The salience network is activated during self‐recognition from both first‐person and third‐person perspectives

We usually observe ourselves from two perspectives. One is the first‐person perspective, which we perceive directly with our own eyes, and the other is the third‐person perspective, which we observe ourselves in a mirror or a picture. However, whether the self‐recognition associated with these two perspectives has a common or separate neural basis remains unclear. To address this, we used functional magnetic resonance imaging to examine brain activity while participants viewed pretaped video clips of themselves and others engaged in meal preparation taken from first‐person and third‐person perspectives. We found that the first‐person behavioral videos of the participants and others induced greater activation in the premotor‐intraparietal region. In contrast, the third‐person behavioral videos induced greater activation in the default mode network compared with the first‐person videos. Regardless of the perspective, the videos of the participants induced greater activation in the salience network than the videos of others. On the other hand, the videos of others induced greater activation in the precuneus and lingual gyrus than the videos of the participants. These results suggest that the salience network is commonly involved in self‐recognition from both perspectives, even though the brain regions involved in action observation for the two perspectives are distinct.     

Exploring the relationship between anthropomorphism and theory‐of‐mind in brain and behaviour

The process of understanding the minds of other people, such as their emotions and intentions, is mimicked when individuals try to understand an artificial mind. The assumption is that anthropomorphism, attributing human‐like characteristics to non‐human agents and objects, is an analogue to theory‐of‐mind, the ability to infer mental states of other people. Here, we test to what extent these two constructs formally overlap. Specifically, using a multi‐method approach, we test if and how anthropomorphism is related to theory‐of‐mind using brain (Experiment 1) and behavioural (Experiment 2) measures. In a first exploratory experiment, we examine the relationship between dispositional anthropomorphism and activity within the theory‐of‐mind brain network (n = 108). Results from a Bayesian regression analysis showed no consistent relationship between dispositional anthropomorphism and activity in regions of the theory‐of‐mind network. In a follow‐up, pre‐registered experiment, we explored the relationship between theory‐of‐mind and situational and dispositional anthropomorphism in more depth. Participants (n = 311) watched a short movie while simultaneously completing situational anthropomorphism and theory‐of‐mind ratings, as well as measures of dispositional anthropomorphism and general theory‐of‐mind. Only situational anthropomorphism predicted the ability to understand and predict the behaviour of the film''s characters. No relationship between situational or dispositional anthropomorphism and general theory‐of‐mind was observed. Together, these results suggest that while the constructs of anthropomorphism and theory‐of‐mind might overlap in certain situations, they remain separate and possibly unrelated at the personality level. These findings point to a possible dissociation between brain and behavioural measures when considering the relationship between theory‐of‐mind and anthropomorphism.     

Mapping of whole‐cerebrum resting‐state networks using ultra‐high resolution acquisition protocols

Resting‐state functional magnetic resonance imaging (fMRI) has been used in numerous studies to map networks in the brain that employ spatially disparate regions. However, attempts to map networks with high spatial resolution have been hampered by conflicting technical demands and associated problems. Results from recent fMRI studies have shown that spatial resolution remains around 0.7 × 0.7 × 0.7 mm³, with only partial brain coverage. Therefore, this work aims to present a novel fMRI technique that was developed based on echo‐planar‐imaging with keyhole (EPIK) combined with repetition‐time‐external (TR‐external) EPI phase correction. Each technique has been previously shown to be effective in enhancing the spatial resolution of fMRI, and in this work, the combination of the two techniques into TR‐external EPIK provided a nominal spatial resolution of 0.51 × 0.51 × 1.00 mm³ (0.26 mm³ voxel) with whole‐cerebrum coverage. Here, the feasibility of using half‐millimetre in‐plane TR‐external EPIK for resting‐state fMRI was validated using 13 healthy subjects and the corresponding reproducible mapping of resting‐state networks was demonstrated. Furthermore, TR‐external EPIK enabled the identification of various resting‐state networks distributed throughout the brain from a single fMRI session, with mapping fidelity onto the grey matter at 7T. The high‐resolution functional image further revealed mesoscale anatomical structures, such as small cerebral vessels and the internal granular layer of the cortex within the postcentral gyrus.     

