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

Social stress enhances intuitive prosocial behavior in males while disrupting self-reward processing: Evidence from behavioral, computational, and neuroimaging studies

In this study, we present behavioral, computational, and neuroimaging evidence that social stress enhances intuitive prosocial value processing while impairing self-reward processing. When deciding on monetary rewards for individuals at various social distances, participants who exhibited elevated cortisol levels following a social stress task were more inclined to choose a disadvantageous unequal option. Neuroimaging data revealed that participants more likely to choose the disadvantageous unequal option exhibited increased encoding of other-regarding rewards in the ventral medial prefrontal cortex (mPFC), whereas the dorsal mPFC exhibited a decrease in encoding. Mediation analyses further indicated that both the ventral and dorsal mPFC indirectly mediated the relationship between heightened cortisol levels and a greater likelihood of choosing a disadvantageous unequal option. Additionally, effective connectivity analysis results demonstrated that cortisol has an excitatory effect on the dorsal mPFC via the ventral striatum, while simultaneously sending inhibitory signals to the dorsal mPFC via the dorsal striatum. These findings provide empirical evidence to clarify the ambiguity surrounding the effects of stress on prosocial decision-making, suggesting that social stress disrupts deliberative decision-making while simultaneously promoting intuitive prosocial motivation through the differential modulation of hierarchically organized cortico-striatal loops.

Individual alpha frequency tACS reduces static functional connectivity across the default mode network

Introduction

Research on the influence of transcranial alternating current stimulation over alpha functional connectivity (FC) is scarce, even when it poses as a potential treatment for various diseases. This study aimed to investigate the effects of individual alpha frequency tACS (IAF-tACS) on FC within the default mode network (DMN) in healthy individuals, particularly following the triple network model.

Materials and methods

27 healthy participants were recruited, who underwent a 20-min IAF-tACS session over parieto-occipital areas and three magnetoencephalography (MEG) recordings: two pre-stimulation and one post-stimulation. Participants were randomly assigned to either the stimulation or sham group. Both dynamic FC (dFC) and static FC (sFC) were evaluated through the leakage corrected amplitude envelope correlation (AEC-c). Statistical analyses compared both Pre-Post FC ratio between groups through ratio t-tests and intragroup FC changes through repeated measures t-tests, with FDR correction applied to account for multiple comparisons. An additional analysis simulated the influence of the cortical folding on the effect of tACS over FC.

Results

IAF-tACS significantly decreased sFC in intra- and inter-DMN links in the stimulation group compared to the sham group, with a special influence over antero-posterior links between hubs of the DMN. Negative correlations were found between AEC-c sFC changes and power alterations in posterior DMN areas, suggesting a complex interaction between cortical folding and electric field direction. On the other hand, dFC increased in both sham and stimulation groups, and no between-group differences were found.

Conclusion

Against our initial hypothesis, IAF-tACS reduced sFC in the DMN, possibly due to phase disparities introduced by cortical gyrification. These findings suggest that tACS might modulate FC in a more complex manner than previously thought, highlighting the need for further research into the personalized application of neuromodulation techniques, as well as its potential therapeutic implications for conditions like Alzheimer's disease.

[11C]Carfentanil PET Whole-Body Imaging of μ-Opioid Receptors: A First in-Human Study

μ-opioid receptors (MORs) are G-coupled receptors widely expressed in the brain and body. MORs have a high affinity for both endogenous opioids such as β-endorphins and exogenous opioids such as fentanyl. They mediate pain and reward and have been implicated in the pathophysiology of opioid, cocaine, and other substance use disorders. Using an instrument with a long axial field of view and the MOR-selective radioligand [11C]carfentanil, we measured the whole-body distribution of MORs in 13 healthy humans. We also examined sex differences in MOR distribution at baseline and after pretreatment with the MOR antagonist naloxone. Methods: Six female and 7 male healthy subjects underwent 2 [11C]carfentanil PET imaging sessions-one at baseline and one immediately after pretreatment with the MOR antagonist naloxone (13 μg/kg). Whole-body PET imaging was performed on an instrument with a 142-cm axial bore. [11C]carfentanil brain distribution volume ratios were determined using the occipital cortex and the visual cortex within it as reference regions. For peripheral organ distribution volume ratios, the descending aorta and proximal-extremity muscle (biceps/triceps) were used as reference regions. Results: Naloxone blockade reduced MOR availability by 40%-50% in the caudate, putamen, thalamus, amygdala, and ventral tegmentum, brain regions known to express high levels of MORs. Women showed greater receptor occupancy in the thalamus, amygdala, hippocampus, and frontal and temporal lobes and a greater naloxone-induced reduction in thalamic MOR availability than men (P < 0.05). For determining brain MOR availability, there was less variance in the visual cortex than in the occipital cortex reference region. For peripheral MOR determination, the descending aorta reference region showed less variance than the extremity muscle, but both showed blocking effects of naloxone. Conclusion: [11C]carfentanil whole-body PET scans are useful for understanding MOR physiology under both baseline and blocking conditions. Extra-central nervous system reference regions may be useful for quantifying radiotracers when a region devoid of binding in the central nervous system is unavailable. The long axial field of view was useful for measuring changes in the short-lived radiotracer [11C]carfentanil, with and without naloxone blocking. Further research is needed to evaluate the behavioral and clinical relevance of sex differences in naloxone-MOR interactions.

Using baseline MRI radiomic features to predict the efficacy of repetitive transcranial magnetic stimulation in Alzheimer's patients

The efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for Alzheimer's disease (AD) is uncertain at baseline. Herein, we aimed to investigate whether radiomic features from the pre-treatment MRI data could predict rTMS efficacy for AD treatment. Out of 110 participants with AD in the active (n = 75) and sham (n = 35) rTMS treatment groups having T1-weighted brain MRI data, we had two groups of responders (active = 55 and sham = 24) and non-responders (active = 20 and sham = 11). We extracted histogram-based radiomic features from MRI data using 3D Slicer software; the most important features were selected utilizing a combination of a two-sample t-test, correlation test, least absolute shrinkage, and selection operator. The support vector machine classified rTMS responders and non-responders with a cross-validated mean accuracy/AUC of 81.9%/90.0% in the active group and 87.4%/95.8% in the sham group. Further, the radiomic features of the active group significantly correlated with participants' AD assessment scale-cognitive subscale (ADAS-Cog) change after treatment (false discovery rate corrected p < 0.05). Given that baseline radiomic features were able to accurately predict AD patients' responses to rTMS treatment, these radiomic features warrant further investigation for personalizing AD therapeutic strategies.

