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

Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network model based on empirical structural connectivity data using the Wendling model, which successfully simulates both pathological and physiological electroencephalography (EEG) signals. Building on this, the EEG functional network is constructed using the phase lag index, effectively characterizing the functional connectivity. Our modeling results indicate that EEG activity and functional network properties undergo significant changes by globally increasing synaptic coupling strength. Specifically, it leads to abnormal neural oscillations clinically reported in ASD, including the decreased dominant frequency, the decreased relative power in the α band and the increased relative power in the δ+θ band, particularly in the frontal lobe. At the same time, the clustering coefficient and global efficiency of the functional network decrease, while the characteristic path length increases, suggesting that the functional network of ASD is inefficient and poorly integrated. Additionally, we find insufficient functional connectivity across multiple brain regions in ASD, along with decreased wavelet coherence in the α band within the frontal lobe and between the frontal and temporal lobes. Considering that most of the synaptic increases in ASD are limited, brain regions are further randomly selected to increase the local synaptic coupling strength. The results show that disturbances in local brain regions can also facilitate the development of ASD. This study reveals the intrinsic link between synapse increase and abnormal brain activity in ASD, and inspires treatments related to synapse pruning.

Mechanistic study of saltiness enhancement induced by three characteristic volatiles identified in Jinhua dry-cured ham using electroencephalography (EEG)

Excessive salt intake is a pressing food health issue, and odor-induced saltiness enhancement (OISE) is a novel strategy for targeted salt reduction. Understanding the neural mechanisms of OISE is essential for salt reduction. In this study, the mechanism of saltiness enhancement induced by three volatile organic compounds (VOCs) identified in Jinhua dry ham was investigated in 20 panelists using electroencephalography (EEG). The study demonstrated that VOCs enhanced salty taste perception, primarily through low-frequency brain waves. Source localization revealed occipital lobe activation during salty taste recognition, while OISE stimuli enhanced activity in the primary and secondary gustatory cortices. Additionally, VOCs enhanced phase synchronization among activated brain regions, as indicated by functional connectivity. This study enhances the understanding of olfactory-gustatory interactions and provides a neurological basis for the effects of OISE.

Frequency-specific network connectivity impairments linked to suicide attempts in major depressive disorder during the GO/NOGO task

BACKGROUND

Major depressive disorder (MDD) is a main risk factor of suicide, emphasizing the urgent need for understanding the neurobiological mechanisms underlying suicide attempts (SAs) in depressive patients. We hypothesized that aberrant frequency-specific functional connectivity patterns underlying an executive and inhibition task might be associated with SA in depression.

METHODS

The current study enrolled 143 subjects including 43 healthy controls and 87 patients with MDD (43 patients with SA and 44 without SA), who attended a GO/NOGO task during the magnetoencephalography recording. Time-frequency features in the whole-brain sensors and frequency-specific brain network connectivity patterns were estimated. Behavioral data was recorded during the tasks and neurocognitive assessments were conducted.

RESULTS

The SA group exhibited poorest behavioral and neurocognitive assessments performances. Decreased alpha/beta oscillations of the GO condition and increased alpha/beta oscillations of NOGO condition were observed in the SA group. Hypo-activated frontal-limbic connectivity in the alpha band and frontal-occipital connectivity in the beta band were observed in the SA group during the GO trials, meanwhile, hyper-activated frontal-temporal connectivity in the alpha band and frontal-parietal connectivity in the beta band were associated with SA during the NOGO trials. Frequency-specific features were correlated with the severity of suicide risk, neurocognitive assessments, and could be used to predict potential SAs.

CONCLUSIONS

Neuroimaging and neurocognitive evidences supported altered alpha/beta oscillations and connectivity patterns associated with SA in depression, suggesting that depressive patients with SA might exhibit impaired cognitive control functions.

Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data

Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, primarily manifesting in childhood. It is characterized by atypical and repetitive behaviors. Conventional diagnostic methods mainly rely on questionnaire surveys and behavioral observations, which are prone to misdiagnosis due to their subjective nature. With advancements in medical imaging, MR imaging-based diagnostics have emerged as a more objective alternative. In this paper, we propose a Hierarchical Neural Network model for ASD identification, termed ASD-HNet, which hierarchically extracts features from functional brain networks based on resting-state functional magnetic resonance imaging (rs-fMRI) data. This hierarchical approach enhances the extraction of brain representations, improving diagnostic accuracy and aiding in the identification of brain regions associated with ASD. Specifically, features are extracted at three levels, i.e., the local region of interest (ROI) scale, the community scale, and the global representation scale. At the ROI scale, graph convolution is employed to transfer features between ROIs. At the community scale, functional gradients are introduced, and a K-Means clustering algorithm is applied to group ROIs with similar functional gradients into communities. Features from ROIs within the same community are then extracted to characterize the communities. At the global representation scale, we extract global features from the whole community-scale brain networks to represent the entire brain. We validate the effectiveness of the ASD-HNet model using the publicly available Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, ADHD-200,dataset and ABIDE-II dataset. Extensive experimental results demonstrate that ASD-HNet outperforms existing baseline methods. The code is available at https://github.com/LYQbyte/ASD-HNet.

Disrupted structural network resilience in atherosclerosis: A large-scale cohort study

BACKGROUND

Atherosclerosis is a major factor in cognitive decline among aging individuals and is frequently linked to the accumulation of white matter hyperintensities. Brain resilience, which represents the brain's capacity to withstand external disruptions, remains poorly understood in terms of how atherosclerosis impacts it and, in turn, influences cognition. Here, we investigated the relationship between atherosclerosis, white matter hyperintensities, and structural network resilience, along with their combined effects on cognitive performance.

METHODS

We utilized data from the large-scale community cohort Polyvascular Evaluation for Cognitive Impairment and Vascular Events (n = 2160). Whole-brain structural connections were constructed, and structural disconnections were simulated based on white matter hyperintensities. SNR, serving as a marker to quantify structural network resilience, is defined by the similarity of hub nodes between the original network and its disconnected counterpart.

RESULTS

SNR showed higher odds ratios compared to white matter hyperintensities in relation to arterial status. Additionally, chain mediation analysis indicated that cognitive decline associated with atherosclerosis was partially mediated by both white matter hyperintensities and structural network resilience. Atherosclerosis accelerates the degradation of brain structural network resilience as age increases.

CONCLUSIONS

These findings suggest that SNR could offer complementary insights into cognitive decline caused by atherosclerosis and serve as a potential biomarker of brain health in atherosclerotic conditions. Additionally, SNR may act as an indicator for guiding the selection of future therapies for atherosclerosis.

Developmental patterns of white matter functional networks in neonates

In recent years, the development of neonatal brain networks has become a research focus, with traditional studies primarily emphasizing gray matter (GM) functional networks. This study systematically explores the developmental characteristics of white matter (WM) functional networks in neonates. Utilizing data from the third release of the Developing Human Connectome Project (dHCP), we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 730 full-term and 157 preterm neonates. We successfully identified ten large-scale WM functional networks and validated their correspondence with established WM fiber tracts using diffusion tensor imaging (DTI). We examined WM functional networks from two dimensions: network functional connectivity and spontaneous activity, incorporating four factors: preterm birth status, age, sex, and hemispheric differences. The results indicate that WM network functional connectivity significantly increases with age, with preterm infants exhibiting lower connectivity than full-term infants, whereas no significant differences were observed between sexes or hemispheres. Regarding spontaneous activity, preterm infants showed lower amplitude in the low-frequency range, whereas in the high-frequency range, their amplitude distribution was more unstable and dispersed. Additionally, certain differences in spontaneous activity were observed between hemispheres and sexes. These findings provide novel insights into the early development of neonatal brain networks and hold significant implications for clinical interventions and treatment strategies for preterm infants.

Altered brain activation during memory retrieval mediates the relationship between developmental trauma and psychotic symptom severity

BACKGROUND

Developmental trauma (DT) and poorer episodic memory performance are associated with increased risk of psychotic symptoms, but the mechanisms underlying these associations remain to be established. We sought to investigate whether memory performance and fMRI activity during the retrieval phase of an associative episodic memory task statistically mediated the relationship between trauma multiplicity and psychotic symptom severity in youth at clinical high risk for psychosis.

METHODS

Measures from 795 participants in the North American Prodrome Longitudinal Study (phase two) were analysed. This included the Childhood Trauma and Abuse scale, neurocognitive measures, the Scale of Psychosis-Risk Symptoms and, in a subsample, neural activation from five regions of interest associated with memory processing (n = 219) during a paired-associate memory task (data from this task; n = 198). Linear regressions were conducted to measure whether trauma multiplicity predicted subclinical delusion and hallucination severity and neurocognitive performance, and neurocognitive measures predicted subclinical hallucination or delusion severity. We used mediation analysis when all paths were significant.

