Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the brain.However,it is not yet clear how the primary visual cortex(V1)is altered in glaucoma.This study used DBA/2J mice as a model for spontaneous secondary glaucoma.The aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J mice.We conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band oscillations.The morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue sections.Eighty-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were examined.Compared with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial summation.Moreove r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J mice.These findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual tuning.Our findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse model.This study might offer some innovative perspectives regarding the treatment of glaucoma.

Multimodal collaborative BCI system based on the improved CSP feature extraction algorithm

Background As a novel approach for people to directly communicate with an external device,the study of brain-computer interfaces(BCIs)has become well-rounded.However,similar to the real-world scenario,where individuals are expected to work in groups,the BCI systems should be able to replicate group attributes.Methods We proposed a 4-order cumulants feature extraction method(CUM4-CSP)based on the common spatial patterns(CSP)algorithm.Simulation experiments conducted using motion visual evoked potentials(mVEP)EEG data verified the robustness of the proposed algorithm.In addition,to freely choose paradigms,we adopted the mVEP and steady-state visual evoked potential(SSVEP)paradigms and designed a multimodal collaborative BCI system based on the proposed CUM4-CSP algorithm.The feasibility of the proposed multimodal collaborative system framework was demonstrated using a multiplayer game controlling system that simultaneously facilitates the coordination and competitive control of two users on external devices.To verify the robustness of the proposed scheme,we recruited 30 subjects to conduct online game control experiments,and the results were statistically analyzed.Results The simulation results prove that the proposed CUM4-CSP algorithm has good noise immunity.The online experimental results indicate that the subjects could reliably perform the game confrontation operation with the selected BCI paradigm.Conclusions The proposed CUM4-CSP algorithm can effectively extract features from EEG data in a noisy environment.Additionally,the proposed scheme may provide a new solution for EEG-based group BCI research.

A brain structural connectivity biomarker for autism spectrum disorder diagnosis in early childhood

Background:Autism spectrum disorder(ASD)is associated with altered brain development,but it is unclear which specific structural changes may serve as potential diagnostic markers,particularly in young children at the age when symptoms become fully estab-lished.Furthermore,such brain markers need to meet the requirements of precision medicine and be accurate in aiding diagnosis at an individual rather than only a group level.Objective:This study aimed to identify and model brain-wide differences in structural connectivity using diffusion tensor imaging(DTI)in young ASD and typically developing(TD)children.Methods:A discovery cohort including 93 ASD and 26 TD children and two independent validation cohorts including 12 ASD and 9 TD children from three different cities in China were included.Brain-wide(294 regions)structural connectivity was measured using DTI(fractional anisotropy,FA)together with symptom severity and cognitive development.A connection matrix was constructed for each child for comparisons between ASD and TD groups.Pattern classification was performed on the discovery dataset and the resulting model was tested on the two independent validation datasets.Results:Thirty-three structural connections showed increased FA in ASD compared to TD children and associated with both autistic symptom severity and impaired general cognitive development.The majority(29/33)involved the frontal lobe and comprised five different networks with functional relevance to default mode,motor control,social recognition,language and reward.Overall,clas-sification achieved very high accuracy of 96.77%in the discovery dataset,and 91.67%and 88.89%in the two independent validation datasets.Conclusions:Identified structural connectivity differences primarily involving the frontal cortex can very accurately distinguish novel individual ASD from TD children and may therefore represent a robust early brain biomarker which can address the requirements of precision medicine.

Separate Neural Networks for Gains and Losses in Intertemporal Choice

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision- making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains： gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or pun- ishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains： in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.

Reconstruction of behavior-relevant individual brain activity:an individualized fMRI study

Different patterns of brain activity are observed in various subjects across a wide functional domain.However,these individual differences,which are often neglected through the group average,are not yet completely understood.Based on the fundamental assumption that human behavior is rooted in the underlying brain function,we speculated that the individual differences in brain activity are reflected in the individual differences in behavior.Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states,we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model.In addition,we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity.Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer.Therefore,our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging.Moreover,reconstructed individual brain activity shows a potential use in precise and personalized medicine.

Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness,pre-diction of prognosis before treatment,and guidance for selection of effective treatments that target patient-relevant pathophysiological features.This is the primary aim of the field of Psychoradiology.Using databases collected from large samples at multiple centers,sophisticated artificial intelligence(AI)algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose,predict,and make treatment decisions.In this review,we selectively summarize psychoradiological research using magnetic reso-nance imaging of the brain to explore the neural mechanism of psychiatric disorders,and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders,as well as limitations in the application of AI that should be considered in future translational research.