Blood biomarkers of amyloid and tau pathologies, brain degeneration, inflammation, and oxidative stress in early- and late-onset Alzheimer's disease

BackgroundNumerous blood biomarkers have emerged as promising biomarkers for Alzheimer's disease (AD) and cognitive decline, but limited knowledge exists concerning the difference of blood biomarkers between early-onset and late-onset cases.ObjectiveInvestigate blood biomarkers associated with amyloid and tau pathologies, brain degeneration, inflammation, and oxidative stress in individuals afflicted with both early-onset and late-onset AD, as well as in age-matched healthy controls.MethodsA total of 125 participants were enrolled. We assessed levels of 18 distinct blood biomarkers and their associations with cerebrospinal fluid biomarkers, neuropsychological test scores, APOE ε4 carrier status, and neuroimaging markers. The diagnostic potential of blood biomarkers was investigated.ResultsIn early-onset AD patients, levels of blood Interleukin (IL)-4, IL-6, and Tumor necrosis factor-alpha (TNF-α) were notably lower comparing to late-onset patients. AD patients exhibited higher blood levels of phosphorylated-tau181 (p-tau181), neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP), as well as lower levels of amyloid-β (Aβ)42 and IL-12p70. Oxidative stress markers, including malondialdehyde, total antioxidant capacity, and superoxide dismutase, exhibited a progressive trend across the continuum of AD. Inflammatory markers demonstrating correlations with neuroimaging markers. Blood levels of Aβ42, p-tau181, NfL, and GFAP associated with neuropsychological scores and effectively discriminated AD, with GFAP exhibiting particular relevance in early-onset cases.ConclusionsInflammatory markers exhibited differences between patients with early- and late-onset AD, associated with alterations in brain structure and function. With the progression of disease continuum, a decrement in antioxidant capacity was observed. Blood Aβ42, p-tau181, NfL, and GFAP showed promise in detecting cognitive decline and AD.

Shared neural correlates of interference control and response inhibition in adolescence and young adulthood

Inhibitory control (IC) is the ability to inhibit dominant or automatic behaviours, responses or thoughts, to allow for the selection of appropriate goal-directed responses. IC is a core executive function and has been associated with specific brain regions, such as the right inferior frontal gyrus (IFG) but also with a broader fronto-parietal network similar to the multiple demand network, which is thought to support the elaboration and maintenance of structured mental programs across a range of tasks. Here, functional magnetic resonance imaging data were collected from different IC tasks within the same participants to investigate similarities and differences in brain activation between tasks and age groups. Adolescents (11-15 years-old, n = 34) and adults (18-26, n = 33) completed block-design numerical Stroop and simple and complex Go/No-go (GNG) tasks. Univariate analyses showed large overlapping fronto-parietal activation in the Stroop and complex GNG tasks, with more limited activation in the simple GNG task. When compared to adolescents, adults showed greater increases in activation in the right IFG in the Stroop task and in temporo-parietal and precentral clusters in the complex GNG task. High multivariate similarity was observed across fronto-parietal regions between complex GNG and Stroop tasks, and between simple and complex GNG tasks, but was much lower between Stroop and simple GNG tasks. Adults showed greater similarity between complex GNG and Stroop tasks, suggesting increased reliance on shared neural processes across tasks, rather than increased specialisation of brain networks to specific aspects of IC over development.

Dynamic modular dysregulation in multilayer networks underlies cognitive and clinical deficits in first-episode schizophrenia

Schizophrenia has been identified to exhibit significant abnormalities in brain functional networks, which are likely to underpin the cognitive and functional impairments observed in patients. Graph theoretical analysis revealed the disrupted modularity in schizophrenia, however, the dynamic network abnormalities in schizophrenia remains unclear. We collected the resting-state functional magnetic resonance imaging data from 82 first-episode schizophrenia (FES) patients and 55 healthy control (HC) subjects. Dynamic functional connectivity matrices were constructed and a multilayer network model was employed to run the dynamic modularity analysis. We also performed correlation analyses to investigate the relationship between flexibility, cognitive function and clinical symptoms. Our findings indicate that FES patients exhibit higher multilayer modularity. The node flexibility of FES patients were found elevated in several brain regions, which were included in the default mode network, fronto-parietal network, salience network and visual network. The node flexibility metrics in aberrant brain regions were found to demonstrate significant correlations with cognitive function and negative symptoms in patients with FES. These findings suggest a pathological imbalance in brain network dynamics, where abnormal modular organization might contribute to the cognitive impairment and functional deficits in schizophrenia.

Brain Responses During Face Processing in Conduct Disorder: Considering Sex and Callous-Unemotional Traits

Functional magnetic resonance imaging (fMRI) studies of conduct disorder (CD) have mostly been limited to males. Here, we examined whether male and female youth with CD showed similar or distinct alterations in brain responses to emotional faces, using a large, mixed-sex sample of youths with CD. We also investigated the influence of callous-unemotional (CU) traits. Brain responses to angry, fearful, and neutral faces were assessed in 161 CD youths (74 females) and 241 typically-developing (TD) youths (139 females) aged 9-18 years. Categorical analyses tested for diagnosis effects (CD vs. TD and CD with high [CD/HCU] vs. low [CD/LCU] levels of CU traits vs. TD) and sex-by-diagnosis interactions. When processing faces in general (all faces versus baseline), youths with CD exhibited lower amygdala responses compared to TD youths, which appeared driven by the CD/HCU subgroup. Sex-by-CU subgroups interactions were identified in the amygdala (CD/LCU females

Changes in brain function during negative emotion processing following cognitive-behavioural therapy in depressive disorders

BACKGROUND

Cognitive-behavioural therapy (CBT) is a first-line treatment for depressive disorders, but research on its neurobiological mechanisms is limited. Given the heterogeneity in CBT response, investigating the neurobiological effects of CBT may improve response prediction and outcomes.

AIMS

To examine brain functional changes during negative emotion processing following naturalistic CBT.

METHOD

In this case-control study, 59 patients with depressive disorders were investigated before and after 20 CBT sessions using a negative-emotion-processing paradigm during functional magnetic resonance imaging, clinical interviews and depressive symptom questionnaires. Healthy controls (n = 60) were also assessed twice within an equivalent time interval. Patients were classified into subgroups based on changes in diagnosis according to DSM-IV criteria (n = 40 responders, n = 19 non-responders). Brain activity changes were examined using group × time analysis of variance for limbic areas, and at the whole-brain level.

RESULTS

Analyses yielded a significant group × time interaction in the hippocampus (P family-wise error [PFWE] = 0.022, η P 2 = 0.101), and a significant main effect of time in the dorsal anterior cingulate cortex (PFWE = 0.043, η P ² = 0.098), resulting from activity decreases following CBT (PFWE ≤ 0.024, η P ² ≤ 0.233), with no changes in healthy controls. Hippocampal activity decreases were driven by responders (PFWE ≤ 0.020, η P ² ≤ 0.260) and correlated with symptom improvement (r = 0.293, P = 0.024). Responders exhibited higher pre-treatment hippocampal activity (PFWE = 0.017, η P ² = 0.189).