RESULTS

Average functional activation in the five memory-associated regions mediated 8.74 % of the association between DT and subclinical hallucination severity, and 7.02 % between DT and delusion severity. Inferior parietal lobe activity mediated 10.08 % of the association between DT and subclinical hallucination severity, and 8.7 % between DT and subclinical delusion severity.

CONCLUSIONS

These findings suggest a role of episodic memory processing and inferior parietal lobe activation in the association between DT and psychosis. Focusing further on these measures could provide insight into the underlying mechanism of this association, and have clinical implications in trauma-exposed individuals with psychosis.

Impact of Intermittent Theta Burst Stimulation on Pain Relief and Brain Connectivity in Chronic Low Back Pain

BACKGROUND

This randomised clinical trial investigated the effect of intermittent theta burst stimulation (iTBS) over the dorsolateral prefrontal cortex (DLPFC) on pain alleviation in patients with chronic low back pain (CLBP) and its underlying mechanisms.

METHODS

Forty CLBP patients were randomly assigned to receive either active or sham iTBS combined with core stability exercise. Pain assessments were completed before and after the intervention. Eleven patients from each group underwent resting-state functional magnetic resonance imaging scans pre- and post-intervention to analyse DLPFC activation and connectivity with other brain regions.

RESULTS

The active iTBS group had a greater pain reduction than the sham group (p = 0.05, 95% CI: -0.009 to 1.109). In the active and sham groups, 80% (16/20) and 40% (8/20) reached the minimal clinically important difference, respectively, with a number needed to treat of 2.5. For the Fear-Avoidance Beliefs Questionnaire, there was a significant difference between the two groups (p = 0.011, r = 0.40). The active iTBS group showed a significantly enhanced functional connectivity between the left DLPFC and the right cerebellum, as well as both occipital gyri (voxel-level, p < 0.001; cluster-level familywise error rate, p < 0.01). Spearman's correlation analysis showed a significant negative correlation between Numerical Rating Scale and the FC of the left DLPFC and the right cerebellum (rho = -0.55, p = 0.008), the right (rho = -0.439, p = 0.01), and left occipital gyri (rho = -0.45, p = 0.034).

CONCLUSION

iTBS may alleviate pain in CLBP patients by enhancing DLPFC connectivity with the cerebellum and occipital gyrus.

SIGNIFICANCE

This study showed a facilitatory effect of iTBS on alleviating CLBP, which might be modulated by brain functional connectivity. Trial Registration Chinese Clinical Trial Registry: ChiCTR2200064899.

Correlation of early-phase β-amyloid positron-emission-tomography and neuropsychological testing in patients with Alzheimer's disease

PURPOSE

Clinical staging in individuals with Alzheimer's disease (AD) typically relies on neuropsychological testing. Recognizing the imperative for an objective measure of clinical AD staging, regional perfusion in early-phase β-amyloid-PET may aid as a cost-efficient index for the assessment of neurodegeneration severity in patients with Alzheimer's disease.

METHODS

Regional perfusion deficits in early-phase β-amyloid-PET as well as neuropsychological testing (max. 90 days delay) were evaluated in 82 patients with biologically defined AD according to the ATN classification. In reference to the Braak staging system patients were classified into the groups stage0, stageI-II+, stageI-IV+, stageI-VI+, and stageatypical+ according to regional perfusion deficits in regions of interest (ROIs) published by the Alzheimer's Disease Neuroimaging Initiative. Multiple regression analysis controlling for age, gender, and education was used to evaluate the association of regional z-scores on perfusion-phase PET with clinical scores for all patients and with annual decline of cognitive performance in 23 patients with follow-up data.

RESULTS

Patients classified as stage0 and stageI-II+ demonstrated significantly superior neuropsychological performance compared to those classified as stageI-IV+ and stageI-VI+. Lower cognitive performance was associated with decreased perfusion in early-phase β-amyloid-PET globally and regionally, with the most pronounced association identified in the left temporal lobe. Mean z-scores on early-phase PET in temporal and parietal regions offered a robust prediction of future annual decline in MMSE and sum scores of the CERAD-Plus (Consortium to Establish a Registry for Alzheimer's Disease) test battery.

CONCLUSION

Regional and global perfusion deficits in early-phase β-amyloid-PET can serve as an objective index of neurodegeneration severity and may act as prognostic markers of future cognitive decline in AD.

IRMA: Machine learning-based harmonization of 18 F-FDG PET brain scans in multi-center studies

PURPOSE

Center-specific effects in PET brain scans arise due to differences in technical and procedural aspects. This restricts the merging of data between centers and introduces source-specific bias.

METHODS

We demonstrate the use of the recently proposed machine learning method Iterated Relevance Matrix Analysis (IRMA) for harmonization of center-specific effects in brain18 F-Fluorodeoxyglucose (18 F-FDG) PET scans. The center difference is learned by applying IRMA on PCA-based feature vectors of healthy controls (HC), resulting in a subspace V , representing information not comparable between centers, and the remaining subspace U , where no center differences are present. In this proof-of-concept study, we demonstrate the properties of the method using data from four centers. After center-harmonization, a Generalized Matrix Learning Vector Quantization (GMLVQ) model was trained to discriminate between Parkinson's disease, Alzheimer's disease and Dementia with Lewy Bodies.

RESULTS

At the initial IRMA iteration, the system was able to determine the center origin of the four HC cohorts almost perfectly. The method required six iterations, corresponding to a six-dimensional subspace V , to determine the entire center difference. An uncorrected disease classification model was highly biased to center-specific effects, creating a falsely inflated performance when applying internal (cross-) validation. The cross-validation performance of the center-harmonized model remained high, while it generalized significantly better to unseen test cohorts. Furthermore, the framework is highly transparent, providing analytic reconstructions of the correction and visualizations of the data in voxel space.

CONCLUSION

IRMA can be used to learn and disregard center-specific information in features extracted from brain18 F-FDG PET scans, while retaining disease-specific information.

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), respectively.The consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87-0.90, FUNC2T1: 0.67-0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinson's disease.

Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data

The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machine learning models for biological age prediction by using the integrated features from multi-biological data of 140 healthy controls and 43 patients with schizophrenia, including brain MRI, gut microbiome, and blood data. Our results revealed that the models using multi-biological data achieved higher predictive accuracy than those using only brain MRI. Feature interpretability analysis of the optimal model elucidated that the substantial contributions of the frontal lobe, the temporal lobe and the fornix were effective for biological age prediction. Notably, patients with schizophrenia exhibited a pronounced increase in the predicted biological age gap (BAG) when compared to healthy controls. Moreover, the BAG in the SZ group was negatively and positively correlated with the MCCB and PANSS scores, respectively. These findings underscore the potential of BAG as a valuable biomarker for assessing cognitive decline and symptom severity of neuropsychiatric disorders.

Brain volumes are related with motor skills at late childhood in children born extremely preterm

BACKGROUND

This study had three aims. First, we wanted to explore if there was difference in motor performance at 12 years of age in children born extremely preterm (EPT < 28 weeks of gestation) and at term. Our second aim was to study whether the volumes of motor networks and regions differed between those groups when they underwent brain scans at 10 years of age. Third, we investigated whether there were differences in the motor networks and regions of the brain in children born EPT who did or did not have motor impairment at 12 years of age.

METHODS

In a Swedish national study, a subgroup of 42 children born before 27 weeks and 25 term-born controls underwent MRI at age 10. A neuroradiologist performed MRI acquisitions, and analyses focused on brain regions associated with motor function. At age 12, motor function was assessed using the Movement Assessment Battery for Children - Second Edition (MABC-2), conducted by a licensed physiotherapist. Examiners were blinded to group status. Motor function and motor-related brain volumes were compared between the EPT and control group, and between children born EPT with and without motor impairments.

RESULTS

Findings revealed significantly reduced motor performance and smaller motor region volumes in EPT children compared to controls (p < 0.001). Among EPT children, those with motor impairment especially in aiming and catching, had notably smaller brain volume in the basal ganglia (mean difference:1.2 cm3, p = 0.049), cerebellum (mean difference:14.4 cm3, p < 0.001), motor execution (mean difference:3.7 cm3, p = 0.049) network and motor imagery network (mean difference 5.6 cm3, p = 0.049) than their EPT peers without such impairments. Cerebellar volume remained significant different between the groups when adjusting for birth weight and sex in a linear regression model, p = 0.02 (η2 = 0.17).

CONCLUSION

The results underscore the impact of extreme prematurity on motor function and brain structure, highlighting a specific link between reduced motor area volumes and impaired ball skills.