Cortico‐striato‐thalamo‐cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic–clonic seizures

Generalized tonic–clonic seizures (GTCS) are the severest and most remarkable clinical expressions of human epilepsy. Cortical, subcortical, and cerebellar structures, organized with different network patterns, underlying the pathophysiological substrates of genetic associated epilepsy with GTCS (GE‐GTCS) and focal epilepsy associated with focal to bilateral tonic–clonic seizure (FE‐FBTS). Structural covariance analysis can delineate the features of epilepsy network related with long‐term effects from seizure. Morphometric MRI data of 111 patients with GE‐GTCS, 111 patients with FE‐FBTS and 111 healthy controls were studied. Cortico‐striato‐thalao‐cerebellar networks of structural covariance within the gray matter were constructed using a Winner‐take‐all strategy with five cortical parcellations. Comparisons of structural covariance networks were conducted using permutation tests, and module effects of disease duration on networks were conducted using GLM model. Both patient groups showed increased connectivity of structural covariance relative to controls, mainly within the striatum and thalamus, and mostly correlated with the frontal, motor, and somatosensory cortices. Connectivity changes increased as a function of epilepsy durations. FE‐FBTS showed more intensive and extensive gray matter changes with volumetric loss and connectivity increment than GE‐GTCS. Our findings implicated cortico‐striato‐thalamo‐cerebellar network changes at a large temporal scale in GTCS, with FE‐FBTS showing more severe network disruption. The study contributed novel imaging evidence for understanding the different epilepsy syndromes associated with generalized seizures.     

Dynamic representations of race: processing goals shape race decoding in the fusiform gyri

People perceive and evaluate others on the basis of social categories, such as race, gender and age. Initial processing of targets in terms of visually salient social categories is often characterized as inevitable. In the current study, we investigated the influence of processing goals on the representation of race in the visual processing stream. Participants were assigned to one of two mixed-race teams and categorized faces according to their group membership or skin color. To assess neural representations of race, we employed multivariate pattern analysis to examined neural activity related to the presentation of Black and White faces. As predicted, patterns of neural activity within the early visual cortex and fusiform gyri (FG) could decode the race of face stimuli above chance and were moderated by processing goals. Race decoding in early visual cortex was above chance in both categorization tasks and below chance in a prefrontal control region. More importantly, race decoding was greater in the FG during the group membership vs skin color categorization task. The results suggest that, ironically, explicit racial categorization can diminish the representation of race in the FG. These findings suggest that representations of race are dynamic, reflecting current processing goals.     

Reliability of task‐evoked neural activation during face‐emotion paradigms: Effects of scanner and psychological processes

Assessing and improving test–retest reliability is critical to efforts to address concerns about replicability of task‐based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task‐related factors influence reliability of neural response to face‐emotion viewing. Forty healthy adult participants completed two face‐emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed‐effects models performed voxel‐wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole‐brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal‐to‐noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel‐wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers'' selection of task conditions and analytic contrasts.     

Hemodynamic correlates of emotion regulation in frontal lobe epilepsy patients and healthy participants