CONCLUSIONS

Following CBT, reduced activity in emotion-processing regions was observed in patients with depressive disorders, with hippocampal activity decreases linked to treatment response. This suggests successful CBT could correct biased emotion processing, potentially by altering activity in key areas of emotion processing. Hippocampal activity may function as a predictive marker of CBT response.

Alterations in Topological Structure and Modular Interactions in Pediatric Patients with Complete Spinal Cord Injury: A Functional Brain Network Study

Traumatic complete spinal cord injury (CSCI) leads to severe impairment of sensory-motor function, and patients often suffer from neuropsychological deficits such as anxiety, depression, and cognitive deficits, which involve different brain functional modules. However, the alterations in modular organization and the interactions between these modules in pediatric patients with CSCI remain unclear. In this study, a total of 70 participants, including 34 pediatric CSCI patients and 36 healthy controls (HCs) aged 6 to 12 years, underwent whole-brain resting-state functional MRI. The functional networks were analyzed via a graph theory approach based on the 90-region Automated Anatomical Labeling (AAL 90) atlas, generating a 90 × 90 correlation matrix. Metrics for nodal, global, and modular scales were calculated to evaluate alterations in the network's topology. Between-group comparisons and partial correlation analysis were performed. Compared to HCs, pediatric CSCI patients exhibited significant decreases in nodal metrics, particularly in subcortical networks (SN) like the bilateral thalamus. Besides, the distribution of core nodes changed, with five newly added core nodes primarily located in the regions of the default mode network (DMN). For modular interactions, patients group presented increased connectivity within the DMN and between the DMN and the attention network (AN) but reduced connectivity between DMN and SN, DMN and vision network (VN), and AN and SN. Notably, the participation coefficient (Pc) of the TPOmid.L (left temporal pole: middle temporal gyrus) was positively correlated with motor scores, suggesting its potential as an indicator for evaluating the motor function in pediatric CSCI patients. Additionally, the patients demonstrated a different modular structure with significantly lower modularity. These findings suggest that functional network and modular alterations chiefly occur in emotional cognition and vision-associated regions, emphasizing the importance to focus on their psychocognitive well-being and providing evidence for visual-feedback related rehabilitation strategies.

Differential modulation of neural oscillations in perception-action links in Tourette syndrome

Gilles de la Tourette Syndrome (GTS) is a multi-faceted neuro-psychiatric disorder. While novel conceptions overcoming the criticized categorization of GTS as a movement disorder are on the rise, little is known about their neural implementation and whether there are links to known pathophysiological processes in GTS. This is the case for conceptions suggesting that aberrant perception-action processes reflect a key feature of GTS. Building on the concept that overly strong perception-action associations are pivotal to understanding GTS pathophysiology, we examined how these associations influence response inhibition and used EEG methods to examine the importance of theta, alpha and beta band activity due to their known relevance for GTS pathophysiology. In this case-control study, behavioural analyses revealed that adult patients with GTS experienced greater difficulty during motor response inhibition when perceptual features of Nogo stimuli overlapped with perceptual features of Go stimuli, indicating impaired reconfiguration of perception-action associations. Neurophysiological findings showed robust differential patterns of modulation in theta and alpha band activity between neurotypical (NT) individuals and GTS patients. Specifically, GTS patients exhibited stronger and more extended theta band modulation but weaker and more restricted alpha band modulation during overlapping Nogo trials than NT individuals. Unlike NT individuals, GTS patients did not exhibit beta band modulations necessary for dynamically handling perception-action codes. The findings highlight increased theta band modulation in GTS patients' significant stronger perception-action bindings and a lack of compensatory alpha band modulation. The robust differential modulation observed provides novel insights, emphasizing theta and alpha oscillations as key elements in GTS pathophysiology and offering potential implications for targeted cognitive-behavioural interventions.

High-Order Graphical Topology Analysis of Brain Functional Connectivity Networks Using fMRI

The brain connectivity network can be represented as a graph to reveal its intrinsic topological properties. While classical graph theory provides a powerful framework for examining brain connectivity patterns, it often focuses on low-order graphical indicators and pays less attention to high-order topological metrics, which are crucial to the comprehensive understanding of brain topology. In this paper, we capture high-order topological features via a graphical topology analysis framework for brain connectivity networks derived from functional Magnetic Resonance Imaging (fMRI). Several high-order metrics are examined across varying sparsity levels of binary graphs to trace the evolution of brain networks. Topological phase transitions are primarily investigated that reflect brain criticality, and a novel indicator called "redundant energy" is proposed to measure the chaos level of the brain. Extensive experiments on diverse datasets from healthy controls validate the reproducibility and generalizability of our framework. The results demonstrate that around critical points, classical graph theoretical indicators change sharply, driven by crucial brain regions that have high node curvatures. Further investigations on fMRI of subjects with and without Parkinson's disease uncover significant alterations in high-order topological features which are further associated with the severity of the disease. This study provides a fresh perspective on studying topological architectures of the brain, with the potential to expand our comprehension on brain function in both healthy and diseased states.

The nature of syntactic working memory during relative clause processing: fMRI evidence from multiple anatomic ROIs

Relative clauses (RC) are a common embedded structure in natural language. They can be classified as Subject-extracted RC (SRC) and object-extracted RC (ORC). Previous studies have suggested an ORC advantage in Chinese. This is consistent with the memory-based theories, which propose that more syntactic working memory (SWM) is needed during the Chinese SRC processing than the ORC processing. However, it is still unclear about the nature of the SWM (language-specific vs. domain-general). In the current study, participants were asked to read Chinese SRC and ORC sentences while undergoing functional magnetic resonance imaging (fMRI) scanning. Because of the important role of the inferior frontal gyrus (IFG) and superior temporal gyrus (STG) in SWM, these two brain regions were divided into sub-regions. Critically, LIFGorbital is more related to language-specific processing whereas LIFGopercular is more related to domain-general processing. Activation analyses and Granger causality (GC) analyses were both conducted. The results first provided more neurophysiological evidence of the ORC advantage in Chinese. More importantly, the results of activation analyses showed that LIFGoper was more activated in the contrast of SRC > ORC. In contrast, the results of GC analyses showed that LIFGorb was more involved in the SRC-specific connectivity. Altogether, these results suggest that the SWM induced by the contrast of SRC > ORC was related to both the language-specific and domain-general processing.

Superior temporal gyrus activation modulates revenge-like aggressive response tendencies in antisocial men after provocation: Evidence from an fMRI study using a modified Taylor aggression paradigm

Antisocial personality disorder (ASPD) is characterized by a disregard of others' feelings, social norms, rules and obligations as well as increased reactive and proactive aggression among others. Experimental investigations of neural correlates of provocation and associated aggression often use competitive reaction time tasks played against a fictional opponent, such as the Taylor Aggression Paradigm (TAP). However, previous TAP neuroimaging research mainly focused on aggression levels in healthy and not forensic populations. This functional magnetic resonance imaging study on monetary TAP (mTAP) provocation and aggression assesses 20 violent offenders with ASPD and compares behavioral and neural responses to 17 age and education-matched healthy community participants (HC). Behaviorally, no significant group differences emerged, all participants reacted with increased punishment when faced with high vs. low provocation. On the neural level, offenders showed significantly stronger right superior temporal gyrus (STG) activation than HC during provocation. Exploratory analyses indicated that this STG activation was behaviorally relevant, as those with ASPD who expressed stronger STG activation during provocation also responded with stronger unprovoked punishment during the aggression phase. In addition, during the aggression phase, provocation was accompanied by increased left superior parietal lobe activation in ASPD compared to HC. In sum, this first mTAP fMRI study in ASPD found enhanced neural processing of provocation in ASPD which was also associated with more unprovoked aggression. The increased neural processing of provocation in ASPD and its association with subsequent higher aggression could have clinical relevance. At least, cognitive processing of perceived provocation could be a worthwhile intervention target for reducing aggressive response tendencies.

Volumetric response after closure of a spinal CSF leak in patients with spontaneous intracranial hypotension: a multicompartmental longitudinal study

BACKGROUND

Cerebrospinal fluid (CSF) loss in spontaneous intracranial hypotension (SIH) is accompanied by volume shifts between the intracranial compartments. This study investigated tricompartimental and longitudinal volume shifts after closure of a CSF leak.

METHODS

Patients with SIH and suitable pre-therapeutic and post-therapeutic imaging for volumetric analysis were identified from our tertiary care center between 2020 and 2023. The Bern SIH score was calculated. Pre-interventional and post-interventional volumetry encompassed the CSF, parenchymal and venous compartments (ie, venous sinus and choroid plexus volumes).

RESULTS

In total, 32 patients with SIH (49.7±16.0 years, 22 women) met inclusion criteria. The mean SIH score decreased between baseline (4.5±2.7) and early (2.7±2.3, <7 days after intervention), and also late follow-up (1.4±1.7, follow-up ≥7 days) after leak closure. This was accompanied by a significant increase in ventricular volume from 22.1 to 25.0 mL (P=0.01) at early follow-up, and 23.9 mL at later follow-up (P=0.080). In contrast, venous sinus volumes decreased from 13.8 to 9.6 mL (P=0.016) at early follow-up, and 10.0 mL (P=0.007) at late follow-up. No significant change in mean choroid plexus, total gray or total white matter volume was observed.

CONCLUSIONS

Closure of a spinal CSF leak leads to an early increase in ventricular CSF volume and a decrease in venous sinus volume. The results reflect the long-term convergence of the SIH score to normal values and indicate that permanent closure of a CSF leak induces a stable recompensation of the intracranial compartments without involving significant volume shifts within the cerebral parenchyma.

Revealing multiple biological subtypes of schizophrenia through a data-driven approach

INTRODUCTION

The brain imaging subtypes of schizophrenia have been widely investigated using data-driven approaches. However, the heterogeneity of SZ in multiple biological data is largely unknown.

METHODS

A data-driven model was used to classify brain imaging, gut microbiota, and brain-gut fusion data obtained through a dot product fusion method, identifying significant subtypes and calculating their correlations with clinical symptoms and cognitive performance.

RESULTS

These subtypes remain relatively independent and demonstrate typical features and biomarkers, which are significantly associated with clinical symptoms and cognitive performance. Two brain subtypes with opposite structural and functional changes are identified: (1) a structural variant-dominant brain subtype with negative symptoms and cognitive deficits and (2) a functional alteration-dominant brain subtype with positive symptoms. The three gut subtypes include the following: (1) Collinsella-dominant; (2) Prevotella-dominant with positive symptoms; and (3) Streptococcus-dominant. Two brain-gut subtypes show different abnormalities in brain‒genus linkages: (1) strong connectivity of "brain function in the temporal and parietal lobes-Prevotella" with reduced attention scores and (2) strong connectivity of "brain structure and function in the frontal and parietal lobes-multiple genera" with positive symptoms. Notably, brain subtypes and brain-gut subtypes are most relevant to clinical symptoms, whereas gut subtypes reveal more cognitive biomarkers.

CONCLUSION

These findings show the potential to identify multiple biological subtypes with distinct biomarkers, thereby suggesting the possibility of personalized and precise treatment for SZ patients.

Evaluation of amyloid PET positivity using machine learning on 18F-FDG PET images

BACKGROUND

Since the approval of disease-modifying drugs for Alzheimer's disease, the demand for amyloid positron emission tomography (PET) scans, which are crucial for determining treatment eligibility, is expected to increase significantly. We thus investigated the ability of an algorithm to predict amyloid accumulation from 18F-fluorodeoxyglucose (FDG)-PET images for use in amyloid PET screening.

METHODS

We analyzed the images of 194 subjects with cognitive disorders who had undergone brain FDG-PET, amyloid PET using Pittsburgh compound-B (11C-PiB), and MRI scans at Kindai University Hospital between 2011 and 2018. Among them, 108 subjects showed positive amyloid accumulation; the other 86 did not. For the 108 positive cases, the input values were the region of interest-based calculated from the automatic anatomical labeling template, which divides the brain into 120 regions, and applied to the anatomically standardized FDG-PET images of each subject. We then used a support vector machine (SVM) machine learning algorithm and conducted a tenfold cross-validation to assess the algorithm's accuracy for predicting amyloid accumulation from FDG-PET images.

RESULTS

We observed 81.5% accuracy, 78.5% sensitivity, 84.6% specificity, and an area under the curve (AUC) of 0.846 during training. The validation results for the trained model revealed 85.9% accuracy, 88.4% sensitivity, 81.0% specificity, and an AUC of 0.918.

CONCLUSION

These results indicate that the performance of our algorithm to predict amyloid accumulation from 18FDG-PET images is adequate for use in amyloid PET scan screenings.

Use of magnetic resonance structural imaging to identify disease progression in patients with mild cognitive impairment: A voxel-based morphometry and surface-based morphometry study

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) based on magnetic resonance structural imaging were used to identify disease progression in mild cognitive impairment (MCI) patients. A retrospective analysis was conducted on 154 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, with 62 patients classified into the progressive MCI (pMCI) group and 92 patients into the stable MCI (sMCI) group. VBM and SBM were employed to identify structural differences between sMCI and pMCI patients, and differential features were extracted for model construction. The logistic regression method was used to establish relevant index models, and the DeLong test was used to compare the diagnostic performance of the different models. Additionally, 51 patients from the National Alzheimer's Coordinating Center (NACC) database were used as an external validation set to further validate the clinical efficacy of the model. Significant structural differences between pMCI and sMCI patients were revealed through VBM and SBM analyses. Volume reductions were observed in the frontal and temporal lobes, and cortical thinning occurred in the left inferior and superior parietal cortices. Reduced gyrification was observed in the bilateral insular gyrus. The structural joint model, which combines volume and cortical indices, demonstrated higher diagnostic accuracy compared to the joint scale index model that combines the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MOCA) indices. The findings indicate that combined VBM and SBM analysis offers a sensitive and noninvasive approach to detect structural biomarkers of MCI progression, providing a practical tool for early risk stratification and personalized clinical management.