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments.One of the most important obstacles is the MI-BCI inefficiency phenomenon.The accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG features.An MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental population.The widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient subjects.In this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological perspectives.These factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent laboratories.We then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem.The potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG features.To date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research area.Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.

Deep Natural Image Reconstruction from Human Brain Activity Based on Conditional Progressively Growing Generative Adversarial Networks

Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of visual perception and mental states.However,due to the limitations of sample size and the lack of an effective reconstruction model,accurate reconstruction of natural images is still a major challenge.The current,rapid development of deep learning models provides the possibility of overcoming these obstacles.Here,we propose a deep learning-based framework that includes a latent feature extractor,a latent feature decoder,and a natural image generator,to achieve the accurate reconstruction of natural images from brain activity.The latent feature extractor is used to extract the latent features of natural images.The latent feature decoder predicts the latent features of natural images based on the response signals from the higher visual cortex.The natural image generatoris applied to generate reconstructed images from the predicted latent features of natural images and the response signals from the visual cortex.Quantitative and qualitative evaluations were conducted with test images.The results showed that the reconstructed image achieved comparable,accurate reproduction of the presented image in both highlevel semantic category information and low-level pixel information.The framework we propose shows promise for decoding the brain activity.

Translational application of neuroimaging in major depressive disorder:a review of psychoradiological studies

Major depressive disorder(MDD)causes great decrements in health and quality of life with increments in healthcare costs,but the causes and pathogenesis of depression remain largely unknown,which greatly prevent its early detection and effective treatment.With the advancement of neuroimaging approaches,numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice.In this review,we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage.The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification,subtyping,and prediction.While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse,the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response.These findings underlined the potential utility of brain biomarkers for clinical practice.

Distinct neuroanatomic subtypes in antipsychotic-treated patients with schizophrenia classified by the predefined classification in a never-treated sample

Background:Distinct neuroanatomic subtypes have been identified in never-treated patients with schizophrenia based on cerebral structural abnormalities,but whether antipsychotic-treated patients would be stratified under the guidance of such previously formed classification remains unclear.Objective:The present study aimed to investigate alterations of brain structures in antipsychotic-treated patients with schizophrenia based on a predefined morphological classification and their relationships with cognitive performance.Methods:Cortical thickness,surface area,and subcortical volume were extracted from 147 antipsychotictreated patients with schizophrenia using structural magnetic resonance imaging for classification.The Brief Assessment of Cognition in Schizophrenia(BACS)and Positive and Negative Syndrome Scale(PANSS)were used to assess cognition and symptoms.Results:Antipsychotic-treated patients were categorized into three subtypes with distinct patterns of brain morphological alterations.Subtypes 1 and 2 were characterized by widespread deficits in cortical thickness but relatively limited deficits in surface area.In contrast,subtype 3 demonstrated cortical thickening mainly in parietal-occipital regions and widespread deficits in surface area.All three subgroups demonstrated cognitive deficits compared with healthy controls.Significant associations between neuroanatomic and cognitive abnormalitieswere only observed in subtype 1,where cortical thinning in the left lingual gyruswas conversely related to symbol coding performance.Conclusions:Similar to drug-naıve patients,neuroanatomic heterogeneity exists in antipsychotic-treated patients,with disparate associations with cognition.These findings promote our understanding of relationships between neuroanatomic abnormalities and cognitive performance in the context of heterogeneity.Moreover,these results suggest that neurobiological heterogeneity needs to be considered in cognitive research in schizophrenia.

Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular–Structural–Functional Co-alterations

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain regions.Owing to lacking an effective approach to quantifying the covarying of structure and functional responses,how the structural–functional circuits interact and how genes encode the relationships,to deepen our knowledge of human cognition and disease,are still unclear.Here,we propose a multimodal covariance network(MCN)construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single individual.We further explored the potential association between brain-wide gene expression patterns and structural–functional covarying in individuals involved in a gambling task and individuals with major depression disorder(MDD),adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent cohorts.MCN analysis showed a replicable cortical structural–functional fine map in healthy individuals,and the expression of cognition-and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN differences.Further analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences.