Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain

Individualized functional connectome abnormalities obtained using two normative model unveil neurophysiological subtypes of obsessive compulsive disorder

The high heterogeneity observed among patients with obsessive-compulsive disorder (OCD) underscores the need to identify neurophysiological OCD subtypes to facilitate personalized diagnosis and treatment. In this study, our aim was to identify potential OCD subtypes based on individualized functional connectome abnormalities. We recruited a total of 99 patients with OCD and 104 healthy controls (HCs) matched for demographic characteristics. Individualized functional connectome abnormalities were obtained using normative models of functional connectivity strength (FCS) and used as features to unveil OCD subtypes. Sensitivity analyses were conducted to assess the reproducibility and robustness of the clustering outcomes. Patients exhibited significant intersubject heterogeneity in individualized functional connectome abnormalities. Two subtypes with distinct patterns of FCS abnormalities relative to HCs were identified. Subtype 1 patients primarily exhibited significantly decreased FCS in regions including the frontal gyrus, insula, hippocampus, and precentral/postcentral gyrus, whereas subtype 2 patients demonstrated increased FCS in widespread brain regions. When all patients were combined, no significant differences were observed. Additionally, the identified subtypes showed significant differences in age of onset. Furthermore, sensitivity analyses confirmed the reproducibility of our subtyping results. In conclusion, the identified OCD subtypes shed new light on the taxonomy and neurophysiological heterogeneity of OCD.

Characterizing microstructural patterns within the cortico-striato-thalamo-cortical circuit in Parkinson's disease

PURPOSE

Parkinson's disease (PD) involves pathological alterations that include cortical impairments at levels of region and network. However, its microstructural abnormalities remain to be further elucidated via an appropriate diffusion neuroimaging approach. This study aimed to comprehensively demonstrate the microstructural patterns of PD as mapped by diffusion kurtosis imaging (DKI).

METHODS

The microstructure of grey matter in both the PD group and the matched healthy control group was quantified by a DKI metric (mean kurtosis). The intergroup difference and classification performance of global microstructural complexity were analyzed in a voxelwise manner and via a machine learning approach, respectively. The patterns of information flows were explored in terms of structural connectivity, network covariance and modular connectivity.

RESULTS

Patients with PD exhibited global microstructural impairments that served as an efficient diagnostic indicator. Disrupted structural connections between the striatum and cortices as well as between the thalamus and cortices were widely distributed in the PD group. Aberrant covariance of the striatocortical circuitry and thalamocortical circuitry was observed in patients with PD, who also showed disrupted modular connectivity within the striatum and thalamus as well as across structures of the cortex, striatum and thalamus.

CONCLUSION

These findings verified the potential clinical application of DKI for the exploration of microstructural patterns in PD, contributing complementary imaging features that offer a deeper insight into the neurodegenerative process.

Spatio-temporal learning and explaining for dynamic functional connectivity analysis: Application to depression

BACKGROUND

Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology.

METHODS

The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified.

RESULTS

We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified.

LIMITATIONS

The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population.

CONCLUSIONS

The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression.

Clinical and Brain Morphometry Predictors of Deep Brain Stimulation Outcome in Parkinson's Disease

Subthalamic deep brain stimulation (STN-DBS) is known to improve motor function in advanced Parkinson's disease (PD) and to enable a reduction of anti-parkinsonian medication. While the levodopa challenge test and disease duration are considered good predictors of STN-DBS outcome, other clinical and neuroanatomical predictors are less established. This study aimed to evaluate, in addition to clinical predictors, the effect of patients' individual brain topography on DBS outcome. The medical records of 35 PD patients were used to analyze DBS outcomes measured with the following scales: Part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III) off medication at baseline, and at 6-months during medication off and stimulation on, use of anti-parkinsonian medication (LED), Abnormal Involuntary Movement Scale (AIMS) and Non-Motor Symptoms Questionnaire (NMS-Quest). Furthermore, preoperative brain MRI images were utilized to analyze the brain morphology in relation to STN-DBS outcome. With STN-DBS, a 44% reduction in the UPDRS-III score and a 43% decrease in the LED were observed (p<0.001). Dyskinesia and non-motor symptoms decreased significantly [median reductions of 78,6% (IQR 45,5%) and 18,4% (IQR 32,2%) respectively, p=0.001 - 0.047]. Along with the levodopa challenge test, patients' age correlated with the observed DBS outcome measured as UPDRS-III improvement (ρ= -0.466 - -0.521, p<0.005). Patients with greater LED decline had lower grey matter volumes in left superior medial frontal gyrus, in supplementary motor area and cingulum bilaterally. Additionally, patients with greater UPDRS-III score improvement had lower grey matter volume in similar grey matter areas. These findings remained significant when adjusted for sex, age, baseline LED and UPDRS scores respectively and for total intracranial volume (p=0.0041- 0.001). However, only the LED decrease finding remained significant when the analyses were further controlled for stimulation amplitude. It appears that along with the clinical predictors of STN-DBS outcome, individual patient topographic differences may influence DBS outcome. Clinical Trial Registration Number: NCT06095245, registration date October 23, 2023, retrospectively registered.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis

Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has identified abnormal brain function in PACG patients, the relationship between PACG and alterations in brain functional networks has yet to be explored. This study seeks to examine the influence of PACG on brain networks, aiming to advance knowledge of its neurobiological processes for better diagnostic and therapeutic approaches utilizing graph theory analysis. A cohort of 44 primary angle-closure glaucoma (PACG) patients and 44 healthy controls participated in this study. Functional brain networks were constructed using fMRI data and the Automated Anatomical Labeling 90 template. Subsequently, graph theory analysis was employed to evaluate global metrics, nodal metrics, modular organization, and network-based statistics (NBS), enabling a comparative analysis between PACG patients and the control group. The analysis of global metrics, including small-worldness and network efficiency, did not exhibit significant differences between the two groups. However, PACG patients displayed elevated nodal metrics, such as centrality and efficiency, in the left frontal superior medial, right frontal superior medial, and right posterior central brain regions, along with reduced values in the right temporal superior gyrus region compared to healthy controls. Furthermore, Module 5 showed notable disparities in intra-module connectivity, while Module 1 demonstrated substantial differences in inter-module connectivity with both Module 7 and Module 8. Noteworthy, the NBS analysis unveiled a significantly altered network when comparing the PACG and healthy control groups. The study proposes that PACG patients demonstrate variations in nodal metrics and modularity within functional brain networks, particularly affecting the prefrontal, occipital, and temporal lobes, along with cerebellar regions. However, an analysis of global metrics suggests that the overall connectivity patterns of the entire brain network remain unaltered in PACG patients. These results have the potential to serve as early diagnostic and differential markers for PACG, and interventions focusing on brain regions with high degree centrality and nodal efficiency could aid in optimizing therapeutic approaches.

Epigenetic molecular underpinnings of brain structural-functional connectivity decoupling in patients with major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is progressively recognized as a stress-related disorder characterized by aberrant brain network dynamics, encompassing both structural and functional domains. Yet, the intricate interplay between these dynamic networks and their molecular underpinnings remains predominantly unexplored.

METHODS

Both structural and functional networks were constructed using multimodal neuroimaging data from 183 MDD patients and 300 age- and gender-matched healthy controls (HC). structural-functional connectivity (SC-FC) coupling was evaluated at both the connectome- and nodal-levels. Methylation data of five HPA axis key genes, including NR3C1, FKBP5, CRHBP, CRHR1, and CRHR2, were analyzed using Illumina Infinium Methylation EPIC BeadChip.

RESULTS

We observed a significant reduction in SC-FC coupling at the connectome-level in patients with MDD compared to HC. At the nodal level, we found an imbalance in SC-FC coupling, with reduced coupling in cortical regions and increased coupling in subcortical regions. Furthermore, we identified 23 differentially methylated CpG sites on the HPA axis, following adjustment for multiple comparisons and control of age, gender, and medication status. Notably, three CpG sites on NR3C1 (cg01294526, cg19457823, and cg23430507), one CpG site on FKBP5 (cg25563198), one CpG site on CRHR1 (cg26656751), and one CpG site on CRHR2 (cg18351440) exhibited significant associations with SC-FC coupling in MDD patients.

CONCLUSIONS

These findings provide valuable insights into the connection between micro-scale epigenetic changes in the HPA axis and SC-FC coupling at macro-scale connectomes. They unveil the mechanisms underlying increased susceptibility to MDD resulting from chronic stress and may suggest potential pharmacological targets within the HPA-axis for MDD treatment.

High modularity, more flexible of brain networks in patients with mild to moderate motor impairments after stroke

Stroke is recognized as a network communication disorder. Advances in neuroimaging technologies have enhanced our comprehension of dynamic cerebral alterations. However, different levels of motor function impairment after stroke may have different patterns of brain reorganization. Abnormal and adaptive patterns of brain activity in mild-to-moderate motor function impairments after stroke remain still underexplored. We aim to identify dynamic patterns of network remodeling in stroke patients with mild-to-moderate impairment of motor function. fMRI data were obtained from 30 stroke patients and 31 healthy controls to establish a spatiotemporal multilayer modularity model. Then, graph-theoretic measures, including modularity, flexibility, cohesion, and disjointedness, were calculated to quantify dynamic reconfiguration. Our findings reveal that the post-stroke brain exhibited higher modular organization, as well as heightened disjointedness, compared to HCs. Moreover, analyzing from the network level, we found increased disjointedness and flexibility in the Default mode network (DMN), indicating that brain regions tend to switch more frequently and independently between communities and the dynamic changes were mainly driven by DMN. Notably, modified functional dynamics positively correlated with motor performance in patients with mild-to-moderate motor impairment. Collectively, our research uncovered patterns of dynamic community reconstruction in multilayer networks following stroke. Our findings may offer new insights into the complex reorganization of neural function in post-stroke brain.

Interpretable deep clustering survival machines for Alzheimer's disease subtype discovery

Alzheimer's disease (AD) is a complex neurodegenerative disorder that has impacted millions of people worldwide. The neuroanatomical heterogeneity of AD has made it challenging to fully understand the disease mechanism. Identifying AD subtypes during the prodromal stage and determining their genetic basis would be immensely valuable for drug discovery and subsequent clinical treatment. Previous studies that clustered subgroups typically used unsupervised learning techniques, neglecting the survival information and potentially limiting the insights gained. To address this problem, we propose an interpretable survival analysis method called Deep Clustering Survival Machines (DCSM), which combines both discriminative and generative mechanisms. Similar to mixture models, we assume that the timing information of survival data can be generatively described by a mixture of parametric distributions, referred to as expert distributions. We learn the weights of these expert distributions for individual instances in a discriminative manner by leveraging their features. This allows us to characterize the survival information of each instance through a weighted combination of the learned expert distributions. We demonstrate the superiority of the DCSM method by applying this approach to cluster patients with mild cognitive impairment (MCI) into subgroups with different risks of converting to AD. Conventional clustering measurements for survival analysis along with genetic association studies successfully validate the effectiveness of the proposed method and characterize our clustering findings.

Alzheimer's disease diagnosis from multi-modal data via feature inductive learning and dual multilevel graph neural network

Multi-modal data can provide complementary information of Alzheimer's disease (AD) and its development from different perspectives. Such information is closely related to the diagnosis, prevention, and treatment of AD, and hence it is necessary and critical to study AD through multi-modal data. Existing learning methods, however, usually ignore the influence of feature heterogeneity and directly fuse features in the last stages. Furthermore, most of these methods only focus on local fusion features or global fusion features, neglecting the complementariness of features at different levels and thus not sufficiently leveraging information embedded in multi-modal data. To overcome these shortcomings, we propose a novel framework for AD diagnosis that fuses gene, imaging, protein, and clinical data. Our framework learns feature representations under the same feature space for different modalities through a feature induction learning (FIL) module, thereby alleviating the impact of feature heterogeneity. Furthermore, in our framework, local and global salient multi-modal feature interaction information at different levels is extracted through a novel dual multilevel graph neural network (DMGNN). We extensively validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and experimental results demonstrate our method consistently outperforms other state-of-the-art multi-modal fusion methods. The code is publicly available on the GitHub website. (https://github.com/xiankantingqianxue/MIA-code.git).

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

Interaction of perceived social support and childhood maltreatment on limbic responsivity towards negative emotional stimuli in healthy individuals

Childhood maltreatment (CM) is associated with increased limbic activity, while social support is linked to decreased limbic activity towards negative stimuli. Our study aimed to explore the interaction of perceived social support with CM, and their combined impact on limbic activity in negative emotion processing. A total of 130 healthy individuals (HC) underwent a negative emotional face processing paradigm. They were divided into two groups based on the Childhood Trauma Questionnaire: n = 65 HC without CM matched with n = 65 HC with CM. In a region-of-interest approach of the bilateral amygdala-hippocampus-complex (AHC), regression analyses investigating the association of CM and perceived social support with limbic activity and a social support x CM ANCOVA were conducted. CM was associated with increased AHC activity, while perceived social support tended to be associated with decreased AHC activity during negative emotion processing. The ANCOVA showed a significant interaction in bilateral AHC activity (pFWE ≤ 0.024) driven by a negative association between perceived social support and bilateral AHC activity in HC without CM. No significant association was observed in HC with CM. Exploratory analyses using continuous CM scores support this finding. Our results suggest that CM moderates the link between perceived social support and limbic activity, with a protective effect of perceived social support only in HC without CM. The lack of this effect in HC with CM suggests that CM may alter the buffering effect of perceived social support on limbic functioning, highlighting the potential need for preventive interventions targeting social perception of HC with CM.

Maternal psychopathology is differentially associated with adolescent offspring neural response to reward given offspring ADHD risk

Reinforcement sensitivity is a hypothesized attention-deficit/hyperactivity disorder (ADHD) intermediate phenotype but its role in transgenerational transmission of ADHD-linked psychopathology risk is largely unknown. We examined, in a carefully phenotyped, N = 123 sample of adolescents (Mage = 15.27 years, SD = 0.984; 61.78% boys), whether (1) parental psychopathology is differentially associated with fMRI-indexed neural response to reward receipt and (2) both maternal and paternal psychopathology are associated with neural response to reward; across adolescents at-risk for and not at-risk for ADHD. Indices of parental psychopathology were differentially associated with adolescent offspring neural response to reward such that across measures, parental psychopathology was negatively or not associated with offspring superior frontal gyrus (SFG) response to reward receipt in adolescents at-risk for ADHD, but parental psychopathology was positively associated with offspring SFG response in adolescents not at-risk. Further, across measures, greater maternal psychopathology was associated with blunted adolescent SFG response to reward in adolescents at-risk for ADHD whereas greater maternal externalizing problems were linked to enhanced adolescent SFG response in adolescents not at-risk. Across measures, paternal psychopathology was not associated with adolescent response to reward, in either group. ADHD risk confers differential reward-related susceptibility to the effects of parental psychopathology. Results also show this association is nonspecific in terms of parental psychopathology type but is specific to maternal psychopathology.

Whole-brain model replicates sleep-like slow-wave dynamics generated by stroke lesions

Focal brain injuries, such as stroke, cause local structural damage as well as alteration of neuronal activity in distant brain regions. Experimental evidence suggests that one of these changes is the appearance of sleep-like slow waves in the otherwise awake individual. This pattern is prominent in areas surrounding the damaged region and can extend to connected brain regions in a way consistent with the individual's specific long-range connectivity patterns. In this paper we present a generative whole-brain model based on (f)MRI data that, in combination with the disconnection mask associated with a given patient, explains the effects of the sleep-like slow waves originated in the vicinity of the lesion area on the distant brain activity. Our model reveals new aspects of their interaction, being able to reproduce functional connectivity patterns of stroke patients and offering a detailed, causal understanding of how stroke-related effects, in particular slow waves, spread throughout the brain. The presented findings demonstrate that the model effectively captures the links between stroke occurrences, sleep-like slow waves, and their subsequent spread across the human brain.

Visual self-face and self-body recognition in a left-brain-damaged prosopagnosic patient

The present case study describes the patient N.G., who reported prosopagnosia along with difficulty in recognising herself in the mirror following a left-sided temporo-occipital hemispheric stroke. The neuropsychological and experimental investigation revealed only a mild form of apperceptive prosopagnosia, without visual agnosia, primarily caused by an impaired visual processing of face-parts and body parts but not of full faces. Emotional expressions did not modulate her face processing. On the other hand, N.G. showed a marked impairment of visual self-recognition, as assessed with visual matching-to-sample tasks, both at the level of body-part and face-part processing and at a full-face level, featured by a deficit in the perceptual discrimination of her own face and body, as compared to the others' face and body. N.G.'s lesion mapping showed damage to the left inferior occipito-temporal cortex, affecting the inferior occipital gyrus and compromising long-range connections between the occipital/temporo-occipital areas and the anterior fronto-temporal areas. Overall, the present case report documents that visual processing of the person's own face may be selectively compromised by a left-sided hemispheric lesion disconnecting extra-striate body- and face-selective visual areas to self-representation regions. Moreover, others' (full) face processing may be preserved, as compared with the impaired ability to discriminate others' body and face parts.

Here Comes Revenge: Peer Victimization Relates to Neural and Behavioral Responses to Social Exclusion

The aim of this study was to examine whether repeated victimization relates to differential processing of social exclusion experiences. It was hypothesized that experiences of repeated victimization would modulate neural processing of social exclusion in the insula, anterior cingulate cortex, and lateral prefrontal cortex. Furthermore, we hypothesized that repeated victimization relates positively to intentions to punish excluders. Exploratively, associations between neural processing and intentions to punish others were examined. The sample consisted of children with known victimization in the past two years (n = 82 (behavioral) / n = 73 (fMRI), 49.4% girls, Mage = 10.6). The participants played Cyberball, an online ball-tossing game, which was manipulated so that in the first block participants were equally included and in the second block they were excluded from play. Victimization was not related to neural activation during social exclusion, although there were indications that victimization may be related to increased insula activation during explicit exclusion. Behaviorally, repeated victimization was related to more intention to punish excluders. Neural activation during social exclusion did not predict intentions to punish excluders, but results tentatively suggested that increased insula activation during social exclusion may be related to increased intentions to punish. Together, these results provide a replication of earlier Cyberball studies and point toward differential processing of social exclusion by children who are victimized.

Chinese Verbal Fluency Deficiency in Temporal Lobe Epilepsy with and without Hippocampal Sclerosis: A Multiscale Study

To test a Chinese character version of the phonemic verbal fluency task in patients with temporal lobe epilepsy (TLE) and assess the verbal fluency deficiency pattern in TLE with and without hippocampal sclerosis, a cross-sectional study was conducted including 30 patients with TLE and hippocampal sclerosis (TLE-HS), 28 patients with TLE and without hippocampal sclerosis (TLE-NHS), and 29 demographically matched healthy controls (HC). Both sexes were enrolled. Participants finished a Chinese character verbal fluency (VFC) task during functional MRI. The activation/deactivation maps, functional connectivity, degree centrality, and community features of the left frontal and temporal regions were compared. A neural network classification model was applied to differentiate TLE-HS and TLE-NHS using functional statistics. The VFC scores were correlated with semantic fluency in HC while correlated with phonemic fluency in TLE-NHS. Activation and deactivation deficiency was observed in TLE-HS and TLE-NHS (p < 0.001, k ≥ 10). Functional connectivity, degree centrality, and community features of anterior inferior temporal gyri were impaired in TLE-HS and retained or even enhanced in TLE-NHS (p < 0.05, FDR-corrected). The functional connectivity was correlated with phonemic fluency (p < 0.05, FDR-corrected). The neural network classification reached an area under the curve of 0.90 in diagnosing hippocampal sclerosis. The VFC task is a Chinese phonemic verbal fluency task suitable for clinical application in TLE. During the VFC task, functional connectivity of phonemic circuits was impaired in TLE-HS and was enhanced in TLE-NHS, representing a compensative phonemic searching strategy applied by patients with TLE-NHS.

Structural brain characteristics of epilepsy patients with comorbid migraine without aura

Migraine is a common bi-directional comorbidity of epilepsy, indicating potential complex interactions between the two conditions. However, no previous studies have used brain morphology analysis to assess possible interactions between epilepsy and migraine. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and structural covariance networks (SCNs) can be used to detect morphological changes with high accuracy. We recruited 30 individuals with epilepsy and comorbid migraine without aura (EM), along with 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. We used VBM, SBM, and SCN analysis to compare differences in gray matter volume, cortical thickness, and global level and local level graph theory indexes between the EM, EC, and HC groups to investigate structural brain changes in the EM patients. VBM analysis showed that the EM group had gray matter atrophy in the right temporal pole compared with the HC group (p < 0.001, false discovery rate correction [FDR]). Furthermore, the headache duration in the EM group was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05). SBM analysis showed cortical atrophy in the left insula, left posterior cingulate gyrus, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus in the EM compared with the HC group (p < 0.001, family wise error correction). We found a positive correlation between headache frequency and the cortical thickness of the left middle temporal gyrus (p < 0.05). SCN analysis revealed no differences in global parameters between the three groups. The area under the curve (AUC) of the nodal betweenness centrality in the right postcentral gyrus was lower in the EM group compared with the HC group (p < 0.001, FDR correction), and the AUC of the nodal degree in the right fusiform gyrus was lower in the EM group compared with the EC group (p < 0.001, FDR correction). We found clear differences in brain structure in the EM patients compared with the HC group. Accordingly, migraine episodes may influence brain structure in epilepsy patients. Conversely, abnormal brain structure may be an important factor in the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the role of brain structure in individuals with epilepsy and comorbid migraine without aura.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Differential network knockoff filter with application to brain connectivity analysis

The brain functional connectivity can typically be represented as a brain functional network, where nodes represent regions of interest (ROIs) and edges symbolize their connections. Studying group differences in brain functional connectivity can help identify brain regions and recover the brain functional network linked to neurodegenerative diseases. This process, known as differential network analysis focuses on the differences between estimated precision matrices for two groups. Current methods struggle with individual heterogeneity in measuring the brain connectivity, false discovery rate (FDR) control, and accounting for confounding factors, resulting in biased estimates and diminished power. To address these issues, we present a two-stage FDR-controlled feature selection method for differential network analysis using functional magnetic resonance imaging (fMRI) data. First, we create individual brain connectivity measures using a high-dimensional precision matrix estimation technique. Next, we devise a penalized logistic regression model that employs individual brain connectivity data and integrates a new knockoff filter for FDR control when detecting significant differential edges. Through extensive simulations, we showcase the superiority of our approach compared to other methods. Additionally, we apply our technique to fMRI data to identify differential edges between Alzheimer's disease and control groups. Our results are consistent with prior experimental studies, emphasizing the practical applicability of our method.

Embodiment in episodic memory through premotor-hippocampal coupling

Episodic memory (EM) allows us to remember and relive past events and experiences and has been linked to cortical-hippocampal reinstatement of encoding activity. While EM is fundamental to establish a sense of self across time, this claim and its link to the sense of agency (SoA), based on bodily signals, has not been tested experimentally. Using real-time sensorimotor stimulation, immersive virtual reality, and fMRI we manipulated the SoA and report stronger hippocampal reinstatement for scenes encoded under preserved SoA, reflecting recall performance in a recognition task. We link SoA to EM showing that hippocampal reinstatement is coupled with reinstatement in premotor cortex, a key SoA region. We extend these findings in a severe amnesic patient whose memory lacked the normal dependency on the SoA. Premotor-hippocampal coupling in EM describes how a key aspect of the bodily self at encoding is neurally reinstated during the retrieval of past episodes, enabling a sense of self across time.

Differences in Topography of Individual Amyloid Brain Networks by Amyloid PET Images in Healthy Control, Mild Cognitive Impairment, and Alzheimer's Disease

Amyloid plaques, implicated in Alzheimer's disease, exhibit a spatial propagation pattern through interconnected brain regions, suggesting network-driven dissemination. This study utilizes PET imaging to investigate these brain connections and introduces an innovative method for analyzing the amyloid network. A modified version of a previously established method is applied to explore distinctive patterns of connectivity alterations across cognitive performance domains. PET images illustrate differences in amyloid accumulation, complemented by quantitative network indices. The normal control group shows minimal amyloid accumulation and preserved network connectivity. The MCI group displays intermediate amyloid deposits and partial similarity to normal controls and AD patients, reflecting the evolving nature of cognitive decline. Alzheimer's disease patients exhibit high amyloid levels and pronounced disruptions in network connectivity, which are reflected in low levels of global efficiency (Eg) and local efficiency (Eloc). It is mostly in the temporal lobe where connectivity alterations are found, particularly in regions related to memory and cognition. Network connectivity alterations, combined with amyloid PET imaging, show potential as discriminative markers for different cognitive states. Dataset-specific variations must be considered when interpreting connectivity patterns. The variability in MCI and AD overlap emphasizes the heterogeneity in cognitive decline progression, suggesting personalized approaches for neurodegenerative disorders. This study contributes to understanding the evolving network characteristics associated with normal cognition, MCI, and AD, offering valuable insights for developing diagnostic and prognostic markers.

Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects

INTRODUCTION

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

AREAS COVERED

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

EXPERT OPINION

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

StepBrain: A 3-Dimensionally Printed Multicompartmental Anthropomorphic Brain Phantom to Simulate PET Activity Distributions

An innovative multicompartmental anatomic brain phantom (StepBrain) is described to simulate the in vivo tracer uptake of gray matter, white matter, and striatum, overcoming the limitations of currently available phantoms. Methods: StepBrain was created by exploiting the potential of fused deposition modeling 3-dimensional printing to replicate the real anatomy of the brain compartments, as modeled through ad hoc processing of healthy-volunteer MR images. Results: A realistic simulation of 18F-FDG PET brain studies, using target activity to obtain the real concentration ratios, was obtained, and the results of postprocessing with partial-volume effect correction tools developed for human PET studies confirmed the accuracy of these methods in recovering the target activity concentrations. Conclusion: StepBrain compartments (gray matter, white matter, and striatum) can be simultaneously filled, achieving different concentration ratios and allowing the simulation of different (e.g., amyloid, tau, or 6-fluoro-l-dopa) tracer distributions, with a potentially valuable role for multicenter PET harmonization studies.

MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis

Autism spectrum disorder (ASD) is a common neurological condition. Early diagnosis and treatment are essential for enhancing the life quality of individuals with ASD. However, most existing studies either focus solely on the brain networks of subjects within a single atlas or merely employ simple matrix concatenation to represent the fusion of multi-atlas. These approaches neglected the natural spatial overlap that exists between brain regions across multi-atlas and did not fully capture the comprehensive information of brain regions under different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas fusion template based on spatial overlap degree of brain regions, which aims to obtain a comprehensive representation of brain networks. Specifically, we formally define a measurement of the spatial overlap among brain regions across different atlases, named spatial overlap degree. Then, we fuse the multi-atlas to obtain brain networks of each subject based on spatial overlap. Finally, the GCN is used to perform the final classification. The experimental results on Autism Brain Imaging Data Exchange (ABIDE) demonstrate that our proposed method achieved an accuracy of 0.757. Overall, our method outperforms SOTA methods in ASD/TC classification.

Prestimulus Alpha Phase Modulates Visual Temporal Integration

When presented shortly after another, discrete pictures are naturally perceived as continuous. The neuronal mechanism underlying such continuous or discrete perception is not well understood. While continuous alpha oscillations are a candidate for orchestrating such neuronal mechanisms, recent evidence is mixed. In this study, we investigated the influence of prestimulus alpha oscillation on visual temporal perception. Specifically, we were interested in whether prestimulus alpha phase modulates neuronal and perceptual processes underlying discrete or continuous perception. Participants had to report the location of a missing object in a visual temporal integration task, while simultaneously MEG data were recorded. Using source reconstruction, we evaluated local phase effects by contrasting phase angle values between correctly and incorrectly integrated trials. Our results show a phase opposition cluster between -0.8 and -0.5 s (relative to stimulus presentation) and between 6 and 20 Hz. These momentary phase angle values were correlated with behavioral performance and event-related potential amplitude. There was no evidence that frequency defined a window of temporal integration.

New objective simple evaluation methods of amyloid PET/CT using whole-brain histogram and Top20%-Map

OBJECTIVE

This study aims to assess the utility of newly developed objective methods for the evaluation of intracranial abnormal amyloid deposition using PET/CT histogram without use of cortical ROI analyses.

METHODS

Twenty-five healthy volunteers (HV) and 38 patients with diagnosed or suspected dementia who had undergone 18F-FPYBF-2 PET/CT were retrospectively included in this study. Out of them, 11C-PiB PET/CT had been also performed in 13 subjects. In addition to the conventional methods, namely visual judgment and quantitative analyses using composed standardized uptake value ratio (comSUVR), the PET images were also evaluated by the following new parameters: the skewness and the mode-to-mean ratio (MMR) obtained from the histogram of the brain parenchyma; Top20%-map highlights the areas with high tracer accumulation occupying 20% volume of the total brain parenchymal on the individual's CT images. We evaluated the utility of the new methods using histogram compared with the visual assessment and comSUVR. The results of these new methods between 18F-FPYBF-2 and 11C-PiB were also compared in 13 subjects.

RESULTS

In visual analysis, 32, 9, and 22 subjects showed negative, border, and positive results, and composed SUVR in each group were 1.11 ± 0.06, 1.20 ± 0.13, and 1.48 ± 0.18 (p < 0.0001), respectively. Visually positive subjects showed significantly low skewness and high MMR (p < 0.0001), and the Top20%-Map showed the presence or absence of abnormal deposits clearly. In comparison between the two tracers, visual evaluation was all consistent, and the ComSUVR, the skewness, the MMR showed significant good correlation. The Top20%-Maps showed similar pattern.

CONCLUSIONS

Our new methods using the histogram of the brain parenchymal accumulation are simple and suitable for clinical practice of amyloid PET, and Top20%-Map on the individual's brain CT can be of great help for the visual assessment.

Imbalanced optimal feedback motor control system in spinocerebellar ataxia type 3

BACKGROUND AND PURPOSE

Human motor planning and control depend highly on optimal feedback control systems, such as the neocortex-cerebellum circuit. Here, diffusion tensor imaging was used to verify the disruption of the neocortex-cerebellum circuit in spinocerebellar ataxia type 3 (SCA3), and the circuit's disruption correlation with SCA3 motor dysfunction was investigated.

METHODS

This study included 45 patients with familial SCA3, aged 17-67 years, and 49 age- and sex-matched healthy controls, aged 21-64 years. Tract-based spatial statistics and probabilistic tractography was conducted using magnetic resonance images of the patients and controls. The correlation between the local probability of probabilistic tractography traced from the cerebellum and clinical symptoms measured using specified symptom scales was also calculated.

RESULTS

The cerebellum-originated probabilistic tractography analysis showed that structural connectivity, mainly in the subcortical cerebellar-thalamo-cortical tract, was significantly reduced and the cortico-ponto-cerebellar tract was significantly stronger in the SCA3 group than in the control group. The enhanced tract was extended to the right lateral parietal region and the right primary motor cortex. The enhanced neocortex-cerebellum connections were highly associated with disease progression, including duration and symptomatic deterioration. Tractography probabilities from the cerebellar to parietal and sensorimotor areas were significantly negatively correlated with motor abilities in patients with SCA3.

CONCLUSION

To our knowledge, this study is the first to reveal that disrupting the neocortex-cerebellum loop can cause SCA3-induced motor dysfunctions. The specific interaction between the cerebellar-thalamo-cortical and cortico-ponto-cerebellar pathways in patients with SCA3 and its relationship with ataxia symptoms provides a new direction for future research.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

Direct Retrieval of Orthographic Representations in Chinese Handwritten Production: Evidence from a Dynamic Causal Modeling Study

This present study identified an optimal model representing the relationship between orthography and phonology in Chinese handwritten production using dynamic causal modeling, and further explored how this model was modulated by word frequency and syllable frequency. Each model contained five volumes of interest in the left hemisphere (angular gyrus [AG], inferior frontal gyrus [IFG], middle frontal gyrus [MFG], superior frontal gyrus [SFG], and supramarginal gyrus [SMG]), with the IFG as the driven input area. Results showed the superiority of a model in which both the MFG and the AG connected with the IFG, supporting the orthography autonomy hypothesis. Word frequency modulated the AG → SFG connection (information flow from the orthographic lexicon to the orthographic buffer), and syllable frequency affected the IFG → MFG connection (information transmission from the semantic system to the phonological lexicon). This study thus provides new insights into the connectivity architecture of neural substrates involved in writing.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Abnormal functional connectivity of the putamen in obsessive-compulsive disorder

The putamen has been proposed to play a critical role in the development of obsessive-compulsive disorder (OCD). The primary objective of this study was to examine the resting-state regional activity and functional connectivity patterns of the putamen in individuals diagnosed with OCD. To achieve this, we employed resting-state functional magnetic resonance imaging (rs-fMRI) to collect data from a sample of 45 OCD patients and 53 healthy control participants. We aimed to use the regional amplitude of low-frequency fluctuation (ALFF) analysis to generate the ROI masks of the putamen and then conduct the whole brain functional connectivity of the putamen in individuals with OCD. Compared to controls, the OCD group demonstrated decreased ALFF in bilateral putamen. The right putamen also displayed decreased FC with the left putamen extending to the inferior frontal gyrus (IFG), bilateral precuneus extending to calcarine, the right middle occipital cortex extending to the right middle temporal cortex, and the left middle occipital gyrus. The decreased connectivity between the right putamen and the left IFG was negatively correlated with Yale-Brown Obsessive Compulsive scale (Y-BOCS) Obsession Scores. This study aimed to reveal the putamen changes in resting-state activity and connectivity in OCD patients, highlighting the significance of aberrant ALFF/FC of the putamen is a key characteristic of OCD.

3D Multimodal Fusion Network With Disease-Induced Joint Learning for Early Alzheimer's Disease Diagnosis

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Functional and structural abnormalities of thalamus in individuals at early stage of schizophrenia

BACKGROUND

Thalamic abnormalities in schizophrenia are recognized, alongside cognitive deficits. However, the current findings about these abnormalities during the prodromal period remain relatively few and inconsistent. This study applied multimodal methods to explore the alterations in thalamic function and structure and their relationship with cognitive function in first-episode schizophrenia (FES) patients and ultra-high-risk (UHR) individuals, aiming to affirm the thalamus's role in schizophrenia development and cognitive deficits.

METHODS

75 FES patients, 60 UHR individuals, and 60 healthy controls (HC) were recruited. Among the three groups, gray matter volume (GMV) and functional connectivity (FC) were evaluated to reflect the structural and functional abnormalities in the thalamus. Pearson correlation was used to calculate the association between these abnormalities and cognitive impairments.

RESULTS

No significant difference in GMV of the thalamus was found among the abovementioned three groups. Compared with HC individuals, FES patients had decreased thalamocortical FC mostly in the thalamocortical triple network, including the default mode network (DMN), salience network (SN), and executive control network (ECN). UHR individuals had similar but milder dysconnectivity as the FES group. Furthermore, FC between the left thalamus and right putamen was significantly correlated with execution speed and attention in the FES group.

CONCLUSIONS

Our findings revealed decreased thalamocortical FC associated with cognitive deficits in FES and UHR subjects. This improves our understanding of the functional alterations in thalamus in prodromal stage of schizophrenia and the related factors of the cognitive impairment of the disease.

TRIAL REGISTRATION

ClinicalTrials.govNCT03965598; https://clinicaltrials.gov/ct2/show/NCT03965598.

Persistent brain metabolic impairment in long COVID patients with persistent clinical symptoms: a nine-month follow-up [18F]FDG-PET study

PURPOSE

A hypometabolic profile involving the limbic areas, brainstem and cerebellum has been identified in long COVID patients using [18F]fluorodeoxyglucose (FDG)-PET. This study was conducted to evaluate possible recovery of brain metabolism during the follow-up of patients with prolonged symptoms.

METHODS

Fifty-six adults with long COVID who underwent two brain [18F]FDG-PET scans in our department between May 2020 and October 2022 were retrospectively analysed, and compared to 51 healthy subjects. On average, PET1 was performed 7 months (range 3-17) after acute COVID-19 infection, and PET2 was performed 16 months (range 8-32) after acute infection, because of persistent severe or disabling symptoms, without significant clinical recovery. Whole-brain voxel-based analysis compared PET1 and PET2 from long COVID patients to scans from healthy subjects (p-voxel < 0.001 uncorrected, p-cluster < 0.05 FWE-corrected) and PET1 to PET2 (with the same threshold, and secondarily with a less constrained threshold of p-voxel < 0.005 uncorrected, p-cluster < 0.05 uncorrected). Additionally, a region-of-interest (ROI) semiquantitative anatomical approach was performed for the same comparisons (p < 0.05, corrected).

RESULTS

PET1 and PET2 revealed voxel-based hypometabolisms consistent with the previously reported profile in the literature. This between-group analysis comparing PET1 and PET2 showed minor improvements in the pons and cerebellum (8.4 and 5.2%, respectively, only significant under the less constrained uncorrected p-threshold); for the pons, this improvement was correlated with the PET1-PET2 interval (r = 0.21, p < 0.05). Of the 14,068 hypometabolic voxels identified on PET1, 6,503 were also hypometabolic on PET2 (46%). Of the 7,732 hypometabolic voxels identified on PET2, 6,094 were also hypometabolic on PET1 (78%). The anatomical ROI analysis confirmed the brain hypometabolism involving limbic region, the pons and cerebellum at PET1 and PET2, without significant changes between PET1 and PET2.

CONCLUSION

Subjects with persistent symptoms of long COVID exhibit durable deficits in brain metabolism, without progressive worsening.

Tumors Affect the Metabolic Connectivity of the Human Brain Measured by 18 F-FDG PET

PURPOSE

18 F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity.

PATIENTS AND METHODS

Forty-three patients with left hemispheric tumors and 18 F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test ( P < 0.05).

RESULTS

Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC ( P < 0.001), except assortativity and betweenness centrality ( P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right ( P < 0.01), with the exception of betweenness centrality ( P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics ( P < 0.001) than temporal tumors, but lower betweenness centrality ( P = 4.5 -7 ). Patients with high KPS showed higher distance local efficiency ( P = 0.01), rich club coefficient ( P = 0.0048), clustering coefficient ( P = 0.00032), betweenness centrality ( P = 0.008), and similarity ( P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses.

CONCLUSIONS

Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

Baseline brain volume predicts home-based transcranial direct current stimulation effects on inattention in adults with attention-deficit/hyperactivity disorder

BACKGROUND

Home-based transcranial direct current stimulation (Hb-tDCS) is a non-invasive brain stimulation technique that utilizes low-intensity electric currents delivered via scalp electrodes to modulate brain activity. It holds significant promise for addressing inattention in adults with attention-deficit/hyperactivity disorder (ADHD). However, its effectiveness varies among individuals, and predicting outcomes remains uncertain, partially due to the influence of individual differences in ADHD-related brain anatomy.

METHODS

We analyzed data from a subsample, composed by twenty-nine adult patients with ADHD, of the Treatment of Inattention Symptoms in Adult Patients with ADHD (TUNED) trial. Fourteen patients underwent active anodal right cathodal left dorsolateral prefrontal cortex (DLPFC) Hb-tDCS for 4 weeks and fifteen received sham-related tDCS intervention. Inattention outcome was evaluated at both baseline and endpoint (4th week). Baseline structural measures of the DLPFC, anterior cingulate cortex (ACC) and subcortical structures, previously associated with ADHD, were quantified. Several linear mixed models, with a three-way interaction between the fixed predictors brain volume or thickness, time, and treatment were calculated. Multiple comparison corrections were applied using the Benjamini-Hochberg method.

RESULTS

Baseline volume of the left DLPFC regions middle frontal gyrus (t (25) = 3.33, p-adjusted = 0.045, Cohen's d = 1.33, 95% CI = [0.45, 2.19]), inferior frontal gyrus (orbital part) (t (25) = 3.10, p-adjusted = 0.045, Cohen's d = 1.24, 95% CI = [0.37, 2.08]), and of the left ACC supragenual (t (25) = 3.15, p-adjusted = 0.045, Cohen's d = 1.26, 95% CI = [0.39, 2.11]) presented significant association with the inattentive score improvement only in the active tDCS group. More specifically, the smaller these regions were, the more the symptoms improved following anodal right cathodal left DLPFC Hb-tDCS.

CONCLUSION

Hb-tDCS was associated with greater improvement in brain areas related to attention regulation. Brain MRI can be potentially used to predict clinical response to tDCS in ADHD adults.

Coupling motor evoked potentials and brain [18F]FDG-PET in Amyotrophic Lateral Sclerosis: preliminary findings on disease severity

BACKGROUND

The diagnosis of amyotrophic lateral sclerosis (ALS) is primarily clinical, supported by the electromyographic examination to reveal signs of lower motor neuron damage. Identifying reliable markers of upper motor neuron (UMN) involvement is challenging. On this regard, the role of transcranial magnetic stimulation-induced motor-evoked potentials (TMS-MEPs), and its relationship with UMN burden, is still under investigation.

OBJECTIVE

To evaluate the ability of TMS-MEPs in delineating the neurophysiological UMN damage, and to determine the relationship between TMS-MEPs and [18F]FDG-PET measures of neural dysfunction.

METHODS

We retrospectively selected 13 ALS patients who underwent, during the diagnostic process, the TMS-MEPs and [18F]FDG-PET scans. Demographic and clinical data were collected. For the MEP evaluation, we considered normal MEP, absent MEP, or significantly increased central-motor-conduction-time. For [18F]FDG-PET, we conducted voxel-wise analyses, both at single-subject and group levels, exploring hypometabolism and hypermetabolism patterns in comparison with a large dataset of healthy controls (HC).

RESULTS

Based on TMS-MEPs, we identified 4/13 patients with normal MEP in all limbs (GROUP-NO), while 9/13 had an abnormal MEP in at least one limb (GROUP-AB). Despite the [18F]FDG-PET single-subject analysis revealed heterogenous expression of regional hypo- and hyper-metabolism patterns in the patients, the group-level analysis revealed a common hypometabolism, involving the precentral gyrus and the supplementary motor area, the paracentral lobule and the anterior cingulate cortex in the GROUP-AB. Moreover, exclusively for the GROUP-AB compared with HC, a relative hypermetabolism was observed in the right cerebellum, right inferior and middle temporal gyrus. The GROUP-NO showed no specific cluster of hypo- and hyper-metabolism compared to HC.

CONCLUSION

This study showed altered brain metabolism only in the ALS group with abnormal MEPs, suggesting an association between the two biomarkers in defining the UMN damage.

A novel graph neural network method for Alzheimer's disease classification

Alzheimer's disease (AD) is a chronic neurodegenerative disease. Early diagnosis are very important to timely treatment and delay the progression of the disease. In the past decade, many computer-aided diagnostic (CAD) algorithms have been proposed for classification of AD. In this paper, we propose a novel graph neural network method, termed Brain Graph Attention Network (BGAN) for classification of AD. First, brain graph data are used to model classification of AD as a graph classification task. Second, a local attention layer is designed to capture and aggregate messages of interactions between node neighbors. And, a global attention layer is introduced to obtain the contribution of each node for graph representation. Finally, using the BGAN to implement AD classification. We train and test on two open public databases for AD classification task. Compared to classic models, the experimental results show that our model is superior to six classic models. We demonstrate that BGAN is a powerful classification model for AD. In addition, our model can provide an analysis of brain regions in order to judge which regions are related to AD disease and which regions are related to AD progression.