Probing the Neurodynamic Mechanisms of Cognitive Flexibility in Depressed Individuals with Autism Spectrum Disorder

Introduction: Autism spectrum disorder (ASD) is characterized by deficits in social behavior and executive function (EF), particularly in cognitive flexibility. Whether transcranial magnetic stimulation (TMS) can improve cognitive outcomes in patients with ASD remains an open question. We examined the acute effects of prefrontal TMS on cortical excitability and fluid cognition in individuals with ASD who underwent TMS for refractory major depression. Methods: We analyzed data from an open-label pilot study involving nine participants with ASD and treatment-resistant depression who received 30 sessions of accelerated theta burst stimulation of the dorsolateral prefrontal cortex, either unilaterally or bilaterally. Electroencephalography data were collected at baseline and 1, 4, and 12-weeks posttreatment and analyzed using a mixed-effects linear model to assess changes in regional cortical excitability using three models of spectral parametrization. Fluid cognition was measured using the National Institutes of Health Toolbox Cognitive Battery. Results: Prefrontal TMS led to a decrease in prefrontal cortical excitability and an increase in right temporoparietal excitability, as measured using spectral exponent analysis. This was associated with a significant improvement in the NIH Toolbox Fluid Cognition Composite score and the Dimensional Change Card Sort subtest from baseline to 12 weeks posttreatment (t = 3.79, p = 0.005, n = 9). Improvement in depressive symptomatology was significant (HDRS-17, F (3, 21) = 28.49, p < 0.001) and there was a significant correlation between cognitive improvement at week 4 and improvement in depression at week 12 (r = 0.71, p = 0.05). Conclusion: These findings link reduced prefrontal excitability in patients with ASD and improvements in cognitive flexibility. The degree to which these mechanisms can be generalized to ASD populations without Major Depressive Disorder remains a compelling question for future research.

Facilitating cognitive neuroscience research with 80-sensor optically pumped magnetometer magnetoencephalography (OPM-MEG)

Recent advancements in optically pumped magnetometer magnetoencephalography (OPM-MEG) make it a promising alternative to conventional SQUID-MEG systems. Nonetheless, as reported in the literature, current OPM-MEG systems are often constrained by a limited number of sampling points, which restricts their capability to match the full-head coverage offered by SQUID-MEG systems. Additionally, whether OPM-MEG can deliver results comparable to SQUID-MEG in practical cognitive neuroscience applications remains largely unexplored. In this study, we introduce a high-density, full-head coverage OPM-MEG system with 80 sensors and systematically compare the performance of OPM-MEG and SQUID-MEG, from sensor- to source-level analysis, across various classic cognitive tasks. Our results demonstrate that visual and auditory evoked fields captured using OPM-MEG align closely with those obtained from SQUID-MEG. Furthermore, steady-state visual evoked field and finger-tapping-induced beta power change recorded with OPM-MEG are accurately localized to corresponding brain regions, with activation centers highly congruent to those observed with SQUID-MEG. For resting-state recordings, the two modalities exhibit similar power distributions, functional connectomes, and microstate clusters. These findings indicate that the 80-sensor OPM-MEG system provides spatial and temporal characteristics comparable to those of traditional SQUID-MEG. Thus, our study offers empirical evidence supporting the efficacy of high-density OPM-MEG and suggests that OPM-MEG, with dense sampling capability, represents a compelling alternative to conventional SQUID-MEG, facilitating further exploration of human cognition.

Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments

INTRODUCTION

Characterizing transitions of cognitive state, including reversion from mild cognitive impairment (MCI) to normal cognition (NC) and subsequent cognitive stability or deterioration for post-reversion, has so far remained limited.

METHODS

Using a retrospective cohort of subjects with an MCI diagnosis at study entry and at least two follow-up visits between 2005 and December 2022, we developed a functional multistate model framework to estimate longitudinal patterns of transition probabilities between different cognitive states.

RESULTS

The probability of reversion increased from 2% at baseline to a maximum of 8% by year 10 before gradual decline thereafter. For post-reversion, the probability of progression to MCI rose from 8% to 35.71% at Year 10 and subsequently stabilized.

DISCUSSION

The instantaneous risk of MCI progressing was similar to the risk of re-progression to MCI for post-reversion. Post-reversion subjects remained at an increased risk of cognitive deterioration.

HIGHLIGHTS

The instantaneous risk of MCI progressing to AD is similar to the risk of re-progression to MCI for post-reversion. The FMSM we developed effectively utilizes multiple longitudinal markers to reveal variable transition patterns between different cognitive states. Considering both spatiotemporal dimensions and sparse irregularities from longitudinal neuroimaging and neuropsychological scales, fMLFPCA and MVFPCA help to extract variation patterns, to capture detailed changes characterizing the multidimensional evolution patterns of MCI.

Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data

The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Thanks to the valuable insights into the brain's anatomy, existing deep learning frameworks focused on forecasting the brain evolution trajectory from a single baseline observation. While yielding remarkable results, they suffer from three major limitations. First, they lack the ability to generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or connectivity type (e.g., T1-w MRI). Second, existing models require extensive training datasets to achieve satisfactory performance which are often challenging to obtain. Third, they do not efficiently utilize incomplete time series data. To address these limitations, we introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets. As a result, we enhance the performance of each hospital's local generative model, while preserving data privacy. The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary K-Nearest Neighbours based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations. Our comprehensive experimental results showed the outperformance of FedGmTE-Net++ in brain multi-trajectory prediction from a single baseline graph in comparison with benchmark methods. Our source code is available at https://github.com/basiralab/FedGmTE-Net-plus.

Using effective connectivity-based predictive modeling to predict MDD scale scores from multisite rs-fMRI data

BACKGROUND

Major depressive disorder (MDD) is a severe mental illness, and the Hamilton Depression Rating Scale (HAMD) is commonly used to quantify its severity. Our aim is to develop a predictive model for MDD symptoms using machine learning techniques based on effective connectivity (EC) from resting-state functional magnetic resonance imaging (rs-fMRI).

NEW METHOD

We obtained large-scale rs-fMRI data and HAMD scores from the multi-site REST-meta-MDD dataset. Average time series were extracted using different atlases. Brain EC features were computed using Granger causality analysis based on symbolic path coefficients, and a machine learning model based on EC was constructed to predict HAMD scores. Finally, the most predictive features were identified and visualized.