Computational modeling of ketamine-induced changes in gamma-band oscillations: The contribution of parvalbumin and somatostatin interneurons

Ketamine, an NMDA receptor (NMDA-R) antagonist, produces psychotomimetic effects when administered in sub-anesthetic dosages. While previous research suggests that Ketamine alters the excitation/inhibition (E/I)-balance in cortical microcircuits, the precise neural mechanisms by which Ketamine produces these effects are not well understood. We analyzed resting-state MEG data from n = 12 participants who were administered Ketamine to assess changes in gamma-band (30-90 Hz) power and the slope of the aperiodic power spectrum compared to placebo. In addition, correlations of these effects with gene-expression of GABAergic interneurons and NMDA-Rs subunits were analyzed. Finally, we compared Ketamine-induced spectral changes to the effects of systematically changing NMDA-R levels on pyramidal cells, and parvalbumin-, somatostatin- and vasoactive intestinal peptide-expressing interneurons in a computational model of cortical layer-2/3 to identify crucial sites of Ketamine action. Ketamine resulted in a flatter aperiodic slope and increased gamma-band power across brain regions, with pronounced effects in prefrontal and central areas. These effects were correlated with the spatial distribution of parvalbumin and GluN2D gene expression. Computational modeling revealed that reduced NMDA-R activity in parvalbumin or somatostatin interneurons could reproduce increased gamma-band power by increasing pyramidal neuron firing rate, but did not account for changes in the aperiodic slope. The results suggest that parvalbumin and somatostatin interneurons may underlie increased gamma-band power following Ketamine administration in healthy volunteers, while changes in the aperiodic component could not be recreated. These findings have implications for current models of E/I-balance, as well as for understanding the mechanisms underlying the circuit effects of Ketamine.

Brain-clinical biotyping in patients with idiopathic REM sleep behavior disorder

Idiopathic REM sleep behavior disorder (iRBD) is a prodromal stage of α-synucleinopathies including Parkinson's disease (PD), yet its clinical heterogeneity remains underexplored. This study aimed to identify novel brain-clinical biotypes in iRBD by integrating structural MRI and clinical assessments. We included 172 patients with video-polysomnography-confirmed iRBD and 126 controls who underwent multimodal MRI and clinical evaluation. Similarity Network Fusion was used to integrate cortical thickness, surface area, subcortical volume, and clinical data, followed by spectral clustering to identify iRBD biotypes. Two distinct biotypes were identified: Biotype 1 showed widespread cortical-subcortical-cerebellar atrophy, functional hypoconnectivity, more motor and cognitive deficits with higher prodromal PD risk; Biotype 2 demonstrated increased surface area in limbic and parietal regions, cortical-cerebellar hyperconnectivity, and preserved neurocognitive function. These findings underscore the presence of distinct neurobiological subtypes in iRBD, highlighting the need for longitudinal monitoring to clarify their trajectories and implications for disease progression.

Combining radiomics and connectomics in MRI studies of the human brain: A systematic literature review

Advances in MRI techniques continue to open new avenues to investigate the structure and function of the human brain. Radiomics, involving the extraction of quantitative image features, and connectomics, involving the estimation of structural and functional neural connections, from large amounts and different types of MRI data sets, represent two key research areas for advancing neuroimaging while exploiting progress in computational and theoretical modelling applied to MRI. This systematic literature review aimed at exploring the combination of radiomics and connectomics in human brain MRI studies, highlighting how the combination of these approaches can provide novel or additional insights into the human brain under normal and pathological conditions. The review was conducted according to the Preferred Reported Item for Systematic Reviews and Meta-Analyses (PRISMA) statement, seeking documents from Scopus and PubMed archives. Eleven studies (out of the initial 675 records) have met the established criteria and reported combined approaches from radiomics and connectomics. Three subgroups of approaches were identified, based on the MRI modalities used to obtain radiomic and connectomic features. The first group of 3 studies combined radiomics and connectomics applied to structural MRI (sMRI) data sets; the second group of 5 studies combined radiomics applied to sMRI data and connectomics applied to diffusion (dMRI) and/or functional MRI (fMRI) data sets; the third group of 3 studies combined radiomics and connectomics applied to fMRI. This review highlighted the recent growing interest in combining MRI-based radiomics and connectomics to explore the human brain for neurological, psychiatric, and oncological conditions. Current methodologies and challenges were discussed, pointing out future research directions to improve or standardize these approaches and the gaps to be filled to advance the field.

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research

The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions.

Approach bias modification reduces automatic gaming tendencies and enhances brain synchronization in internet gaming disorder

BACKGROUND

Automatic approaches to gaming-related cues are key factors in internet gaming disorder (IGD). Approach bias modification (ApBM) has been shown to reduce addictive behaviors, but its neurobiological effects remain poorly understood. This study examined changes in brain activities in the 'natural' state in IGD patients after ApBM.

METHODS

Fifty-five (of 61) IGD patients were randomly assigned to the approach-avoidance task (AAT, n = 30) and sham-AAT (n = 25) groups. Participants completed the pre-test, five real/sham ApBM sessions, and the post-test. In the pre-and post-tests, fMRI data were collected while viewing gaming and neutral videos. Inter-subject correlation (ISC) and functional connectivity (FC) analyses were conducted to explore the ApBM-related changes.

RESULTS

ANOVA of behavioral data revealed that ApBM significantly decreased the approach bias and addiction scores. The ISC analyses revealed increased synchronization in the paracentral lobule, precuneus, and insula regions in the ATT group after ApBM. Additionally, decreased FC was observed between the insula and superior frontal gyrus, precuneus, and orbitofrontal cortex in the AAT group.

CONCLUSIONS

Preliminary findings suggest that ApBM may be effective in reducing automatic approach tendencies toward gaming cues, highlighting its potential as an intervention strategy. However, it is important to note that the neurobiological evidence in this study only provides a possible association, and the results should be interpreted with caution. Future research is needed to further examine the clinical efficacy of ApBM in IGD, whether as a stand-alone treatment or as an adjunct to formal therapy.

Behaviorally inhibited preschoolers experience stronger connectivity among social-related neural regions while interacting with a stranger

Social behavioral inhibition (BI), or wariness in response to unfamiliar social stimuli, is a temperament trait that, when present in preschool-age children, predicts neural alterations and anxiety disorders by adolescence. The current study assessed neural functioning associated with BI during the preschool years. Our sample was enriched for BI based on mother report and included 59 preschool-age children (54 % female, Mage = 3.7 years). Children interacted with an unfamiliar experimenter via the Stranger Approach paradigm from the preschool version of Lab-TAB, and neural data were collected simultaneously to measure neural response to an unfamiliar social encounter. Children who exhibited more social BI-related behaviors experienced stronger functional connectivity between multiple social-related neural regions, including the temporoparietal junction, superior temporal gyrus, and medial and lateral prefrontal cortex while interacting with a stranger. Additionally, children who experienced stronger connectivity between the right and left temporoparietal junction had greater mother-reported anxiety symptoms one year later. Our results suggest that observable social BI during early childhood is associated with distinct neural patterns, which may elucidate biomarkers that underlie risk for later anxiety.

Cerebello-Prefrontal Connectivity Underlying Cognitive Dysfunction in Spinocerebellar Ataxia Type 2

OBJECTIVE

Spinocerebellar ataxia type 2 (SCA2) is a hereditary cerebellar degenerative disorder, with motor and cognitive symptoms. The constellation of cognitive symptoms due to cerebellar degeneration is named cerebellar cognitive affective syndrome (CCAS), which has increasingly been recognized to profoundly impact patients' quality of life; however, the brain circuits underlying these cognitive dysfunctions remain elusive.

METHODS

We utilized a novel technique, cerebello-cortical electroencephalogram (EEG), to investigate the resting-state functional connectivity in different frequency domains in 12 SCA2 patients and 24 age-matched controls. Given that the prefrontal cortex is strongly connected to the cerebellum, we studied the EEG connectivity between the cerebellum and the prefrontal cortex. We also conducted correlation analyses to explore the association between this connectivity and the severity of cognitive dysfunction, determined by CCAS scores.

RESULTS

Source-space spectral analysis differences between SCA2 patients and controls were observed in the cerebellum at the delta, theta, and beta frequencies. Functional connectivity between the posterior cerebellum and the prefrontal cortex revealed decreased theta and increased beta connectivity in SCA2 patients, with no differences in delta connectivity. Increased beta connectivity was unique to the prefrontal regions, not seen in the connectivity to the primary motor cortex or mid-temporal lobe. Interestingly, this beta connectivity correlated with CCAS scores in SCA2 patients.

CONCLUSION

Our findings demonstrated that SCA2 patients have an increase in beta cerebello-prefrontal connectivity that correlates with cognitive performance. These findings suggest cerebello-cortical EEG could track circuit dysfunction underlying cognitive symptoms in SCA2, paving the way for developing targeted neuromodulation therapeutics.

