Caliper-based precise positioning of the target(CALIPPOT)for transcranial magnetic stimulation without neuronavigation system

To the editor:Transcranial magnetic stimulation(TMS)is a non-invasive brain modulation technique.One important usage of TMS is the transient interruption of cognitive brain function(also named virtual lesion)for investigating precisely where and when a specific cortical region contributes to a specific cognitive function.1 A more important usage of TMS is the treatment of brain disorders by repetitive TMS(rTMS).

Rational and Continuous Measurement of Emotional-Fingerprint, Emotional-Quotient and Categorical vs Proportional Recognition of Facial Emotions with M.A.R.I.E., Second Half

Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: M.A.R.I.E. enables the rational, quantified measurement of Emotional Visual Acuity (EVA) in an individual observer and a population aged 20 to 70 years. Meanwhile, it can measure the range and intensity of expressed emotions through three Face- Tests, quantify the performance of a sample of 204 observers with hypernormal measures of cognition, “thymia” (defined elsewhere), and low levels of anxiety, and perform analysis of the six primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual- Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Decision-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”, 6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Fingerprint-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.

Rational and Continuous Measurement of the Emotional Decision Making in Visual Recognition of Facial Emotional Expressions with M.A.R.I.E.: First Half

Context: The advent of Artificial Intelligence (AI) requires modeling prior to its implementation in algorithms for most human skills. This observation requires us to have a detailed and precise understanding of the interfaces of verbal and emotional communications. The progress of AI is significant on the verbal level but modest in terms of the recognition of facial emotions even if this functionality is one of the oldest in humans and is omnipresent in our daily lives. Dysfunction in the ability for facial emotional expressions is present in many brain pathologies encountered by psychiatrists, neurologists, psychotherapists, mental health professionals including social workers. It cannot be objectively verified and measured due to a lack of reliable tools that are valid and consistently sensitive. Indeed, the articles in the scientific literature dealing with Visual-Facial-Emotions-Recognition (ViFaEmRe), suffer from the absence of 1) consensual and rational tools for continuous quantified measurement, 2) operational concepts. We have invented a software that can use computer-morphing attempting to respond to these two obstacles. It is identified as the Method of Analysis and Research of the Integration of Emotions (M.A.R.I.E.). Our primary goal is to use M.A.R.I.E. to understand the physiology of ViFaEmRe in normal healthy subjects by standardizing the measurements. Then, it will allow us to focus on subjects manifesting abnormalities in this ability. Our second goal is to make our contribution to the progress of AI hoping to add the dimension of recognition of facial emotional expressions. Objective: To study: 1) categorical vs dimensional aspects of recognition of ViFaEmRe, 2) universality vs idiosyncrasy, 3) immediate vs ambivalent Emotional-Decision-Making, 4) the Emotional-Fingerprint of a face and 5) creation of population references data. Methods: With M.A.R.I.E. enable a rational quantified measurement of Emotional-Visual-Acuity (EVA) of 1) a) an individual observer, b) in a population aged 20 to 70 years old, 2) measure the range and intensity of expressed emotions by 3 Face-Tests, 3) quantify the performance of a sample of 204 observers with hyper normal measures of cognition, “thymia,” (ibid. defined elsewhere) and low levels of anxiety 4) analysis of the 6 primary emotions. Results: We have individualized the following continuous parameters: 1) “Emotional-Visual-Acuity”, 2) “Visual-Emotional-Feeling”, 3) “Emotional-Quotient”, 4) “Emotional-Deci-sion-Making”, 5) “Emotional-Decision-Making Graph” or “Individual-Gun-Trigger”6) “Emotional-Fingerprint” or “Key-graph”, 7) “Emotional-Finger-print-Graph”, 8) detecting “misunderstanding” and 9) detecting “error”. This allowed us a taxonomy with coding of the face-emotion pair. Each face has specific measurements and graphics. The EVA improves from ages of 20 to 55 years, then decreases. It does not depend on the sex of the observer, nor the face studied. In addition, 1% of people endowed with normal intelligence do not recognize emotions. The categorical dimension is a variable for everyone. The range and intensity of ViFaEmRe is idiosyncratic and not universally uniform. The recognition of emotions is purely categorical for a single individual. It is dimensional for a population sample. Conclusions: Firstly, M.A.R.I.E. has made possible to bring out new concepts and new continuous measurements variables. The comparison between healthy and abnormal individuals makes it possible to take into consideration the significance of this line of study. From now on, these new functional parameters will allow us to identify and name “emotional” disorders or illnesses which can give additional dimension to behavioral disorders in all pathologies that affect the brain. Secondly, the ViFaEmRe is idiosyncratic, categorical, and a function of the identity of the observer and of the observed face. These findings stack up against Artificial Intelligence, which cannot have a globalist or regionalist algorithm that can be programmed into a robot, nor can AI compete with human abilities and judgment in this domain. *Here “Emotional disorders” refers to disorders of emotional expressions and recognition.