RESULTS

Experimental results indicate that different brain atlases significantly impact predictive performance, with the Dosenbach atlas performing best. EC-based models outperformed functional connectivity, achieving the best predictive accuracy (r = 0.81, p < 0.001, Root Mean Squared Error=3.55). Among various machine learning methods, support vector regression demonstrated superior performance.

COMPARISON WITH EXISTING METHODS

Current phenotype score prediction primarily relies on FC, which cannot indicate the direction of information flow within brain networks. Our method is based on EC, which contains more comprehensive brain network information and has been validated on large-scale multi-site data.

CONCLUSIONS

Brain network connectivity features effectively predict HAMD scores in MDD patients. The identified EC feature network may serve as a biomarker for predicting symptom severity. Our work may provide clinically significant insights for the early diagnosis of MDD, thereby facilitating the development of personalized diagnostic tools and therapeutic interventions.

Early life adversity is associated with greater similarity in neural representations of ambiguous and threatening stimuli

Exposure to early life adversity (ELA) is hypothesized to sensitize threat-responsive neural circuitry. This may lead individuals to overestimate threat in the face of ambiguity, a cognitive-behavioral phenotype linked to poor mental health. The tendency to process ambiguity as threatening may stem from difficulty distinguishing between ambiguous and threatening stimuli. However, it is unknown how exposure to ELA relates to neural representations of ambiguous and threatening stimuli, or how processing of ambiguity following ELA relates to psychosocial functioning. The current fMRI study examined multivariate representations of threatening and ambiguous social cues in 41 emerging adults (aged 18 to 19 years). Using representational similarity analysis, we assessed neural representations of ambiguous and threatening images within affective neural circuitry and tested whether similarity in these representations varied by ELA exposure. Greater exposure to ELA was associated with greater similarity in neural representations of ambiguous and threatening images. Moreover, individual differences in processing ambiguity related to global functioning, an association that varied as a function of ELA. By evidencing reduced neural differentiation between ambiguous and threatening cues in ELA-exposed emerging adults and linking behavioral responses to ambiguity to psychosocial wellbeing, these findings have important implications for future intervention work in at-risk, ELA-exposed populations.

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In addition, interactions between SC and FC within distributed association regions are related to alterations in cognition or neurological diseases, considering the inherent linkage between neural function and structure. However, there is a scarcity of existing learning-based methods that leverage both modality-specific characteristics and high-order interactions between the two modalities for regression or classification. Hence, this study proposes an interpretable modality-specific and interactive graph convolutional network (MS-Inter-GCN) that incorporates modality-specific information, reflecting the unique neural mechanism for each modality, and structure-function interactions, capturing the underlying foundation provided by white-matter fiber tracts for high-level brain function. In MS-Inter-GCN, we generate modality-specific task-relevant embeddings separately from both FC and SC using a graph convolutional encoder-decoder module. Subsequently, we learn the interactive weights between corresponding regions of FC and SC, reflecting the coupling strength, by employing an interactive module on the embeddings of both modalities. A novel graph structure is constructed, which uses modality-specific task-relevant embeddings and inserts the interactive weights as edges connecting corresponding regions of two modalities, and then is used for the regression or classification task. Finally, a post-hoc explainable technology - GNNExplainer- is used to identify salient regions and connections of each modality as well as salient interactions between FC and SC associated with tasks. We apply the proposed framework to fluid cognition prediction, Parkinson's disease (PD), Alzheimer's disease (AD), and schizophrenia (SZ) classification. Experimental results demonstrate that our method outperforms the other ten state-of-the-art methods on multi-modal brain features on all tasks. The GNNExplainer identifies salient structural and functional regions and connections for fluid cognition, PD, AD, and SZ. It confirms that strong structure-function coupling within the executive and control networks, combined with weak coupling within the motor network, is associated with fluid cognition. Moreover, structure-function decoupling in specific brain regions serves as a marker for different diseases: decoupling of the prefrontal, superior parietal, and superior occipital cortices is a marker of PD; decoupling of the middle frontal and lateral parietal cortices, temporal pole, and subcortical regions is indicative of AD; and decoupling of the prefrontal, parietal, and temporal cortices, as well as the cerebellum, contributes to SZ.

Investigating brain network dynamics in state-dependent stimulation: A concurrent electroencephalography and transcranial magnetic stimulation study using hidden Markov models

BACKGROUND

Systems neuroscience studies have shown that baseline brain activity can be categorized into large-scale networks (resting-state-networks, RNSs), with influence on cognitive abilities and clinical symptoms. These insights have guided millimeter-precise selection of brain stimulation targets based on RSNs. Concurrently, Transcranial Magnetic Stimulation (TMS) studies revealed that baseline brain states, measured by EEG signal power or phase, affect stimulation outcomes. However, EEG dynamics in these studies are mostly limited to single regions or channels, lacking the spatial resolution needed for accurate network-level characterization.

OBJECTIVE

We aim at mapping brain networks with high spatial and temporal precision and to assess whether the occurrence of specific network-level-states impact TMS outcome. To this end, we will identify large-scale brain networks and explore how their dynamics relates to corticospinal excitability.

METHODS

This study leverages Hidden Markov Models to identify large-scale brain states from pre-stimulus source space high-density-EEG data collected during TMS targeting the left primary motor cortex in twenty healthy subjects. The association between states and fMRI-defined RSNs was explored using the Yeo atlas, and the trial-by-trial relation between states and corticospinal excitability was examined.

RESULTS

We extracted fast-dynamic large-scale brain states with unique spatiotemporal and spectral features resembling major RSNs. The engagement of different networks significantly influences corticospinal excitability, with larger motor evoked potentials when baseline activity was dominated by the sensorimotor network.

CONCLUSIONS

These findings represent a step forward towards characterizing brain network in EEG-TMS with both high spatial and temporal resolution and underscore the importance of incorporating large-scale network dynamics into TMS experiments.

Brain-wide spatial mapping of oscillatory activity during naturalistic motor behavior

Understanding oscillatory neural activity associated with motor behavior is greatly contributing to the development of neuroprosthetic systems, robotic interfaces, and advanced neurorehabilitation techniques. Most current knowledge about movement-specific patterns of cortical activity is derived from laboratory experiments using highly standardized, repetitive, and often meaningless movements that are very distinct from natural motor behavior. This is characterized by frequent task switching, diverse kinematics, and endogenous motivation. Whether observed patterns of movement-related neural activity during standard laboratory tasks can be generalized to natural motor behavior is largely unknown. Here, we investigated the spatial, spectral, and temporal features of oscillatory neural activity associated with human motor control in a parkour of everyday movements. We replicated strong and significant decreases in the alpha/beta frequency range before movement onset and further show that this power decrease began about 2 s before movement initiation and reached a nadir around movement onset. In addition to the sustained event-related decrease in the alpha/beta range, we identified brief (4-5 cycles) increases in low-frequency activity (3-5 Hz) that either preceded or peaked at movement onset. These low-frequency increases exhibited much greater focality and lateralization compared with the wide-spread alpha/beta decrease. Together, our results provide a comprehensive account of brain rhythmic electric activity across spatial, spectral, and temporal scales in naturalistic motor behavior. Movement-preceding low-frequency activity has previously been identified as a promising brain stimulation target in patients with stroke. Detectability of low-frequency activity in naturalistic movements may enhance its utility as a target for on-demand brain stimulation in neurorehabilitation.NEW & NOTEWORTHY We here provide a comprehensive account of brain rhythmic electric activity across spatial, spectral, and temporal scales, associated with ecologically valid, freely chosen, auditory cued, and visually guided movements of either hand. New and noteworthy, the brain-wide topography of movement preceding, short-lasting increases in low-frequency activity (3-5 Hz), recently identified as a promising target for on-demand neurostimulation in stroke rehabilitation, is described and compared with classically studied sensorimotor rhythms.