Childhood family environment and μ-opioid receptor availability in vivo in adulthood

Animal studies have reported associations of early maternal separation with altered μ-opioid receptor function but data on humans are scarce. We now investigated whether childhood family environment is related to μ-opioid receptor availability in the human brain in adulthood. Healthy participants (n = 37-39 in the analyses) were recruited from the prospective population-based Young Finns Study (YFS) that started in 1980. Childhood family environment was evaluated in 1980, including scores for stress-prone life events, disadvantageous emotional family atmosphere, and adverse socioeconomic environment. We used positron emission tomography (PET) with radioligand [11C]carfentanil to measure μ-opioid receptor availability in adulthood. Age- and sex-adjusted analyses showed that exposure to stress-prone life events in childhood was related to lower μ-opioid receptor binding in the orbitofrontal cortex, hippocampus, putamen, amygdala, insula, thalamus, anterior cingulate cortex, and dorsal caudate in adulthood (when compared to participants not exposed to stress-prone life events). Unfavorable socioeconomic family environment or disadvantageous emotional family atmosphere was not associated with μ-opioid receptor availability in adulthood. In conclusion, exposure to environmental instability (i.e., to stress-prone life events below traumatic threshold) during early development is associated with dysregulation of the u-opioid receptor transmission in adulthood. The findings increase understanding of the neurobiological mechanisms involved in the associations between childhood adversities and adulthood mental disorders.

Influence of binge drinking on the resting state functional connectivity of university Students: A follow-up study

Binge Drinking (BD) is characterized by consuming large amounts of alcohol on one occasion, posing risks to brain function. Nonetheless, it remains the most prevalent consumption pattern among students. Cross-sectional studies have explored the relationship between BD and anomalies in resting-state functional connectivity (RS-FC), but the medium/long-term consequences of BD on RS-FC during developmental periods remain relatively unexplored. In this two-year follow-up study, the impact of sustained BD on RS-FC was investigated in 44 college students (16 binge-drinkers) via two fMRI sessions at ages 18-19 and 20-21. Using a seed-to-voxel approach, RS-FC differences were examined in nodes of the main brain functional networks vulnerable to alcohol misuse, according to previous studies. Group differences in RS-FC were observed in four of the explored brain regions. Binge drinkers, compared to the control group, exhibited, at the second assessment, decreased connectivity between the right SFG (executive control network) and right precentral gyrus, the ACC (salience network) and right postcentral gyrus, and the left amygdala (emotional network) and medial frontal gyrus/dorsal ACC. Conversely, binge drinkers showed increased connectivity between the right Nacc (reward network) and four clusters comprising bilateral middle frontal gyrus (MFG), right middle cingulate cortex, and right MFG extending to SFG. Maintaining a BD pattern during critical neurodevelopmental years impacts RS-FC, indicating mid-to-long-term alterations in functional brain organization. This study provides new insights into the neurotoxic effects of adolescent alcohol misuse, emphasizing the need for longitudinal studies addressing the lasting consequences on brain functional connectivity.

Resting-state functional MRI metrics to detect freezing of gait in Parkinson's disease: a machine learning approach

Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patients, and it remains not fully understood. Accurate classification of FOG in PD is crucial for tailoring effective interventions and is necessary for a better understanding of its underlying mechanisms. In the present work, we applied four Machine Learning (ML) classifiers (Decision Tree - DT, Random Forest - RF, Multilayer Perceptron - MLP, Logistic Regression - LOG) to different four metrics derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data processing to assess their accuracy in automatically classifying PD patients based on the presence or absence of Freezing of Gait (FOG). To validate our approach, we applied the same methodologies to distinguish PD patients from a group of Healthy Subject (HS). The performance of the four ML algorithms was validated by repeated k-fold cross-validation on randomly selected independent training and validation subsets. The results showed that when discriminating PD from HS, the best performance was achieved using RF applied to fractional Amplitude of Low-Frequency Fluctuations (fALFF) data (AUC 96.8 ± 2 %). Similarly, when discriminating PD-FOG from PD-nFOG, the RF algorithm was again the best performer on all four metrics, with AUCs above 90 %. Finally, trying to unbox how AI system black-box choices were made, we extracted features' importance scores for the best-performing method(s) and discussed them based on the results obtained to date in rs-fMRI studies on FOG in PD and, more generally, in PD. In summary, regions that were more frequently selected when differentiating both PD from HS and PD-FOG from PD-nFOG patients were mainly relevant to the extrapyramidal system, as well as visual and default mode networks. In addition, the salience network and the supplementary motor area played an additional major role in differentiating PD-FOG from PD-nFOG patients.

Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's disease

INTRODUCTION

The interaction of amyloid and tau in neurodegenerative diseases is a central feature of AD pathophysiology. While experimental studies point to various interaction mechanisms, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. The aim of this study was to compare mathematical reaction-diffusion models encoding distinct cross-species couplings to identify which interactions were key to model success.

METHODS

We tested competing mathematical models of network spread, aggregation, and amyloid-tau interactions on publicly available data from ADNI.

RESULTS

Although network spread models captured the spatiotemporal evolution of tau and amyloid in human subjects, the model including a one-way amyloid-to-tau aggregation interaction performed best.

DISCUSSION

This mathematical exposition of the "pas de deux" of co-evolving proteins provides quantitative, whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.

Association of node assortativity and internalizing symptoms with ketogenic diet effectiveness in pediatric patients with drug-resistant epilepsy

BACKGROUND

The ketogenic diet (KD) is an effective alternative therapy for drug-resistant epilepsy (DRE). However, there are no established predictors for KD effectiveness. We aimed to investigate the impact of 12 months of KD therapy (KDT) on brain connectivity, as measured by functional magnetic resonance imaging (fMRI), and its correlation with seizure control, behavioral/mood alterations, and parental stress.

METHODS

Children with DRE were enrolled in this single-center, prospective cohort study from February 2020 to October 2021. They were divided into a control group and a KDT group. The Child Behavior Checklist (CBCL) and Parental Stress Index (PSI) were administered to parents at the initiation of KDT (T0) and at 12 months (T1). Resting-state fMRI was performed at T0 and at 6 months of KDT. The primary outcome was the between-group difference in the change of CBCL/PSI scores, and brain connectivity metrics after KDT, and the secondary outcome involved measuring their correlation with seizure reduction rates.

RESULTS

Twenty-two patients with DRE were enrolled. We had 13 patients in the control group and 9 in the KDT group. Our data revealed that 12 months of KDT can reduce monthly seizure frequency. Several subscales of CBCL T-scores were higher at T0 compared with the control group, then becoming comparable at T1. The PSI scores from 'mothers' reports reduced after receiving KDT. The changes in node assortativity (ΔAssortativity) were positively correlated with behavioral problems and negatively with seizure reduction rates in the KD group.

CONCLUSIONS

Twelve months of KDT can reduce monthly seizure frequency and improve mood/behavioral disturbances in patients with DRE. Furthermore, KDT could relieve primary caregivers' stress. A lower ΔAssortativity value was associated with better behavioral outcomes and greater seizure reduction. The ΔAssortativity value in fMRI may be a crucial predictor for the effectiveness of KDT.

Differentiating Treatment-Resistant Depression With and Without Parkinsonism in the Elderly From a Psychiatric Perspective by 99mTc-TRODAT-1 SPECT Imaging

OBJECTIVES

Late-life depression often overlaps with neurodegenerative diseases leading to diagnostic and treatment challenges for neuropsychiatrists. This study aimed to differentiate elderly treatment-resistant depression (TRD) comorbid with parkinsonism from elderly TRD without Parkinsonism as well as elderly healthy controls using striatum dopamine transporter (DAT) imaging by 99mTc TRODAT-1 SPECT.

METHODS

Three groups were enrolled, including patients with TRD, patients with TRD comorbid with parkinsonism, and healthy controls. To obtain the DAT availability, the specific uptake ratios of the bilateral striatum were evaluated. Linear regression analyses were performed to evaluate the relationship between age and DAT level in the subregions of the striatum. Machine learning was applied to categorize the three groups with 10-fold cross-validation.

RESULTS

The study enrolled 32 patients with TRD (66.15 ± 6.82 $66.15\pm 6.82$ ), 36 TRD patients with parkinsonism (70.27 ± 5.63 $70.27\pm 5.63$ ), and 74 healthy elderly (66.95 ± 10.59 $66.95\pm 10.59$ ). A normative DAT concentration by age was established, providing a reference for clinical use. DAT levels differed among groups (all pairwise p < 0.01), with healthy controls exhibiting the highest levels, followed by patients with TRD, and then TRD patients with parkinsonism. Further, the Fine k-NN classifier emerged as the top performer to achieve 85.7% accuracy.

CONCLUSIONS

Besides clinical assessment, dopaminergic assessment may help differentiate parkinsonism from TRD in old age. The findings of lower DAT availability in TRD suggest that TRD may be a prodromal symptom of Parkinson's disease. Psychiatrists should consider comorbid neurodegenerative disorders in elderly, depressed patients and use clinical assessment, neurological examination, and brain imaging for early Parkinson's Disease screening.

