported the notion of non-sensory factors being represented in S1. This article is part of the theme issue 'Sensing and feeling: an integrative approach to sensory processing and emotional experience'.

Utilizing connectome fingerprinting functional MRI models for motor activity prediction in presurgical planning: A feasibility study.

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

State of the art of brain function detection technologies in robot-assisted lower limb rehabilitation.

BACKGROUND: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). OBJECTIVE: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. METHODS: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies (BFDT). RESULTS: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks, but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. CONCLUSION: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks.

Altered resting-state brain entropy in cerebral small vessel disease patients with cognitive impairment.

OBJECTIVE: Cerebral small vessel disease (CSVD) is a primary vascular disease of cognitive impairment. Previous studies have predominantly focused on brain linear features. However, the nonlinear measure, brain entropy (BEN), has not been elaborated. Thus, this study is aim to investigate if BEN abnormalities could manifest in CSVD patients with cognitive impairment. METHOD: 34 CSVD patients with cognitive impairment and 37 healthy controls (HCs) were recruited. Analysis of gray matter approximate entropy (ApEn) and sample entropy (SampEn) which are two indices of BEN were calculated. To explore whether BEN can provide unique information, we further performed brain linear methods, namely amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), to observe their differences. The ratios of BEN/ALFF and BEN/ReHo which represent the coupling of nonlinear and linear features were introduced. Correlation analysis was conducted between imaging indices and cognition. Subsequently, the linear support vector machine (SVM) was used to assess their discriminative ability. RESULTS: CSVD patients exhibited lower ApEn and SamEn value in sensorimotor areas, which were correlated with worse memory and executive function. Additionally, the results of BEN showed little overlap with ALFF and ReHo in brain regions. Correlation analysis also revealed a relationship between the two ratios and cognition. SVM analysis utilizing BEN and its ratios as features achieved an accuracy of 74.64 % (sensitivity: 86.49 %; specificity: 61.76 %; and AUC: 0.82439). CONCLUSION: Our study reveals that the reduction of sensorimotor system complexity is correlated with cognition. BEN exhibits distinctive characteristics in brain activity. Combining BEN and the ratios can be new biomarkers to diagnose CSVD with cognitive impairment.

Hippocampal activations obtained during language fMRI tasks: A complementary tool for predicting postoperative memory prognosis.

In medial temporal lobe epilepsy (MTLE), the benefits of surgery must be balanced against the risk of post-operative memory decline. Prediction of postoperative outcomes based on functional magnetic resonance imaging (fMRI) tasks is increasingly common but remains uncertain. The aim of this retrospective study was to determine whether hippocampal activations elicited by fMRI language tasks could enhance or refine memory fMRI in MTLE patients candidates to surgery. Forty-six patients were included: 30 right and 16 left MTLE, mostly with hippocampal sclerosis. Preoperative assessment included neuropsychological tests and fMRI with language (syntactic verbal fluency) and memory tasks (encoding, delayed, and immediate recognition of images of objects). Thirty patients underwent surgery and had neuropsychological evaluations one year after surgery. Worsening was defined as a degradation of more than 10 % in postoperative forgetting scores compared to preoperative scores in verbal, non-verbal and global memory. Memory fMRI had the best sensitivity with hippocampal activations obtained in 95 % of patients, versus 65 % with language fMRI. Considering the patients who elicited an hippocampal activation, language fMRI led to 80 %, 65 % and 85 % of correct predictions for respectively global, verbal and non verbal memory (versus 71 %, 64 % and 68 % with memory fMRI). Memory and language fMRI predictions outperformed those made by neuropsychological tests. In summary, language fMRI was less sensitive than memory fMRI to elicit hippocampal activations but when it did, the proportion of correct memory predictions was better. Moreover, it proved to be an independent predictive factor regardless of the side of the epileptic focus. Given the ease of setting up a language task in fMRI, we recommend the systematic combination of memory and language tasks to predict the post-operative memory outcome of MTLE patients undergoing epilepsy surgery.

How sexual objectification marks the brain: fMRI evidence of self-objectification and its harmful emotional consequences.

Female Sexual Objectification refers to perceiving and treating women based on their body appearance. This phenomenon may serve as a precursor for dysfunctional behaviors, particularly among females prone to self-objectification and experiencing shame emotions. Understanding this challenging trajectory by disclosing its neural consequences may be crucial for comprehending extreme psychopathological outcomes. However, investigations in this sense are still scarce. The present study explores the neural correlates of female participants' experiences of being objectified and their relationship with self-objectification, emotional responses and individual dispositions in self-esteem, emotion regulation abilities and self-conscious emotion proneness. To this aim, 25 female participants underwent an fMRI experimental session while they were exposed to interpersonal encounters with objectifying or non-objectifying men. Participants' experienced emotions and levels of attention shifted toward their bodies (self-objectification) was reported after each interaction. The results revealed increased brain activity in objectifying contexts, impacting cortical (frontal, occipital and temporal cortex) and subcortical regions (thalamus, and hippocampus) involved in visual, emotion, and social processing. Remarkably, the inferior temporal gyrus emerged as a crucial neural hub associated in opposite ways with self-esteem and the self-conscious emotion of shame, highlighting its role in self-referential processing during social dynamics. This study points out the importance of adopting a neuroscientific perspective for a deeper understanding of sexual objectification, and to shed light on its possible neural consequences.