Acute Alcohol-Induced Changes Measured With Metabotropic Glutamate Receptor 5 Positron Emission Tomography

BACKGROUND

Alcohol consumption at clinically relevant doses alters brain glutamate release. However, few techniques exist to measure these changes in humans. The metabotropic glutamate receptor 5 (mGluR5) PET radioligand [11C]ABP688 is sensitive to acute alcohol in rodents, possibly mediated by alcohol effects on glutamate release. This study aimed to determine the sensitivity of [11C]ABP688 PET to an acute alcohol challenge in humans.

METHODS

Eight social drinkers (25-42 years; 5 females) with a recent drinking occasion achieving a blood alcohol level (BAL) > 80 mg/dL were recruited. All participants underwent a 90-min dynamic baseline [11C]ABP688 PET scan. Two weeks later (range: 7-29 days), participants completed an oral laboratory alcohol challenge over 30 min, targeting a BAL of 60 mg/dL. Immediately after the challenge, a second [11C]ABP688 PET scan was performed. Non-displaceable binding potential (BPND; indicative of mGluR5 availability) and R1 (indicative of relative blood flow) were estimated using the simplified reference tissue model with the cerebellum as the reference region. Blood samples were taken throughout the scanning procedure to measure the BAL.

RESULTS

Seven participants (4 females) completed the study. The mean peak BAL achieved was 61 ± 18 mg/dL. Acute alcohol significantly decreased [11C]ABP688 BPND, F(1, 42) = 17.05, p < 0.001, Cohen's d = 0.32-0.60, and increased [11C]ABP688 R1, F(1, 42) = 6.67, p = 0.013, Cohen's d = 0.32-0.48, across brain regions. Exploratory analysis showed a positive relationship between alcohol-induced % change in [11C]ABP688 R1 in cortical regions and peak BAL (Spearman rho = 0.78 [frontal cortex] and 0.85 [temporal cortex] = 0.024 and 0.011).

CONCLUSIONS

This proof-of-concept study demonstrates that [11C]ABP688 PET imaging is sensitive to the effects of acute alcohol consumption. The observed decrease in mGluR5 availability aligns with preclinical data potentially indicating acute increased extracellular glutamate concentrations following ethanol dosing. This imaging tool could be useful for future investigations into the acute effects of alcohol on the brain during abstinence and withdrawal.

Multi-knowledge informed deep learning model for multi-point prediction of Alzheimer's disease progression

The diagnosis of Alzheimer's disease (AD) based on visual features-informed by clinical knowledge has achieved excellent results. Our study endeavors to present an innovative and detailed deep learning framework designed to accurately predict the progression of Alzheimer's disease. We propose Mul-KMPP, a Multi-Knowledge Informed Deep Learning Model for Multi-Point Prediction of AD progression, intended to facilitate precise assessments of AD progression in older adults. Firstly, we designed a dual-path methodology to capture global and local brain characteristics for visual feature extraction (utilizing MRIs). Then, we developed a diagnostic module before the prediction module, leveraging AAL (Anatomical Automatic Labeling) knowledge. Following this, predictions are informed by clinical insights. For this purpose, we devised a new composite loss function, including diagnosis loss, prediction loss, and consistency loss of the two modules. To validate our model, we compiled a dataset comprising 819 samples and the results demonstrate that our Mul-KMPP model achieved an accuracy of 86.8%, sensitivity of 86.1%, specificity of 92.1%, and area under the curve (AUC) of 95.9%, significantly outperforming several competing diagnostic methods at every time point. The source code for our model is available at https://github.com/Camelus-to/Mul-KMPP.

Functional divisions of the left anterior and posterior temporoparietal junction for phonological and semantic processing in Chinese character reading

Previous studies have shown that the left temporoparietal junction (TPJ) plays a critical role in word reading. Nevertheless, there is still controversy surrounding the phonological and semantic functions of the left TPJ. The parietal unified connectivity-biased computation (PUCC) model posits that the function of the left TPJ depends on both the neurocomputation of this local area and its long-range connectivity. To clarify the specific roles of different TPJ subregions in phonological and semantic processing of Chinese characters, the present study used connectivity-based clustering to identify seven subdivisions within the left TPJ, and conducted comprehensive analyses including functional and structural connectivity, univariate and multivariate analyses (i.e., representational similarity analysis, RSA) on multimodal imaging data (task-state fMRI, resting-state fMRI, and diffusion-weighted imaging [DWI]). Functional and structural connectivity analyses revealed that the left anterior TPJ had stronger connections with the phonological network, while the left posterior TPJ had stronger connections with the semantic network. RSA revealed that the left anterior and posterior TPJ represented phonological and semantic information of Chinese characters, respectively. More importantly, the phonological and semantic representations of the left TPJ were respectively correlated with its functional connectivity to the phonological and semantic networks. Altogether, our results provide a more elaborate perspective on the functional dissociation of the left anterior and posterior TPJ in phonological and semantic processing of Chinese characters, and support the PUCC model.

Multi-Spatial Voxel-Scale Modulation of Acupuncture on Abnormal Brain Activity in Migraine Patients Without Aura: A Randomized Study Neuroimaging Trial

BACKGROUND

The role of acupuncture in treating migraine has been widely recognized, but the systematic, comprehensive and, multi-spatial voxel-scale mechanism of brain function changes is still unclear.

OBJECTIVE

Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate the modulatory effect of acupuncture on brain activity in patients with migraine without aura (MwoA) at different spatial voxel scales.

METHODS

A total of 64 patients with MwoA were randomized into true acupuncture (TA) and sham acupuncture (SA) groups. MwoA patients received TA or SA three times a week for four weeks, a total of 12 sessions. A clinical symptoms assessment and rs-fMRI scans were evaluated before and after four weeks of treatment. Amplitude of low frequency fluctuation (ALFF) and fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were used to evaluate the spontaneous activity, activity coherence and connectivity importance of brain function at the single voxel, local voxel, and global voxel scales, respectively.