Impact of Sleeve Gastrectomy on Brain Structural Integrity

INTRODUCTION

Potential brain structural differences in people with obesity (PwO) who achieve over or less than 50% excess weight loss (EWL) after sleeve gastrectomy (SG) are currently unknown. We compared measures of gray matter volume (GMV) and white matter (WM) microstructural integrity of PwO who achieved over or less than 50% EWL after SG with a group of controls with obesity (CwO) without a past history of metabolic bariatric surgery.

METHODS

Sixty-two PwO underwent 1.5 T MRI scanning: 24 who achieved more than 50% of EWL after SG ("group a"), 18 who achieved less than 50% EWL after SG ("group b"), and 20 CwO ("group c"). Voxel-based morphometry and tract-based spatial Statistics analyses were performed to investigate GMV and WM differences among groups. Multiple regression analyses were performed to investigate relationships between structural and psychological measures.

RESULTS

Group a demonstrated significantly lower GMV loss and higher WM microstructural integrity with respect to group b and c in some cortical regions and several WM tracts. Positive correlations were observed in group a between WM integrity and several psychological measures; the lower the WM integrity, the higher the mental distress, emotional dysregulation, and binge eating behavior.

CONCLUSION

The present results gain a new understanding of the neural mechanisms of outcome in patients who undergo SG. We found limited GMV changes and extensive WM microstructural differences between PwO who achieved over or less than 50% EWL after SG, which may be due to higher vulnerability of WM to the metabolic dysfunction present in PwO.

Cortical beta oscillations help synchronise muscles during static posture holding in healthy motor control

How cortical oscillations are involved in the coordination of functionally coupled muscles and how this is modulated by different movement contexts (static vs dynamic) remains unclear. Here, this is investigated by recording high-density electroencephalography (EEG) and electromyography (EMG) from different forearm muscles while healthy participants (n = 20) performed movement tasks (static and dynamic posture holding, and reaching) with their dominant hand. When dynamic perturbation was applied, beta band (15-35 Hz) activities in the motor cortex contralateral to the performing hand reduced during the holding phase, comparative to when there was no perturbation. During static posture holding, transient periods of increased cortical beta oscillations (beta bursts) were associated with greater corticomuscular coherence and increased phase synchrony between muscles (intermuscular coherence) in the beta frequency band compared to the no-burst period. This effect was not present when resisting dynamic perturbation. The results suggest that cortical beta bursts assist synchronisation of different muscles during static posture holding in healthy motor control, contributing to the maintenance and stabilisation of functional muscle groups. Theoretically, increased cortical beta oscillations could lead to exaggerated synchronisation in different muscles making the initialisation of movements more difficult, as observed in Parkinson's disease.

Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study

BACKGROUND

Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features.

METHODS

The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years.

RESULTS

A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects.

CONCLUSIONS

The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.

Neuroimaging analysis reveals distinct cerebral perfusion responses to fasting-postprandial metabolic switching in Alzheimer's disease patients

AIMS

Extended fasting-postprandial switch intermitting time has been shown to affect Alzheimer's disease (AD). Few studies have investigated the cerebral perfusion response to fasting-postprandial metabolic switching (FMS) in AD patients. We aimed to evaluate the cerebral perfusion response to FMS in AD patients.

METHODS

In total, 30 AD patients, 32 mild cognitive impairment (MCI) patients, and 30 healthy control individuals (HCs) were included in the quantification of cerebral perfusion via cerebral blood flow (CBF). The cerebral perfusion response to FMS was defined as the difference (ΔCBF) between fasting and postprandial CBF.

RESULTS

Patients with AD had a regional negative ΔCBF in the anterior temporal lobe, part of the occipital lobe and the parietal lobe under FMS stimulation, whereas HCs had no significant ΔCBF. The AD patients had lower ΔCBF values in the right anterior temporal lobe than the MCI patients and HCs. ΔCBF in the anterior temporal lobe was negatively correlated with cognitive severity and cognitive reserve factors in AD patients.

CONCLUSIONS

AD patients exhibited a poor ability to maintain cerebral perfusion homeostasis under FMS stimulation. The anterior temporal lobe is a distinct area that responds to FMS in AD patients and negatively correlates with cognitive function.

Time to bury the chisel: a continuous dorsal association tract system

The arcuate fasciculus may be subdivided into a tract directly connecting frontal and temporal lobes and a pair of indirect subtracts in which the fronto-temporal connection is mediated by connections to the inferior parietal lobe. This tripartition has been advanced as an improvement over the centuries-old consensus that the lateral dorsal association fibers form a continuous system with no discernible discrete parts. Moreover, it has been used as the anatomical basis for functional hypotheses regarding linguistic abilities. Ex hypothesi, damage to the indirect subtracts leads to deficits in the repetition of multi-word sequences, whereas damage to the direct subtract leads to deficits in the immediate reproduction of single multisyllabic words. We argue that this partitioning of the dorsal association tract system enjoys no special anatomical status, and the search for the anatomical substrates of linguistic abilities should not be constrained by it. Instead, the merit of any postulated partitioning should primarily be judged on the basis of whether it enlightens or obfuscates our understanding of the behavior of patients in which individual subtracts are damaged.

Transcriptional patterns of amygdala functional connectivity in first-episode, drug-naïve major depressive disorder

Previous research has established associations between amygdala functional connectivity abnormalities and major depressive disorder (MDD). However, inconsistencies persist due to limited sample sizes and poorly elucidated transcriptional patterns. In this study, we aimed to address these gaps by analyzing a multicenter magnetic resonance imaging (MRI) dataset consisting of 210 first-episode, drug-naïve MDD patients and 363 age- and sex-matched healthy controls (HC). Using Pearson correlation analysis, we established individualized amygdala functional connectivity patterns based on the Automated Anatomical Labeling (AAL) atlas. Subsequently, machine learning techniques were employed to evaluate the diagnostic utility of amygdala functional connectivity for identifying MDD at the individual level. Additionally, we investigated the spatial correlation between MDD-related amygdala functional connectivity alterations and gene expression through Pearson correlation analysis. Our findings revealed reduced functional connectivity between the amygdala and specific brain regions, such as frontal, orbital, and temporal regions, in MDD patients compared to HC. Importantly, amygdala functional connectivity exhibited robust discriminatory capability for characterizing MDD at the individual level. Furthermore, we observed spatial correlations between MDD-related amygdala functional connectivity alterations and genes enriched for metal ion transport and modulation of chemical synaptic transmission. These results underscore the significance of amygdala functional connectivity alterations in MDD and suggest potential neurobiological mechanisms and markers for these alterations.

The Neurofunctional Correlates of Morphosyntactic and Thematic Impairments in Aphasia: A Systematic Review and Meta-analysis

Lesion-symptom studies in persons with aphasia showed that left temporoparietal damage, but surprisingly not prefrontal damage, correlates with impaired ability to process thematic roles in the comprehension of semantically reversible sentences (The child is hugged by the mother). This result has led to challenge the time-honored view that left prefrontal regions are critical for sentence comprehension. However, most studies focused on thematic role assignment and failed to consider morphosyntactic processes that are also critical for sentence processing. We reviewed and meta-analyzed lesion-symptom studies on the neurofunctional correlates of thematic role assignment and morphosyntactic processing in comprehension and production in persons with aphasia. Following the PRISMA checklist, we selected 43 papers for the review and 27 for the meta-analysis, identifying a set of potential bias risks. Both the review and the meta-analysis confirmed the correlation between thematic role processing and temporoparietal regions but also clearly showed the involvement of prefrontal regions in sentence processing. Exploratory meta-analyses suggested that both thematic role and morphosyntactic processing correlate with left prefrontal and temporoparietal regions, that morphosyntactic processing correlates with prefrontal structures more than with temporoparietal regions, and that thematic role assignment displays the opposite trend. We discuss current limitations in the literature and propose a set of recommendations for clarifying unresolved issues.