Regional homogeneity of intrinsic brain activity related to the main alexithymia dimensions

Background Alexithymia is a multidimensional personality construct.Objective This study aims to investigate the neuronal correlates of each alexithymia dimension by examining the regional homogeneity （ReHo） of intrinsic brain activity in a resting situation.Methods From university freshmen, students with alexithymia and non-alexithymia were recruited. Their alexithymic traits were assessed using the Toronto Alexithymia Scale-20. The ReHo was examined using a resting-state functional MRI approach.Results This study suggests signifcant group differences in ReHo in multiple brain regions distributed in the frontal lobe, parietal lobe, temporal lobe, occipital lobe and insular cortex. However, only the ReHo in the insula was positively associated with diffculty identifying feelings, a main dimension of alexithymia. The ReHo in the lingual gyrus, precentral gyrus and postcentral gyrus was？positively associated with diffculty describing feelings in？participants with？alexithymia. Lastly, the ReHo in the right dorsomedial prefrontal cortex （DMPFC_R） was negatively related to the externally oriented thinking style of participants with？alexithymia.Conclusion In conclusion, these results suggest that the main dimensions of alexithymia are correlated with specifc brain regions’ function, and the role of the insula, lingual gyrus, precentral gyrus, postcentral gyrus and DMPFC_R in the neuropathology of alexithymia should be further investigated.

Evidence of impaired facial emotion recognition in mild Alzheimer’s disease: A mathematical approach and application

Objectives: The first objective of this paper is to show the improved binary outcomes resulting from using MARIE as a diagnostic instrument that allows valid and reliable visual recognition of facial emotional expressions (VRFEE) in an objective and quantitative manner. The second objective is to demonstrate mathematical modeling of binary responses that allow the measurement of categorical dimension, sensitivity, camber, equilibrium points, transition thresholds, etc. The final objective is to illustrate the use of this test for 1) testing a homogeneous sample of healthy young participants;and 2) applying this method to a sample of 12 participants with early Alz-heimer disease compared to a matched control sample of healthy elderly participants. Design: Transforming the binary outcomes of MARIE in mathematical variables (experiment 1), allowing verification of a disorder of VRFEE in early Alzheimer’s disease (experiment 2). Measures: Comparison of numerical variables and graphic representations of both samples. Results: The objective measurement of VRFEE is possible in a healthy population. The application of this methodology to a pathological population is also made possible. The results support the current literature. Conclusion: The combination of the mathematical method with the diagnostic instrument MARIE shows its power and ease of use in clinical practice and research. Its application in many clinical conditions and in clinical research can be useful for understanding brain function. This method improves 1) the inter-examiner comparison and standardizes the quantification of VRFEE for use by multiple researchers;2) the follow-up of a sample over time;3) the comparison of two or more samples. This method is already available in clinical work for refining the diagnosis of Alzheimer’s Disease (AD) in our department.

A Study of Visual Recognition of Facial Emotional Expressions in a Normal Aging Population in the Absence of Cognitive Disorders

Objective: To examine and measure the decision-making processes involved in Visual Recognition of Facial Emotional Expressions (VRFEE) and to study the effects of demographic factors on this process. Method: We evaluated a newly designed software application (M.A.R.I.E.) that permits computerized metric measurement of VRFEE. We administered it to 204 cognitively normal participants ranging in age from 20 to 70 years. Results: We established normative values for the recognition of anger, disgust, joy, fear, surprise and sadness expressed on the faces of three individuals. There was a significant difference in the: 1) measurement (F (8.189) = 3896, p = 0.0001);2) education level (x2(12) = 28.4, p = 0.005);3) face (F(2.195) = 10, p = 0.0001);4)series (F (8.189)=28, p = 0.0001);5) interaction between the identity and recognition of emotions (F (16, 181 =11, p = 0.0001). However, performance did not differ according to: 1) age (F (6.19669) = 1.35, p = 0.2) or 2) level of education (F (1, 1587) = 0.6, p = 0.4). Conclusions: In healthy participants, the VRFEE remains stable throughout the lifespan when cognitive functions remain optimal. Disgust, sadness, fear, and joy seem to be the four most easily recognized facial emotions, while anger and surprise are not easily recognized. Visual recognition of disgust and fear is independent of aging. The characteristics of a face have a significant influence on the ease with which people recognize expressed emotions (idiosyncrasy). Perception and recognition of emotions is categorical, even when the facial images are integrated in a spectrum of morphs reflecting two different emotions on either side.

Erratum to“The power of many brains:Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration”[Sci Bull 2024;69:1536-1555]

The publisher regrets to note that reference citation errors have occurred in panels b,c,e-l in Fig 2 and the sentence“However,the literature reports both decreased and increased intra-network functional connections among patients with depression[115,116].”The publisher would like to apologise for any inconvenience caused.Fig.2.Principal neuroimaging findings in major depressive disorder.(a)Decreased intra-DMN FC is observed in recurrent MDD patients[35].(b)Eight-week antidepressant treatment reduce extensive large-scale functional networks[107].(c)Reduced global and local efficiency(Eglob/Eloc)are revealed in MDD patients[108].(d)Structural variations of the cortex and subcortical nuclei are found in ENIGMA-MDD studies[82].(e)Accelerated brain aging based on functional MRI is observed in MDD patients[114].(f)Accelerated brain aging based on structural MRI is observed in MDD patients[115].(g)Two subtypes of MDD can be identified through DMN FC[127].(h)A significant schizophrenia PRS by MDD interaction for rostral anterior cingulate cortex thickness is found in the UK Biobank dataset[215].(i)Stability of the four MDD subtypes based on FC[126].(j)The two subtypes of MDD exhibit distinct patterns of FC within and between SMS,DMN,and subcortical structures[130].(k)Performance of the functional connectivity-based classifiers across two multicenter datasets[135].(l)Salient brain regions that serve as important classification features for the graph convolutional network-based classifier[136].Brain-PAD:brain-predicted age difference;DAN:dorsal attention network;DMN:default mode network;FC:functional connectivity;FEDN:first-episode and drug-naïve;FPN:frontoparietal network;GCN:graph convolutional neural network;HC:healthy control;linear-SVM:linear support vector machine;MDD:major depressive disorder;mddrest:REST-meta-MDD dataset;NC:normal control;RACC:rostral anterior cingulate cortex;PRS:polygenic risk score;psymri:PsyMRI dataset;rbf-SVM:radial basis function support vector machine;SCN:subcortical network;SCZ:schizophrenia;SMN:sensorimotor network;SMS:sensory and motor systems;SubC:subcortical network;VAN:ventral attention network;VN:visual network.

Connectome-based prediction of the severity of autism spectrum disorder

Background:Autism spectrum disorder(ASD)is characterized by social and behavioural deficits.Current diagnosis relies on be-havioural criteria,but machine learning,particularly connectome-based predictive modelling(CPM),offers the potential to uncover neural biomarkers for ASD.Objective:This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes,seeking to enhance diagnosis and understanding of ASD.Methods:Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model.CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule(ADOS)scores.After the model was constructed,it was applied to independent samples to test its replicability(172 ASD patients)and specificity(36 healthy control participants).Furthermore,we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results.Results:The CPM successfully identified negative networks that significantly predicted ADOS total scores[r(df=150)=0.19,P=0.008 in all patients;r(df=104)=0.20,P=0.040 in classic autism]and communication scores[r(df=150)=0.22,P=0.010 in all patients;r(df=104)=0.21,P=0.020 in classic autism].These results were reproducible across independent databases.The networks were characterized by enhanced inter-and intranetwork connectivity associated with the occipital network(OCC),and the sensorimotor network(SMN)also played important roles.Conclusions:A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD.Large-scale net-works,including the OCC and SMN,played important roles in the predictive model.These findings may provide new directions for the diagnosis and intervention of ASD,and maybe could be the targets in novel interventions.

众脑之力:通过开放神经影像数据和大规模协作催化神经精神疾病的创新发现

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders.By pooling images from various cohorts,statistical power has increased,enabling the detection of subtle abnormalities and robust associations,and fostering new research methods.Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment.Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies.We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders.However,challenges such as data harmonization across different sites,privacy protection,and effective data sharing must be addressed.With proper governance and open science practices,we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis,treatment selection,and outcome prediction,contributing to optimal brain health.

Prevalence of developmental dyslexia in primary school children:a protocol for systematic review and meta-analysis

Background Developmental dyslexia(DD)is a specific impairment during the acquisition of reading skills and may have a lifelong negative impact on individuals.Reliable estimates of the prevalence of DD serve as the basis for evidence-based health resource allocation and policy making.However,the prevalence of DD in primary school children varies largely across studies.Moreover,it is unclear whether there are differences in prevalence in different genders and writing systems.Hence,the present study aims to conduct a systematic review and meta-analysis to assess the global prevalence of DD and to explore related factors.Methods We will undertake a comprehensive literature search in 14 databases,including EMBASE,PubMed,Web of Science,China National Knowledge Infrastructure and Cochrane,from their inception to June 2021.Cross-sectional and longitudinal studies that describe the prevalence of DD will be eligible.The quality of the included observational studies will be assessed using the Strengthening the Reporting of Observational Studies in Epidemiology statement.The risk of bias will be determined by sensitivity analysis to identify publication bias.Results One meta-analysis will be conducted to estimate the prevalence of DD in primary school children.Heterogeneity will be assessed in terms of the properties of subjects(e.g.,gender,grade and writing system)and method of diagnosis in the included primary studies.Subgroup analyses will also be performed for population and secondary outcomes.Conclusion The results will synthesize the prevalence of DD and provide information for policy-makers and public health specialists.

Neurocognitive Correlates of Statistical Learning of Orthographic-Semantic Connections in Chinese Adult Learners

We examined the neural correlates of the statistical learning of orthographic-semantic connections in Chinese adult learners.Visual event-related potentials(ERPs) were recorded while participants were exposed to a sequence of artificial logographic characters containing semantic radicals carrying low,moderate,or high levels of semantic consistency.The behavioral results showed that the mean accuracy of participants’ recognition of previously exposed characters was 63.1% that was significantly above chance level(50%),indicating the statistical learning of the regularities of semantic radicals.The ERP data revealed a temporal sequence of the neural process of statistical learning of orthographic-semantic connections,and different brain indexes were found to be associated with this processing,i.e.,a clear N170-P200-N400 pattern.For N170,the larger negative amplitudes were evoked by the high and moderate consistency than the low consistency.For P200,the mean amplitudes elicited by the moderate and low consistency were larger than the high consistency.In contrast,a larger N400 amplitude was observed in the low than moderate and high consistency;and more negative amplitude was elicited by the moderate than high consistency.We propose that the initial potential shifts(N170 and P200) may reflect orthographic or graphic form identification,while the later component(N400) may be associated with semantic information analysis.

Frequency-dependent alterations in regional homogeneity in young carriers of the apolipoprotein E genotype

Using resting-state functional magnetic resonance imaging(rf MRI),previous studies showed that the APOE e4 allele might affect the functional connectivity between remote brain regions[1,2].However,the local functional connectivity of APOE e4 carriers has rarely been explored.Regional homogeneity(Re Ho)has been widely used to

Spontaneous activity in medial orbitofrontal cortex correlates with trait anxiety in healthy male adults

Medial orbitofrontal cortex （mOFC） abnormalities have been observed in various anxiety disorders. However, the relationship between mOFC activity and anxiety among the healthy population has not been fully examined. Here, we conducted a resting state functional magnetic resonance imaging （R-fMRI） study with 56 healthy male adults from the Nathan Kline Institute/Rockland Sample （NKI-RS） to examine the relationship between the fractional amplitude of low-frequency fluctuation （fALFF） signals and trait anxiety across the whole brain. A Louvain method for module detection based on graph theory was further employed in the automated functional subdivision to explore subregional correlates of trait anxiety. The results showed that trait anxiety was related to fALFF in the mOFC. Additionally, the resting-state functional connectivity （RSFC） between the right subregions of the mOFC and the precuneus was correlated with trait anxiety. These findings provided evidence about the involvement of the mOFC in anxiety processing among the healthy population.

Vernier But Not Grating Acuity Contributes to an Early Stage of Visual Word Processing

The process of reading words depends heavily on efficient visual skills, including analyzing and decomposing basic visual features. Surprisingly, previous reading-related studies have almost exclusively focused on gross aspects of visual skills, while only very few have investigated the role of finer skills. The present study filled this gap and examined the relations of two finer visual skills measured by grating acuity（the ability to resolve periodic luminance variations across space） and Vernier acuity（the ability to detect/discriminate relative locations of features） to Chinese character-processing as measured by character form-matching and lexical decision tasks in skilled adult readers. The results showed that Vernier acuity was significantly correlated with performance in character form-matching but not visual symbol formmatching, while no correlation was found between grating acuity and character processing. Interestingly, we found no correlation of the two visual skills with lexical decisionperformance. These findings provide for the first time empirical evidence that the finer visual skills, particularly as reflected in Vernier acuity, may directly contribute to an early stage of hierarchical word processing.

RESTplus: an improved toolkit for resting-state functional magnetic resonance imaging data processing

Resting-state functional magnetic resonance imaging (RS-fMRI)[1,2] provides relatively high spatial and temporal resolution for mapping spontaneous brain activity non-invasively. It has been widely used in cognitive neuroscience and clinical studies. A number of comprehensive software packages have been developed for RS-fMRI data analysis, among which a MATLAB package named REST (RESing-state fMRI data analysis Toolkit, released in October 2008 at http://www.restfmri.net)[3] is the earliest one dedicated to RS-fMRI analysis. REST focuses on RS-fMRI postprocessing metrics.