DLG2 rs11607886 polymorphism associated with schizophrenia and precuneus functional changes

BACKGROUND AND HYPOTHESIS

Schizophrenia (SZ) is a severe mental disorder with high heritability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93, Postsynaptic Density Protein 93), and its variants were associated with an increased risk of SZ. However, the role of DLG2 locus variation in SZ remains elusive. This study aims to investigate the association between DLG2 gene polymorphisms and SZ susceptibility and the relationship between DLG2 and altered brain function and clinical symptoms in SZ patients.

STUDY DESIGN

Single nucleotide polymorphisms (SNPs) rs11607886 and rs7479949 were genotyped in 350 SZ patients and 407 healthy controls (HCs). 47 SZ patients and 79 HCs were genotyped into two groups: the risk A allele carrier group and the GG-pure group. Functional magnetic resonance imaging (fMRI) indices were further analyzed. Subsequently, data from different brain regions were correlated with clinical symptom assessment.

STUDY RESULTS

DLG2 rs11607886 was significantly associated with SZ. Significant main effects were found in the ALFF and ReHo, especially for the left precuneus gyrus (PCu). A significant interaction between genotype and diagnosis had a significant effect on FC, which was increased between the left PCu and the right middle temporal gyrus in carriers of the A allele with SZ (r = -0.336, Pun-corrected = 0.042) and negatively correlated with spatial breadth scores (r = 0.444, PFDR-corrected = 0.002).

CONCLUSIONS

The rs11607886 polymorphism in DLG2 may influence the pathogenesis of SZ and have potential effects on cognitive function. The present study emphasizes DLG2 as a candidate gene for SZ and suggests an important role for PCu in SZ.

The neural basis of sharing information through goal-directed conversation: A hyperscanning functional magnetic resonance imaging study

The human brain maintains internal models of physical and social environments, representing an individual's "subjectivity". Through conversation, two or more individuals share their models and modify them based on the exchange, a process that represents and is referred to as "intersubjectivity." To investigate the neural substrates of this dynamic process, hyperscanning functional magnetic resonance imaging was conducted to test the hypothesis that Inter-Brain Synchronization (IBS) in the default mode network (DMN) is involved in representing intersubjectivity. Twenty-four Japanese-speaking participant pairs played maze games over a two-day period. Each participant pair received a different maze, i.e., a maze with a different pathway to its goal. Although pairs shared a maze, each participant in a pair had only partial knowledge of the maze layout and what they knew about the layout differed. Taking turns, participants moved their pieces to their goals. Since each had only partial information about the pathway, effective communication between partners was important. Behavioral data showed participants' conversation about potential maze piece moves significantly increased as the game proceeded, implying that the exchange for such information was critical. Correspondingly, the DMN increased task-related activation, including the dorsomedial prefrontal cortex (dmPFC) and the bilateral temporoparietal junction (TPJ), extending through the superior temporal sulcus to the temporal pole and the right middle frontal gyrus. Within these areas, the dmPFC and the right TPJ showed task- and partner-specific IBS throughout all games. Thus, the DMN is likely required for representing intersubjectivity, based on internal models shared through real-time conversations.

Neural Electrical Correlates of Subjective Happiness

Happiness is a subjective experience that can serve as the ultimate goal for humans. A recent study that employed resting-state functional magnetic resonance imaging (fMRI) reported that spontaneous fluctuation (fractional amplitude of low-frequency fluctuation: fALFF) in the precuneus is negatively associated with subjective happiness. However, little is known about the neural electrical correlates of subjective happiness, which can provide direct evidence of neural activity and insights regarding the underlying psychological, cellular, and neurotransmitter mechanisms. Therefore, we measured 400-channel whole-head magnetoencephalography (MEG) during resting state in participants whose subjective happiness was evaluated using questionnaires. We conducted source reconstruction analysis utilizing bandpass-filtered MEG data and analyzed the fALFF of the band-limited power time series as an index of spontaneous neural fluctuation. Gamma-band fALFF values in the right precuneus were negatively associated with subjective happiness scores (partial correlation coefficient = -0.56). These findings indicate that subjective happiness has a neural electrical correlate of reduced spontaneous fluctuation of gamma-band neuronal oscillations in the right precuneus, and that it could be mediated by a reduction in wandering, clinging self-consciousness through heightened N-methyl-d-aspartate-dependent gamma-aminobutyric acid-ergic parvalbumin inhibitory interneuron activity.

Trial-By-Trial Variation In Upper Extremity Movement Smoothness After Acute Stroke Relates To Clinical Assessments And Corticospinal Tract Injury

BackgroundVariability in movement is critical for performance under dynamic conditions. Stroke causes focal injury to the motor system, disrupts voluntary motor control, and leads to less smooth and more variable upper extremity movements. Few studies have characterized trial-by-trial variation in upper extremity movement smoothness and its clinical and neuroanatomic correlates in the first week post-stroke.ObjectiveTo evaluate trial-by-trial variation in upper extremity movement smoothness during planar reaching and relate it to clinical outcomes and neuroanatomical injury after acute stroke.MethodsTwenty-two patients (4.4 ± 1.7 days post-stroke) and 22 able-bodied adults completed a planar center-out reaching task. Smoothness was quantified with spectral arc length (SPARC). Median and interquartile range (IQR, a quantification of trial-by-trial variation) of SPARC values were assessed. Patients completed a clinical assessment battery acutely and at 90 days post-stroke. MRI-derived stroke lesions were analyzed to estimate basal ganglia, motor cortex, and corticospinal tract injury. Intraclass correlation, Spearman's correlation, and multivariate regression evaluated trial-by-trial variation and its relation to clinical assessments, outcomes, and neuroanatomical injury.ResultsPost-stroke reaching was less smooth and more variable (larger IQR) compared to able-bodied adults. Variability in post-stroke smoothness was primarily driven by within-subject, trial-by-trial variation. More variable smoothness, even after controlling for median smoothness, related to worse performance on clinical assessments and 90-day outcomes. More variable smoothness related to greater corticospinal tract injury (ρ = 0.537, P = .011), but not to basal ganglia or motor cortex injury.ConclusionTrial-by-trial variation of movement is valuable for understanding sensorimotor control post-stroke and has implications for targeted neurorehabilitation.

Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-symptom mapping (LSM) studies that address this connection have relied on mass univariate statistics, which do not account for multidimensional relationships between variables. Machine learning (ML) techniques, which can capture these intricate connections, offer a promising complement to LSM methods. To test this promise, we benchmarked ML models on structural and functional MRI to predict aphasia severity (N = 238) and naming impairment (N = 191) for a cohort of chronic-stage stroke survivors. We used nested cross-validation to examine performance along three dimensions: (1) parcellation schemes (JHU, AAL, BRO, and AICHA atlases), (2) neuroimaging modalities (resting-state functional connectivity, structural connectivity, mean diffusivity, fractional anisotropy, and lesion location) and (3) ML methods (Random Forest, Support Vector Regression, Decision Tree, K Nearest Neighbors, and Gradient Boosting). The best results were obtained by combining the JHU atlas, lesion location, and the Random Forest model. This combination yielded moderate to high correlations with the two different behavioral scores. Key regions identified included several perisylvian areas and pathways within the language network. This work complements existing LSM methods with new tools for improving the prediction of language outcomes in stroke survivors.

Multimodal MEG and Microstructure-MRI Investigations of the Human Hippocampal Scene Network

Although several studies have demonstrated that perceptual discrimination of complex scenes relies on an extended hippocampal posteromedial system, we currently have limited insight into the specific functional and structural properties of this system in humans. Here, combining electrophysiological (magnetoencephalography) and advanced microstructural (multishell diffusion magnetic resonance imaging; quantitative magnetization transfer) imaging in healthy human adults (30 females/10 males), we show that both theta power modulation of the hippocampus and fiber restriction/hindrance (reflecting axon packing/myelination) of the fornix (a major input/output pathway of the hippocampus) were independently related to scene, but not face, perceptual discrimination accuracy. Conversely, microstructural features of the inferior longitudinal fasciculus (a long-range occipitoanterotemporal tract) correlated with face, but not scene, perceptual discrimination accuracy. Our results provide new mechanistic insight into the neurocognitive systems underpinning complex scene discrimination, providing novel support for the idea of multiple processing streams within the human medial temporal lobe.