The ability to regulate emotions is indispensable for maintaining psychological health. It heavily relies on frontal lobe functions which are disrupted in frontal lobe epilepsy. Accordingly, emotional dysregulation and use of maladaptive emotion regulation strategies have been reported in frontal lobe epilepsy patients. Therefore, it is of clinical and scientific interest to investigate emotion regulation in frontal lobe epilepsy. We studied neural correlates of upregulating and downregulating emotions toward aversive pictures through reappraisal in 18 frontal lobe epilepsy patients and 17 healthy controls using functional magnetic resonance imaging. Patients tended to report more difficulties with impulse control than controls. On the neural level, patients had diminished activity during upregulation in distributed left‐sided regions, including ventrolateral and dorsomedial prefrontal cortex, angular gyrus and anterior temporal gyrus. Patients also showed less activity than controls in the left precuneus for upregulation compared to downregulation. Unlike controls, they displayed no task‐related activity changes in the left amygdala, whereas the right amygdala showed task‐related modulations in both groups. Upregulation‐related activity changes in the left inferior frontal gyrus, insula, orbitofrontal cortex, anterior and posterior cingulate cortex, and precuneus were correlated with questionnaire data on habitual emotion regulation. Our results show that structural or functional impairments in the frontal lobes disrupt neural mechanisms underlying emotion regulation through reappraisal throughout the brain, including posterior regions involved in semantic control. Findings on the amygdala as a major target of emotion regulation are in line with the view that specifically the left amygdala is connected with semantic processing networks.     

Gamma‐band neural coupling during conceptual alignment

Conceptual alignment is a prerequisite for mutual understanding. However, little is known about the neurophysiological brain‐to‐brain underpinning during conceptual alignment for mutual understanding. Here, we recorded multi‐channel electroencephalogram (EEG) simultaneously from two participants in Experiment 1 and adopted the dual‐tACS techniques in Experiment 2 to investigate the underlying brain‐to‐brain EEG coupling during conceptual alignment and the possible enhancement effect. Our results showed that 1) higher phase‐locking value (PLV), a sensitive measure for quantifying neural coupling strength between EEG signals, at the gamma frequency band (28–40 Hz), was observed in the left temporoparietal site (left TP) area between successful versus unsuccessful conceptual alignment. The left TP gamma coupling strength correlated with the accuracy of conceptual alignment and differentiated whether subjects belonged to the SUCCESS or FAILURE groups in our study. 2) In‐phase gamma‐band transcranial alternating current stimulation (tACS) over the left TP area increased the accuracy of subjects in the SUCCESS group but not the FAILURE group. 3) The effect of perspective‐taking on the accuracy was mediated by the gamma coupling strength within the left TP area. Our results support the role of gamma‐band coupling between brains for interpersonal conceptual alignment. We provide dynamic interpersonal neurophysiological insights into the formation of successful communication.     

Taking the body off the mind: Decreased functional connectivity between somatomotor and default‐mode networks following Floatation‐REST

Floatation‐Reduced Environmental Stimulation Therapy (REST) is a procedure that reduces stimulation of the human nervous system by minimizing sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational, and proprioceptive channels, in addition to minimizing musculoskeletal movement and speech. Initial research has found that Floatation‐REST can elicit short‐term reductions in anxiety, depression, and pain, yet little is known about the brain networks impacted by the intervention. This study represents the first functional neuroimaging investigation of Floatation‐REST, and we utilized a data‐driven exploratory analysis to determine whether the intervention leads to altered patterns of resting‐state functional connectivity (rsFC). Healthy participants underwent functional magnetic resonance imaging (fMRI) before and after 90 min of Floatation‐REST or a control condition that entailed resting supine in a zero‐gravity chair for an equivalent amount of time. Multivariate Distance Matrix Regression (MDMR), a statistically‐stringent whole‐brain searchlight approach, guided subsequent seed‐based connectivity analyses of the resting‐state fMRI data. MDMR identified peak clusters of rsFC change between the pre‐ and post‐float fMRI, revealing significant decreases in rsFC both within and between posterior hubs of the default‐mode network (DMN) and a large swath of cortical tissue encompassing the primary and secondary somatomotor cortices extending into the posterior insula. The control condition, an active form of REST, showed a similar pattern of reduced rsFC. Thus, reduced stimulation of the nervous system appears to be reflected by reduced rsFC within the brain networks most responsible for creating and mapping our sense of self.     