Right-to-left shunt-associated brain functional changes in migraine: evidences from a resting-state FMRI study

Background

Migraine, a neurological condition perpetually under investigation, remains shrouded in mystery regarding its underlying causes. While a potential link to Right-to-Left Shunt (RLS) has been postulated, the exact nature of this association remains elusive, necessitating further exploration.

Methods

The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and functional connectivity (FC) were employed to investigate functional segregation and functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics.

Results

Compared with migraine patients without RLS, patients with RLS exhibited a significant increase in the ALFF within left middle occipital and superior occipital gyrus; In migraine patients with RLS, significantly reduced brain functional connectivity was found, including the connectivity between default mode network and visual network, ventral attention network, as well as the intra-functional connectivity of somatomotor network and its connection with the limbic network, and also the connectivity between the left rolandic operculum and the right middle cingulate gyrus. Notably, a significantly enhanced functional connectivity between the frontoparietal network and the ventral attention network was found in migraine with RLS; Patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Additionally, these patients displayed a diminished nodal degree in the occipital lobe and reduced nodal efficiency within the fusiform gyrus; Further, the study found positive correlations between ALFF in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity.

Conclusion

RLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.

Neurological activation during verbal fluency task and resting-state functional connectivity abnormalities in obsessive-compulsive disorder: a functional near-infrared spectroscopy study

Objective

This study aims to investigate the activation of frontotemporal functional brain areas in patients with Obsessive-Compulsive Disorder (OCD) during a Verbal Fluency Task (VFT), and to compare their brain functional connectivity in a resting state with that of healthy controls. The goal is to deepen our understanding of the neuropathological mechanisms underlying OCD.

Methods

32 patients with OCD and 32 controls matched for age, gender, handedness, and years of education participated in this study, they were divided into OCD group and healthy comtrol group. We conducted VFT task tests and 10-minute resting state tests on both groups by using functional Near-Infrared Spectroscopy (fNIRS). The VFT was utilized to assess the activation (beta values) and the integral and centroid values of the frontal and bilateral temporal lobes, including brain areas BA9 and 46 (dorsolateral prefrontal cortex), BA10 (frontal pole), BA45 (inferior frontal gyrus), BA21 (middle temporal gyrus), and BA22 (superior temporal gyrus). We evaluated the functional connectivity levels of these areas during the resting state. Differences in these measures between OCD patients and healthy controls were analyzed using two-sample independent t-tests and non-parametric Mann-Whitney U tests.

Results

During VFT, OCD had smaller integral values(z=5.371, p<0.001; t=4.720, p<0.001), and larger centroid values(t=-2.281, p=0.026; z=-2.182, p=0.029) compared to healthy controls, along with a reduced number of activated channels detected by fNIRS. Additionally, activation values (β) in various functional brain areas, including BA9, BA46, BA10, BA45, BA21, and BA22, were significantly lower in the OCD group (all p< 0.01). In the resting state, notable disparities in functional connectivity were observed between the inferior frontal gyrus (IFG) and dorsolateral prefrontal cortex (DLPFC) in the OCD group, in comparison to the control group. Specifically, there was a significant increase in connectivity between the left IFG and right DLPFC, suggesting the presence of altered connectivity patterns in these areas.

Conclusions

The study highlights significant disparities in neural activation and functional connectivity between OCD patients and healthy controls during VFT. Specifically, reduced activation was noted in the frontal and bilateral temporal lobes of OCD patients, alongside alterations in resting-state functional connectivity between the IFG and DLPFC. These findings contribute to our understanding of the neurobiological underpinnings of OCD and may guide future therapeutic strategies.

The interaction of oxytocin and nicotine addiction on psychosocial stress: an fMRI study

The anxiolytic effect of oxytocin (OXT) on psychosocial stress has been well documented, but the effectiveness under the interference of other factors still requires in-depth research. Previous studies have shown that nicotine addiction interacts with OXT on psychosocial stress on the behavioral level. However, the underlying neural mechanism of interaction between OXT and nicotine addiction on psychosocial stress has not been examined, and we conducted two experiments to reveal it. Firstly, after intranasal administration of randomized OXT or placebo (saline), a group of healthy participants (n = 27) and a group of smokers (n = 26) completed the Montreal Imaging Stress Task (MIST) in an MRI scanner. Secondly, a group of smokers (n = 22) was recruited to complete a transcranial direct current stimulation (tDCS) experiment, in which anodal tDCS was applied on subjects' anterior right superior temporal gyrus (rSTG). In both experiment, subjective stress ratings, salivary cortisol samples and the amount of daily cigarette consumption were obtained from each participant. Analysis of variance were applied on both behavioral and neural data to examine the effects of OXT and nicotine addiction, and correlation analysis were used to examine relationships between neural and behavioral data. In first fMRI experiment, analysis of variance (ANOVA) revealed an interaction of OXT and nicotine addiction on subjective stress. In smokers, OXT failed to suppress the elevation of subjective stress and craving ratings after psychosocial stress. A voxel-wise ANOVA of fMRI data identified an interaction between OXT and nicotine addiction in anterior rSTG, and its functional connectivity with right middle frontal gyrus. Correlations between this functional connectivity and subjective psychosocial stress were also found abnormal in smokers. In second tDCS experiment, we found that under tDCS, OXT successfully suppressed the elevation of subjective stress and craving ratings after stress. In summary, we found that nicotine addiction blocked OXT's anxiolytic on psychosocial stress, which was related to abnormalities in anterior rSTG. By applying anodal tDCS on anterior rSTG, OXT's anxiolytic effect was restored in smokers. These findings will support further development on oxytocin's intervention of psychosocial stress in nicotine addiction, and provides essential information for indicating OXT's effectiveness.

Next move in movement disorders: neuroimaging protocols for hyperkinetic movement disorders

Introduction

The Next Move in Movement Disorders (NEMO) study is an initiative aimed at advancing our understanding and the classification of hyperkinetic movement disorders, including tremor, myoclonus, dystonia, and myoclonus-dystonia. The study has two main objectives: (a) to develop a computer-aided tool for precise and consistent classification of these movement disorder phenotypes, and (b) to deepen our understanding of brain pathophysiology through advanced neuroimaging techniques. This protocol review details the neuroimaging data acquisition and preprocessing procedures employed by the NEMO team to achieve these goals.

Methods and analysis

To meet the study's objectives, NEMO utilizes multiple imaging techniques, including T1-weighted structural MRI, resting-state fMRI, motor task fMRI, and 18F-FDG PET scans. We will outline our efforts over the past 4 years to enhance the quality of our collected data, and address challenges such as head movements during image acquisition, choosing acquisition parameters and constructing data preprocessing pipelines. This study is the first to employ these neuroimaging modalities in a standardized approach contributing to more uniformity in the analyses of future studies comparing these patient groups. The data collected will contribute to the development of a machine learning-based classification tool and improve our understanding of disorder-specific neurobiological factors.

Ethics and dissemination

Ethical approval has been obtained from the relevant local ethics committee. The NEMO study is designed to pioneer the application of machine learning of movement disorders. We expect to publish articles in multiple related fields of research and patients will be informed of important results via patient associations and press releases.