Transition and dynamic reconfiguration in late-life depression based on hidden Markov model

Late-life depression is characterized by persistent emotional distress and cognitive dysfunction, yet understanding the specific brain dynamics and molecular mechanisms involved remains limited. Here, we employed a hidden Markov model to analyze resting-state functional magnetic resonance imaging data from 154 patients with late-life depression and 147 healthy controls. This analysis revealed 12 recurring brain states with distinct spatiotemporal patterns and identified atypical dynamic features across several networks. Notably, patients exhibited significantly higher transition probabilities for entering, exiting, and maintaining in the positive activation state of the default mode network, with genes linked to this state mainly enriched in regulation of neuronal synaptic plasticity and cognitive processes. Hierarchical clustering further found a critical entry and exit point between two high-level meta-states with opposing activation patterns, highlighting large-scale network dysfunction and potential molecular mechanisms associated with late-life depression through the decoding of brain states.

Type-1 Diabetes Impacts Brain Microstructure and Anatomical Associations in Young and Well-Controlled Individuals

Type 1 Diabetes Mellitus (T1DM) progression has a direct impact on brain microstructural integrity and typical functional organization from the early stages of neurodevelopment. Diffusion Tensor Imaging (DTI) is a neuroimaging method that has proven sensitive to changes in white matter microstructure. Using diffusion-weighted probabilistic tractography methods, we aim to evaluate the white matter integrity and anatomical relationships within the Default Mode Network (DMN) brain regions, which have been proven to be particularly affected by T1DM in a group of eighteen carefully selected clinically well-controlled young T1DM patients versus eighteen healthy matched controls according to sex, age, and education level. Results showed no relevant differences in the anatomical distribution of DMN between the groups. However, the transitivity graph metric was significantly lower in T1DM patients, who also showed weaker connectivity between the left ventral prefrontal cortex and the left medial temporal gyrus, representing the anatomical trajectory of the arcuate fasciculus. Considering that neural myelination is affected by language input and the critical role of language-related structures on brain development, the current findings denote early ill-driven brain modifications to better adapt to the increasing daily demands.

Data-driven, cross-sectional image-based subtyping and staging of brain volumetric changes in Parkinson's disease

BACKGROUND

Several subtyping methods have been proposed to characterize Parkinson's disease (PD) progression, yet the trajectory of subcortical and cortical neurodegeneration and its clinical implications remain unclear.

OBJECTIVES

We aimed to conduct a strictly image-based, data-driven classification of PD progression through Subtype and Stage Inference (SuStaIn) algorithm.

METHODS

Brain volumetric data from 565 patients with PD and 150 propensity-matched healthy controls were analyzed. 16 regions of interest, including 9 cortical and 7 deep grey matter structures, were segmented from T1-weighted magnetic resonance images. Clinical data, including REM sleep behavior disorder (RBD), levodopa equivalent daily dose (LEDD), and motor complications were collected. SuStaIn was trained and tested using a 10-folds cross-validation and identified two distinct PD progression subtypes, which were compared for differences in clinical and radiological characteristics.

RESULTS

We found two distinct neurodegenerative trajectories: deep grey matter (DG)-first and cortex (CO)-first. The CO-first subtype had a higher prevalence of RBD (p = 0.009) and levodopa-induced dyskinesia (p = 0.024) than the DG-first subtype. Disease progression was faster in the CO-first subtype (0.203 year/stage, LEDD increase 59.3 mg/year), than in the DG-first subtype (0.081 year/stage, LEDD increase 45.1 mg/year, respectively). Regardless of the subtypes, the sensorimotor and auditory cortices were the earliest affected cortical regions, while the amygdala was the first affected subcortically. A subset of participants (n = 186) showed no significant atrophy progression.

CONCLUSIONS

Our findings support the existence of two distinct subtypes of PD progression based on neuroimaging data. Longitudinal studies are warranted to track their evolution.

Estimating brain effective connectivity from time series using recurrent neural networks

Effective Connectivity (EC) reflects the causal influence between brain regions. Identifying Effective Connectivity Networks (ECN) in the brain can enhance our understanding of brain functions and reveal the impact of mental illnesses on these functions. However, existing EC estimation methods face challenges in extracting deep features from functional magnetic resonance imaging (fMRI) data. In this study, we propose a novel Time Series to Effective Connectivity (TS2EC) prediction model based on recurrent neural networks, which directly extracts deep features from fMRI time series without relying on a fixed model order. Specifically, we introduce a method for generating EC labels from electrocortical stimulation fMRI (es-fMRI) data, representing the first attempt to use es-fMRI for EC estimation. We evaluated TS2EC on three datasets: an es-fMRI dataset with 23 subjects (augmented to 7,082 samples), a multivariate autoregressive simulated dataset, and a Smith simulated dataset. On the es-fMRI dataset, TS2EC achieved a mean squared error of 0.0057, significantly outperforming existing methods. Experiments on the simulated datasets demonstrated that TS2EC attained superior performance in accuracy, recall, structural Hamming distance, and F1-score. Experimental results demonstrate that the EC prediction performance of TS2EC is significantly higher than current EC analysis methods. TS2EC holds promise as a novel tool for the analysis of ECN in the brain.

Neocortical and Hippocampal Theta Oscillations Track Audiovisual Integration and Replay of Speech Memories

"Are you talkin' to me?!" If you ever watched the masterpiece "Taxi Driver" directed by Martin Scorsese, you certainly recall the monologue during which Travis Bickle rehearses an imaginary confrontation in front of a mirror. While remembering this scene, you recollect a myriad of speech features across visual and auditory senses with a smooth sensation of unified memory. The aim of this study was to investigate how the fine-grained synchrony between coinciding visual and auditory features impacts brain oscillations when forming multisensory speech memories. We developed a memory task presenting participants with short synchronous or asynchronous movie clips focused on the face of speakers in real interviews, all the while undergoing magnetoencephalography recording. In the synchronous condition, the natural alignment between visual and auditory onsets was kept intact. In the asynchronous condition, auditory onsets were delayed to present lip movements and speech sounds in antiphase specifically with respect to the theta oscillation synchronizing them in the original movie. Our results first showed that theta oscillations in the neocortex and hippocampus were modulated by the level of synchrony between lip movements and syllables during audiovisual speech perception. Second, theta asynchrony between the lip movements and auditory envelope during audiovisual speech perception reduced the accuracy of subsequent theta oscillation reinstatement during memory recollection. We conclude that neural theta oscillations play a pivotal role in both audiovisual integration and memory replay of speech.

Anatomical heterogeneity in low-grade and high-grade gliomas: A multiscale perspective

BACKGROUND

Low-grade gliomas (LGGs) and high-grade gliomas (HGGs) often exhibit distinct spatial distributions, a phenomenon that remains incompletely understood. Based on previous research, we hypothesized that functional networks, neurotransmitters, and isocitrate dehydrogenase-1 (IDH-1) status characterize the spatial patterns of LGG and HGG.

METHODS

We analyzed 399 patients diagnosed with primary gliomas. First, we generated glioma frequency maps based on tumor grade, neurotransmitters, and IDH-1 status and constructed a brain functional connectivity network to explore heterogeneity in glioma location. Second, all tumor masks were mirror-symmetrized onto the brain's left hemisphere to facilitate feature extraction. We performed independent component analysis on merged four-dimensional files using Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC), identifying four IDH-1 wild-type lesion covariance networks (IDHwt-LCNs) and three IDH-1 mutant lesion covariance networks (IDHmut-LCNs) with distinct spatial distributions, and analyzing correlation between the neurotransmitter levels and the IDH-wt/mut specific LCNs. Finally, we compared 42 white matter fibers extracted using XTRACT with 39 functional brain connectivity networks from the multi-subject dictionary learning (MSDL) atlas, revealing significant associations among the frontal aslant tract (FAT) and the intraparietal sulcus (IPS).

RESULTS

Our findings revealed high anatomical heterogeneity between LGG and HGG. Moreover, the high node strength played a critical role in the distinct spatial distribution of glioma. Significant correlations were observed between glioma frequency maps and dopaminergic, cholinergic, μ-opioid, and serotonergic neurotransmission. Furthermore, IDHwt/mut-LCNs analysis demonstrated that IDH-1 status influences glioma distribution, involving key brain structures. Lastly, we also found significant correlations between IDHwt/mut-LCNs and the neurotransmission of dopaminergic, cholinergic, μ-opioid, and serotonergic systems.

CONCLUSION

Our study highlighted the mechanisms by which functional networks, neurotransmitter systems, and IDH-1 status collectively contribute to the anatomical heterogeneity observed in LGG and HGG.