Age‐specificity and generalization of behavior‐associated structural and functional networks and their relevance to behavioral domains

Behavior‐associated structural connectivity (SC) and resting‐state functional connectivity (rsFC) networks undergo various changes in aging. To study these changes, we proposed a continuous dimension where at one end networks generalize well across age groups in terms of behavioral predictions (age‐general) and at the other end, they predict behaviors well in a specific age group but fare poorly in another age group (age‐specific). We examined how age generalizability/specificity of multimodal behavioral associated brain networks varies across behavioral domains and imaging modalities. Prediction models consisting of SC and/or rsFC networks were trained to predict a diverse range of 75 behavioral outcomes in a young adult sample (N = 92). These models were then used to predict behavioral outcomes in unseen young (N = 60) and old (N = 60) subjects. As expected, behavioral prediction models derived from the young age group, produced more accurate predictions in the unseen young than old subjects. These behavioral predictions also differed significantly across behavioral domains, but not imaging modalities. Networks associated with cognitive functions, except for a few mostly relating to semantic knowledge, fell toward the age‐specific end of the spectrum (i.e., poor young‐to‐old generalizability). These findings suggest behavior‐associated brain networks are malleable to different degrees in aging; such malleability is partly determined by the nature of the behavior.     

Socioeconomic status and depressive symptoms in older people with the mediation role of social support: A population‐based longitudinal study

BackgroundDepressive symptoms has become an increasingly important public health issue, contributing to disability and disease burden around the world. Higher socioeconomic status (SES) has been found to be associated with lower prevalence of depression, but there are few studies about the older Chinese adults with long‐term follow up and rigorous prospective design. Meanwhile, there is little conclusive evidence about the mechanisms through which SES influences the onset of depressive symptoms.ObjectiveTo prospectively examine the association of baseline socioeconomic factors with the risks of developing depressive symptoms during 7‐year follow up in older Chinese population, and to study the mechanism by which SES impacts the prevalence of depressive symptoms.MethodsA total of 5677 individuals over 45 years who participated in an ongoing nationally representative prospective cohort study, China Health and Retirement Longitudinal Study, were free from depressive symptoms at baseline, and completed 7‐year follow‐up were included. Depressive symptoms were assessed using the 10‐item Center for Epidemiological Studies Depression Scale score. Cox proportional hazards regression models were used to examine the association of SES and the incidence of depressive symptoms in 2011 to 2018. Generalized structural equation model was used to analyze the mediation effects of social support on the relation between SES and depressive symptoms.ResultsDuring the 7‐year follow‐up, 2398 (42.2%) cases were identified as depressive symptoms. Compared with the lowest level of household income, participants with the highest level of household income had a 20% reduction in risk (95% CI, 0.70–0.92, P < 0.001). Participants who had junior high school or above education had a 41% lower risk of depressive disorders compared with illiterate participants (95% CI, 0.52–0.69, P < 0.001). The relationship between SES and depressive symptoms was partially mediated by the social support, where higher social support was negatively associated with depressive symptoms. The proportion of mediation effect was even larger for women compared with men.ConclusionSocioeconomic factors were independently associated with the development of depressive symptoms, and the relationship was partially mediated by social support. Social support could be an effective intervention to alleviate the negative effects of lower SES on mental health. Multiple‐level policies should precisely target low‐SES groups, and timely intervention to promote social support for this group should be used to reduce the influence of depression on individuals, family as well as the whole society.     

Updating the dual‐mechanism model for cross‐sensory attentional spreading: The influence of space‐based visual selective attention

Selective attention to visual stimuli can spread cross‐modally to task‐irrelevant auditory stimuli through either the stimulus‐driven binding mechanism or the representation‐driven priming mechanism. The stimulus‐driven attentional spreading occurs whenever a task‐irrelevant sound is delivered simultaneously with a spatially attended visual stimulus, whereas the representation‐driven attentional spreading occurs only when the object representation of the sound is congruent with that of the to‐be‐attended visual object. The current study recorded event‐related potentials in a space‐selective visual object‐recognition task to examine the exact roles of space‐based visual selective attention in both the stimulus‐driven and representation‐driven cross‐modal attentional spreading, which remain controversial in the literature. Our results yielded that the representation‐driven auditory Nd component (200–400 ms after sound onset) did not differ according to whether the peripheral visual representations of audiovisual target objects were spatially attended or not, but was decreased when the auditory representations of target objects were presented alone. In contrast, the stimulus‐driven auditory Nd component (200–300 ms) was decreased but still prominent when the peripheral visual constituents of audiovisual nontarget objects were spatially unattended. These findings demonstrate not only that the representation‐driven attentional spreading is independent of space‐based visual selective attention and benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading.     

Distinct and temporally associated neural mechanisms underlying concurrent,postsuccess, and posterror cognitive controls: Evidence from a stop‐signal task

Cognitive control is built upon the interactions of multiple brain regions. It is currently unclear whether the involved regions are temporally separable in relation to different cognitive processes and how these regions are temporally associated in relation to different task performances. Here, using stop‐signal task data acquired from 119 healthy participants, we showed that concurrent and poststop cognitive controls were associated with temporally distinct but interrelated neural mechanisms. Specifically, concurrent cognitive control activated regions in the cingulo‐opercular network (including the dorsal anterior cingulate cortex [dACC], insula, and thalamus), together with superior temporal gyrus, secondary motor areas, and visual cortex; while regions in the fronto‐parietal network (including the lateral prefrontal cortex [lPFC] and inferior parietal lobule) and cerebellum were only activated during poststop cognitive control. The associations of activities between concurrent and poststop regions were dependent on task performance, with the most notable difference in the cerebellum. Importantly, while concurrent and poststop signals were significantly correlated during successful cognitive control, concurrent activations during erroneous trials were only correlated with posterror activations in the fronto‐parietal network but not cerebellum. Instead, the cerebellar activation during posterror cognitive control was likely to be driven secondarily by posterror activation in the lPFC. Further, a dynamic causal modeling analysis demonstrated that postsuccess cognitive control was associated with inhibitory connectivity from the lPFC to cerebellum, while excitatory connectivity from the lPFC to cerebellum was present during posterror cognitive control. Overall, these findings suggest dissociable but temporally related neural mechanisms underlying concurrent, postsuccess, and posterror cognitive control processes in healthy individuals.     

Voxel‐based diktiometry: Combining convolutional neural networks with voxel‐based analysis and its application in diffusion tensor imaging for Parkinson's disease

Extracting population‐wise information from medical images, specifically in the neurological domain, is crucial to better understanding disease processes and progression. This is frequently done in a whole‐brain voxel‐wise manner, in which a population of patients and healthy controls are registered to a common co‐ordinate space and a statistical test is performed on the distribution of image intensities for each location. Although this method has yielded a number of scientific insights, it is further from clinical applicability as the differences are often small and altogether do not permit for a high‐performing classifier. In this article, we take the opposite approach of using a high‐performing classifier, specifically a traditional convolutional neural network, and then extracting insights from it which can be applied in a population‐wise manner, a method we call voxel‐based diktiometry. We have applied this method to diffusion tensor imaging (DTI) analysis for Parkinson''s disease (PD), using the Parkinson''s Progression Markers Initiative database. By using the network sensitivity information, we can decompose what elements of the DTI contribute the most to the network''s performance, drawing conclusions about diffusion biomarkers for PD that are based on metrics which are not readily expressed in the voxel‐wise approach.     

A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI

Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations.     

MDReg‐Net: Multi‐resolution diffeomorphic image registration using fully convolutional networks with deep self‐supervision

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self‐supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image‐wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi‐resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self‐supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high‐resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state‐of‐the‐art image registration algorithms.     

Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains

We aim to synthesize brain time‐of‐flight (TOF) PET images/sinograms from their corresponding non‐TOF information in the image space (IS) and sinogram space (SS) to increase the signal‐to‐noise ratio (SNR) and contrast of abnormalities, and decrease the bias in tracer uptake quantification. One hundred forty clinical brain ¹⁸F‐FDG PET/CT scans were collected to generate TOF and non‐TOF sinograms. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). The predicted TOF sinogram was reconstructed and the performance of both models (IS and SS) compared with reference TOF and non‐TOF. Wide‐ranging quantitative and statistical analysis metrics, including structural similarity index metric (SSIM), root mean square error (RMSE), as well as 28 radiomic features for 83 brain regions were extracted to evaluate the performance of the CycleGAN model. SSIM and RMSE of 0.99 ± 0.03, 0.98 ± 0.02 and 0.12 ± 0.09, 0.16 ± 0.04 were achieved for the generated TOF‐PET images in IS and SS, respectively. They were 0.97 ± 0.03 and 0.22 ± 0.12, respectively, for non‐TOF‐PET images. The Bland & Altman analysis revealed that the lowest tracer uptake value bias (−0.02%) and minimum variance (95% CI: −0.17%, +0.21%) were achieved for TOF‐PET images generated in IS. For malignant lesions, the contrast in the test dataset was enhanced from 3.22 ± 2.51 for non‐TOF to 3.34 ± 0.41 and 3.65 ± 3.10 for TOF PET in SS and IS, respectively. The implemented CycleGAN is capable of generating TOF from non‐TOF PET images to achieve better image quality.     

Efficacy and safety of 3‐n‐butylphthalide for the treatment of cognitive impairment: A systematic review and meta‐analysis

BackgroundCurrent evidence for the efficacy of pharmacological treatment in improving cognitive function is absent. Recent studies have reported that 3‐n‐butylphthalide (NBP) has a positive effect on improving cognitive impairment; however, its clinical efficacy and safety is unclear. Therefore, we conducted a meta‐analysis to assess its efficacy and safety for cognitive impairment.MethodsWe systematically searched the PubMed, EMBASE, Cochrane Library, Web of Science, and Scopus databases, and two reviewers independently screened and extracted the data from included studies. We synthesized the data using the Review Manager Software version 5.3.ResultsWe included six randomized clinical trials (RCTs), encompassing 851 patients with cognitive impairment. The results showed that NBP improved cognitive impairment. Specifically, the clinical efficacy was better than that in the control group, with better performance in improving the Mini‐Mental State Examination and the Montreal Cognitive Assessment scores, while decreasing the Alzheimer''s Disease Assessment Scale‐Cognitive subscale and the Clinician''s Interview‐Based Impression of Change plus caregiver input scores. There was no significant difference in the incidence of adverse events between both groups.ConclusionThe NBP is effective and safe in improving cognitive impairment; however, more high‐quality RCTs are needed to confirm these findings.     

Social cognition in the blind brain: A coordinate‐based meta‐analysis

Social cognition skills are typically acquired on the basis of visual information (e.g., the observation of gaze, facial expressions, gestures). In light of this, a critical issue is whether and how the lack of visual experience affects neurocognitive mechanisms underlying social skills. This issue has been largely neglected in the literature on blindness, despite difficulties in social interactions may be particular salient in the life of blind individuals (especially children). Here we provide a meta‐analysis of neuroimaging studies reporting brain activations associated to the representation of self and others'' in early blind individuals and in sighted controls. Our results indicate that early blindness does not critically impact on the development of the “social brain,” with social tasks performed on the basis of auditory or tactile information driving consistent activations in nodes of the action observation network, typically active during actual observation of others in sighted individuals. Interestingly though, activations along this network appeared more left‐lateralized in the blind than in sighted participants. These results may have important implications for the development of specific training programs to improve social skills in blind children and young adults.