The role of anxiety in modulating temporal processing and sensory hyperresponsiveness in autism spectrum disorder: an fMRI study

The atypical sensory features and high comorbidity of anxiety disorders in individuals with autism spectrum disorder (ASD) are attracting increasing attention. Among individuals with ASD, those who exhibit heightened sensory hyperresponsiveness tend to show enhanced temporal processing of sensory stimuli, despite no observed differences in stimulus detection thresholds. A previous study reported the role of anxiety in modulating emotion-cued changes of visual temporal resolution in ASD. Building on this, we hypothesized that elevated anxiety might contribute to increased activation of neural circuits for timing perception and sensory hyperresponsiveness. This study included 25 individuals with ASD and 25 typically developed (TD) participants. Using functional magnetic resonance imaging (fMRI), we examined neural activity during a visual temporal order judgment task pre-cued by facial emotions. In the TD group, but not the ASD group, the presence of fearful facial expressions enhanced temporal processing. However, a correlation of anxiety levels with emotion-cued task performance and sensory hyperresponsiveness, respectively, was evident in the ASD group. In the TD group, neuroimaging revealed greater activation of the right caudate compared with that in the ASD group and a functional connectivity between the amygdala and left supramarginal gyrus. Individuals with ASD showed a relationship between anxiety level and activation of the right angular gyrus. Moreover, anxiety mediated the link between right angular gyrus activation and sensory hyperresponsiveness in the ASD group. These findings suggest that enhancement of temporal processing by fear-related cues-reflecting an emotion-timing neural circuit-may be disrupted in individuals with ASD. Heightened anxiety and sensory hyperresponsiveness in ASD may be mediated by brain regions involved in timing perception.

Relationship Between Cognitive Estimation, Executive Functions, and Theory of Mind in Patients With Prefrontal Cortex Damage

OBJECTIVE

Conflicting evidence has arisen from the few studies that have examined the role of the prefrontal cortex and executive control functions in theory of mind (ToM). Moreover, the involvement of other cognitive domains in the ability to infer mental states is still under debate. This study aims to examine, in addition to the potential contribution of executive functions, the role of cognitive estimation in ToM abilities, given that cognitive estimation processes are strongly associated with some aspects of executive control functions.

METHOD

The cognitive estimation task, along with a set of neuropsychological tasks assessing executive functions, was administered to 30 patients with prefrontal cortex damage and 30 control subjects matched by gender, age, and education level.

RESULTS

Patients with prefrontal cortex damage were impaired in all measures of executive functions, cognitive estimation, and theory of mind compared with control subjects. Regression analysis showed a significant interaction between executive measures and cognitive estimation in predicting ToM performance for patients with prefrontal cortex damage. Additionally, voxel-based lesion analysis identified a partially common bilaterally distributed prefrontal network involved in all three domains, centered within the ventral and dorsomedial areas with extension to the dorsolateral prefrontal cortex.

CONCLUSION

Our findings highlight that, apart from executive functions, cognitive estimation plays a crucial role in the ability to interpret others' cognitive and emotional states in both patients with prefrontal cortex damage and control subjects.

Topological abnormalities of left middle orbital frontal gyrus and amygdala associated with hypoactive sexual desire disorder: A diffusion tensor imaging study

INTRODUCTION

Sexual desire has been found to be associated with brain areas involved in sexual excitation and inhibition. However, little is known regarding whether hypoactive sexual desire disorder patients have structural abnormalities related to hypofunctional excitation or hyperfunctional inhibition in the brain.

METHODS

Magnetic resonance imaging data were collected from 26 hypoactive sexual desire disorder patients and 28 healthy controls. The structural brain networks were constructed based on diffusion tensor imaging data. Finally, the nodal parameters were calculated by the graph theoretical analysis and were compared between hypoactive sexual desire disorder and healthy controls.

RESULTS

There were no significant differences in the age, education level, and scores of emotional scales between groups. Meanwhile, all hypoactive sexual desire disorder patients showed normal hormone levels. Compared with healthy controls, hypoactive sexual desire disorder patients showed higher scores on the Arizona Sexual Experience Scale and its sexual desire subscale. In fractional anisotropy-weighted brain networks, a decreased clustering coefficient was found in the left middle frontal gyrus (orbital part), and decreased local efficiency was found in the left amygdala of hypoactive sexual desire disorder patients when compared with healthy controls.

CONCLUSION

The present study demonstrated impaired left middle orbital frontal gyrus and amygdala in the structural brain network of hypoactive sexual desire disorder patients, which might be the central pathological mechanisms underlying hypoactive sexual desire disorder and could be used as a neuroimaging diagnostic biomarker for hypoactive sexual desire disorder.

Co-Community Network Analysis Reveals Alterations in Brain Networks in Alzheimer's Disease

Background: Alzheimer's disease (AD) is a common neurodegenerative disease. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, our research goal is to investigate how the brain network structure, as measured by resting-state fMRI, differs across distinct physiological states. Method: With the research goal of addressing the limitations of BOLD signal-based brain networks constructed using Pearson correlation coefficients, individual brain networks and community detection are used to study the brain networks based on co-community probability matrices (CCPMs). We used CCPMs and enrichment analysis to compare differences in brain network topological characteristics among three typical brain states. Result: The experimental results indicate that AD patients with increasing disease severity levels will experience the isolation of brain networks and alterations in the topological characteristics of brain networks, such as the Somatomotor Network (SMN), dorsal attention network (DAN), and Default Mode Network (DMN). Conclusion: This work suggests that using different data-driven methods based on CCPMs to study alterations in the topological characteristics of brain networks would provide better information complementarity, which can provide a novel analytical perspective for AD progression and a new direction for the extraction of neuro-biomarkers in the early diagnosis of AD.

SFPGCL: Specificity-preserving federated population graph contrastive learning for multi-site ASD identification using rs-fMRI data

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder that affects people's social communication and daily routine. Most existing imaging studies on ASD use single site resting-state functional magnetic resonance imaging (rs-fMRI) data, which may suffer from limited samples and geographic bias. Improving the generalization ability of the diagnostic models often necessitates a large-scale dataset from multiple imaging sites. However, centralizing multi-site data generally faces inherent challenges related to privacy, security, and storage burden. Federated learning (FL) can address these issues by enabling collaborative model training without centralizing data. Nevertheless, multi-site rs-fMRI data introduces site variations, causing unfavorable data heterogeneity. This negatively impacts biomarker identification and diagnostic decision. Moreover, previous FL approaches for fMRI analysis often ignore site-specific demographic information, such as age, gender, and full intelligence quotient (FIQ), providing useful information as non-imaging features. On the other hand, Graph Neural Networks (GNNs) are gaining popularity in fMRI representation learning due to their powerful graph representation capabilities. However, existing methods often focus on extracting subject-specific connectivity patterns and overlook inter-subject relationships in brain functional topology. In this study, we propose a specificity-preserving federated population graph contrastive learning (SFPGCL) framework for rs-fMRI analysis and multi-site ASD identification, including a server and multiple clients/sites for federated model aggregation. At each client, our model consists of a shared branch and a personalized branch, where parameters of the shared branch are sent to the sever, while those of the personalized branch remain local. This setup facilitates invariant knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph neural network to learn temporal dynamics in fMRI data invariant to each site, and introduce a model-contrastive learning method to mitigate client data heterogeneity. In the personalized branch, we utilize population graph structure to fully integrate demographic information and functional network connectivity to preserve site-specific characteristics. Then, a site-invariant population graph is built to derive site-invariant representations based on the dynamic representations acquired from the shared branch. Finally, representations generated by the two branches are fused for classification. Experimental results on Autism Brain Imaging Data Exchange (ABIDE) show that SFPGCL achieves 80.0 % accuracy and 79.7 % AUC for ASD identification, which outperforms several other state-of-the art approaches.

An Activation Likelihood Estimation Meta-Analysis of Voxel-Based Morphometry Studies of Chemotherapy-Related Brain Volume Changes in Breast Cancer

BACKGROUND/OBJECTIVES

Breast cancer chemotherapy patients and survivors face cognitive side effects that are not fully understood. Neuroimaging can provide a unique way to study these effects; however, it can be difficult to recruit large numbers of subjects. Our meta-analysis aims to synthesize volumetric neuroimaging data to highlight consistent findings in regional brain volume changes to further advance our understanding of the chemotherapy-related cognitive impairments faced by breast cancer patients and survivors.

METHODS

An Activation Likelihood Estimation analysis was conducted across the data from eight voxel-based morphometry experiments examining changes in the brains of breast cancer patients and survivors exposed to chemotherapy over time and three voxel-based morphometry experiments comparing chemotherapy-exposed subjects to controls with and without breast cancer.

RESULTS

There were consistent volume reductions across the whole brain in both experiment groups. The subjects' over-time analysis showed peak consistency among the studies in the right inferior frontal gyrus and the left insula.

CONCLUSIONS

Chemotherapy for non-central nervous system cancers such as breast cancer can cause physical changes throughout the brain that can be quantitatively measured by neuroimaging methodologies and may underlie persistent cognitive deficits in some individuals.

Coupled sleep rhythm disruption predicts cognitive decline in Alzheimer's disease

The effect of sleep on memory consolidation depends on the precise interaction of slow oscillations (SOs), theta bursts, and spindles. Disruption in coupling of these sleep rhythms has been reported for individuals with Alzheimer's disease (AD). However, it is unknown how the sleep rhythms evolve during AD progression and whether disrupted sleep rhythms facilitate cognitive decline in AD. Here, we analyze data of 93 individuals from sleep electroencephalography (EEG), MRI, cerebrospinal fluid (CSF) AD biomarkers, and two-year cognitive assessments among three populations: AD dementia (n = 33), mild cognitive impairment (MCI) due to AD (n = 38), and cognitively normal (CN, n = 22). Our study identifies the evolving pattern of coupled sleep rhythm disruption with advancing cognitive stages in AD. Specifically, the frequency of SO-theta burst coupling and SO-spindle coupling decreases from CN to MCI; SO-theta burst coupling and SO-spindle coupling further misalign from MCI to AD dementia. The APOE ε4 allele and elevated amyloid and tau burden are associated with coupled sleep rhythm disruption. Hippocampal and medial prefrontal cortex atrophy are respectively linked to disruption of SO-theta burst coupling and SO-spindle coupling. Notably, coupled sleep rhythm disruption predicts accelerated cognitive decline over a two-year follow-up period. Our study presents that integrating sleep EEG with CSF and MRI biomarkers enhances the predictive ability for AD progression, which unravels the potential of sleep rhythms as monitoring and interventional targets for AD.

Cortical tinnitus evaluation using functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) estimates the cortical hemodynamic response induced by sound stimuli. fNIRS can be used to understand the symptomatology of tinnitus and consequently provide effective ways of evaluating and treating the symptom.

OBJECTIVE

Compare the changes in the oxy-hemoglobin and deoxy-hemoglobin concentration of individuals with and without tinnitus using auditory stimulation by fNIRS.

METHODS

A tinnitus group (n = 23) and a control group (n = 23) were evaluated by an auditory task for assessing sound-evoked auditory cortex activity. The fNIRS was composed of 20 channels arranged into 4x2 arrays over the frontal, temporal and parietal cortices. Then, a passive listening block-paradigm design was adopted with reoccurring blocks of tasks with 15 s interspersed with randomized silence periods between 15-25 s.

RESULTS

There was a significant difference in the condition (type of sound), region of interest (ROI) and channel. As well as significant interaction in group and condition, and group and channel. The Tinnitus Frequency decreased HbO levels, while other sounds (white noise - WN and 1KHZ) increased HbO levels. All conditions differed from each other, except 1KHz with Baseline (silence) in the control group. Regarding the channels, the frontal channels (1, 3, and 11) differed in the tinnitus group, while in the control group a difference was observed in the channels of the frontal, temporal and parietal regions.

CONCLUSION

The type of sound presented, and brain region influenced the variations in HbO levels, but there was no difference between tinnitus and control participants. The tinnitus loudness, annoyance, and severity showed a weak correlation with variations in HbO levels.

Functional brain connectivity of the salience network in alcohol use and anxiety disorders

The interplay between alcohol use disorder (AUD) and anxiety disorders (ANX) is well-documented, yet the underlying neurobiological mechanisms remain elusive. This study aims to elucidate these mechanisms by examining the resting-state functional connectivity (RSFC) within the salience network and to the amygdala, both implicated in alcohol and anxiety disorders. We analyzed data from 264 inpatient participants culled from a wider group of 518 inpatients at The Menninger Clinic in Houston, TX, categorized into four groups (n = 66 each) based on DSM-IV diagnoses: AUD without ANX (AUD), ANX without AUD (ANX), concurrent AUD and ANX (BOTH), and neither (NEITHER). Our findings reveal significant RSFC differences, particularly between the right supramarginal gyrus (SMG) and 1) right rostral prefrontal cortex (RPFC) (corrected p = 0.029; RSFC significantly higher in NEITHER than in BOTH), and 2) left supramarginal gyrus (SMG) (corrected p = 0.016; RSFC significantly higher in AUD and NEITHER than in BOTH). Furthermore, correlations with a clinical measure for alcohol use (World Health Organization Alcohol, Smoking and Substance Involvement Screening Test; WHO ASSIST) indicated significant relationships: WHO ASSIST alcohol scores negatively correlated with right SMG to right RPFC RSFC (r = -0.14, p = 0.02) and positively correlated with the interhemispheric SMG RSFC (r = 0.17, p = 0.006). This research enhances our understanding of the complex neurobiological interconnections between alcohol use and anxiety disorders, suggesting a disrupted neural architecture that may underpin the behavioral manifestations observed in these highly comorbid conditions.

Gray matter morphology and pain-related disability in young adults with low back pain

Structural neuroplasticity in the brain may contribute to the persistence of low back pain (LBP) symptoms and the disability associated with them. It is not known if structural adaptations are evident early in the lifespan in young adults with LBP. This study compared gray matter in cortical sensorimotor regions in young adults with and without persistent LBP and identified gray matter and clinical predictors of pain-related disability. Eighty-two individuals with and without a history of LBP participated. Peak and average gray matter density in cortical sensorimotor regions of interest was quantified using voxel-based morphometry. Pain-related disability, pain intensity, pain duration, and pain-related fear were also assessed. Multiple linear regression was used to determine independent predictors of pain-related disability. We document significantly greater peak gray matter density in individuals with LBP in the primary somatosensory cortex, angular gyrus, and the midcingulate cortex. Pain-related disability positively correlated with average gray matter density in the posterior cingulate cortex. The most robust predictors of disability were average gray matter in the posterior cingulate, pain intensity, and pain-related fear. We demonstrate that in young adults, persistent LBP, and pain-related disability, are linked with structural differences in regions forming part of the brain network termed the pain matrix. In contrast with studies of LBP in older adults, our findings of increased rather than decreased gray matter in young adults with LBP suggest that gray matter may increase initially in response to nociceptive pain.

Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network

BACKGROUND

Mild cognitive impairment (MCI) is a heterogeneous disorder with significant individual variabilities in clinical and biological features. Abnormal inter-regional structural covariance suggests disruption of the brain structural network in MCI. Most studies have examined group-level structural covariance alterations while ignoring individual-level differences. Hence, we aimed to investigate the heterogeneity of MCI using individual differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted images of 596 MCI patients and 309 cognitively normal (CN) were collected from the ADNI database as discovery dataset, and 122 MCI and 117 CN from the OASIS-3 dataset as validation cohort. We constructed each patient's IDSCN using regional gray matter volume and applied K-means clustering analysis to identify MCI subtypes based on significantly altered covariance edges. Then, clinical features, brain structure, and gene expression profiles were evaluated for each subtype.

RESULTS

In the ADNI dataset, MCI patients exhibited significant alterations in structural covariance edges, mainly involving the hippocampus, parahippocampal gyrus, and amygdala. Two robust MCI subtypes were identified. Subtype 1 showed faster disease progression relative to subtype 2, which was validated in the independent OASIS-3 dataset. Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal ion transport and neuron projection development. Finally, correlation analysis and functional annotation further revealed that the affected edges were related to cognitive performance and implicated in memory and emotion terms.

CONCLUSIONS

In summary, these findings offer new perspectives into understanding the heterogeneity of MCI and facilitate strategies for future precision medicine.

Association of spatiotemporal interaction of gamma oscillations with heart rate variability during response inhibition processing in patients with major depressive disorder: An MEG study

BACKGROUND

Impairment in response inhibition function is highly prevalent in patients with major depressive disorder (MDD), yet the spatiotemporal neural activity underlying response inhibition and its relationship with the autonomic nervous system (ANS) remains unclear.

METHODS

35 MDD participants and 35 healthy controls (HC) were included with magnetoencephalography (MEG) and electrocardiogram (ECG) data collecting during a go/no-go task. Heart rate variability (HRV) indices were calculated from the ECG data. Differences in functional connectivity (FC) of gamma oscillations (60-90 Hz) between 0-200 ms, 200-400 ms, and 400-600 ms in the two groups after no-go stimuli were analyzed, and the correlation between FC and HRV indices was examined.

RESULTS

The MDD group exhibited poorer task performance and lower HRV indices than the HC group. During the 200-400 ms period, compared to the HC group, the MDD group exhibited decreased FC between the left inferior frontal gyrus (opercular part) and right temporal pole (middle temporal gyrus) (t = 3.62, p < 0.05), and increased FC between the right superior frontal gyrus (orbital part) and right superior occipital gyrus (t = 3.68, p < 0.05). Additionally, a significant positive correlation was found between FC of the left inferior frontal gyrus (opercular part) and right middle temporal gyrus (temporal pole) and the HRV index RMSSD in the MDD group (r = 0.491, p < 0.05).

CONCLUSION

Abnormal spatiotemporal interactions in gamma oscillations related to response inhibition are observed in MDD patients and abnormal gamma oscillations showed task-dependent covariation with ANS indices, suggesting their potential interplay in MDD pathophysiology.