RESULTS

The clinical symptoms of both groups were improved compared with baseline. There were significant differences between the TA group and the SA group in migraine frequency, days and pain intensity. The neuroimaging data suggest that TA modulates a broader and more significant brain neural activity than SA. TA modulates the neural activity of the default mode network (DMN), visual network (VN), and sensorimotor network (SMN) at the single voxel scale, local voxel scale and global voxel scale, and these changes are correlated with the improvement of the migraine and quality of life.

CONCLUSIONS

TA could exert therapeutic effects at different spatial voxel scales by modulating the DMN, VN, and SMN, which may be the neuroimaging mechanism of acupuncture for MwoA.

Brain imaging-to-graph generation using adversarial hierarchical diffusion models for MCI causality analysis

Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current methods utilize software toolkits to extract empirical features from brain imaging to estimate effective connectivity. These methods heavily rely on manual parameter settings and may result in large errors during effective connectivity estimation. In this paper, a novel brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis. The proposed BIGG framework is based on the diffusion denoising probabilistic models (DDPM), where each denoising step is modeled as a generative adversarial network (GAN) to progressively translate the noise and conditional fMRI to effective connectivity. By introducing the diffusive factor, the denoising inference with a large sampling step size is more efficient and can maintain high-quality results. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model. The proposed model not only achieves superior prediction performance compared with other competing methods but also predicts MCI-related causal connections that are consistent with clinical studies.

Predicting cognitive decline: Deep-learning reveals subtle brain changes in pre-MCI stage

BACKGROUND

Mild cognitive impairment (MCI) and preclinical MCI (e.g., subjective cognitive decline, SCD) are considered risk states of dementia, such as Alzheimer's Disease (AD). However, it is challenging to accurately predict conversion from normal cognition (NC) to MCI, which is important for early detection and intervention. Since neuropathological changes may have occurred in the brain many years before clinical AD, we sought to detect the subtle brain changes in the pre-MCI stage using a deep-learning method based on structural Magnetic Resonance Imaging (MRI).

OBJECTIVES

To discover early structural neuroimaging changes that differentiate between stable and progressive cognitive status, and to establish a predictive model for MCI conversion.

DESIGN, SETTING AND PARTICIPANTS

We first created a unique deep-learning framework for pre-AD conversion prediction through the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) database (n = 845). Then, we tested the model on ADNI-2 (n = 321, followed 3 years) and our private study (n = 109), the China Longitudinal Aging Study (CLAS), to validate the rationality for pre-MCI conversion prediction. The CLAS is a 7-year community-based cohort study in Shanghai. Our framework consisted of two steps: 1) a single-ROI-based network (SRNet) for identifying informative regions in the brain, and 2) a multi-ROI-based network (MRNet) for pre-AD conversion prediction. We then utilized these "ROI-based deep learning" neural networks to create a composite score using advanced algorithm-building. We coined this score as the Progressive Index (PI), which serves as a metric for assessing the propensity of AD conversion. Ultimately, we employed the PI to gauge its predictive capability for MCI conversion in both ADNI-2 and CLAS datasets.

MEASUREMENTS

We primarily utilized baseline T1-weighted MRI scans to identify the most discriminative brain regions and subsequently developed the PI in both training and validation datasets. We compared the PI across different cognitive groups and conducted logistic regression models along with their AUCs, adjusting for education level, gender, neuropsychological test scores, and the presence of comorbid conditions.

RESULTS

We trained the SRNet and MRNet using 845 subjects from ADNI-1 with baseline MRI data, in which AD and progressive MCI (converting to AD within 3 years) patients were considered as positive samples, while NC and stable MCI (remaining stable for 3 years) subjects were considered as negative samples. The convolutional neural networks identified the top 10 regions of interest (ROIs) for distinguishing progressive from stable cases. These key brain regions included the hippocampus, amygdala, temporal lobe, insula, and anterior cerebellum. A total of 321 subjects from ADNI-2, including 209 NC (18 progressive NC (pNC), 113 stable NC (sNC), and 78 remaining NC (rNC)) and 112 SCD (11 pSCD, 5 sSCD, and 96 rSCD), as well as 109 subjects from CLAS, including 17 sNC, 16 pNC, 52 sSCD and 24 pSCD participated in the test set, separately. We found that the PI score effectively sorted all subjects by their stages (stable vs progressive). Furthermore, the PI score demonstrated excellent discrimination between the two outcomes in the CLAS data(p<0.001), even after controlling for age, gender, education level, depression symptoms, anxiety symptoms, somatic diseases, and baseline MoCA score. Better performance for prediction progression to MCI in CLAS was obtained when the PI score was combined with clinical measures (AUC=0.812; 95 %CI: 0.725-0.900).

CONCLUSIONS

This study effectively predicted the progression to MCI among order individuals at normal cognition state by deep learning algorithm with MRI scans. Exploring the key brain alterations during the very early stages, specifically the transition from NC to MCI, based on deep learning methods holds significant potential for further research and contributes to a deeper understanding of disease mechanisms.

Regional skull translocator protein elevation in autistic adults detected by PET-MRI

Immune processes have been implicated in the pathophysiology of autism spectrum disorder (ASD). Brain borders, such as the skull, have recently been highlighted as sites where neuro-immune interactions occur with key consequences for brain immunity. Translocator protein (TSPO), a mitochondrial protein involved in immune functions, was measured in the skull using [11C]PBR28 positron emission tomography-magnetic resonance imaging (PET-MRI) in 38 autistic adults (26 males, 12 females) and 29 age-and sex-matched healthy controls (19 males, 10 females). [11C]PBR28 uptake relative to a pseudo-reference region assessed using standardized uptake value ratio (SUVR) revealed elevated TSPO in autistic adults in frontal and temporal skull. We did not observe an association between [11C]PBR28 uptake in total or regional skull areas and autism symptom severity. C-reactive protein levels were positively associated with [11C]PBR28 uptake in the total skull across participants. Lastly, [11C]PBR28 uptake in the total skull was stable across a 4-month period. This work indicates regional TSPO elevations in the skull in autistic adults, which may suggest immune involvement.

Relationship between use of anti-platelet agents, oral anti-coagulants, and Aβ burden with cerebral microhemorrhages in cognitively asymptomatic adults

INTRODUCTION

Cerebral microhemorrhages (CMHs) are detectable by magnetic resonance imaging (MRI). CMHs in deep brain regions are linked to hypertensive vasculopathy, while those in lobar regions with amyloid beta (Aβ) deposition in blood vessels. This study aims to determine the association between anti-thrombotic treatment and CMH prevalence among cognitively asymptomatic adults, and to assess the role of Aβ markers, apolipoprotein E (APOE) ε4 carrier status, and cardiovascular risk factors in CMH development.

METHODS

Using baseline data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies, we examined CMH presence via 3T MRI, along with medication use, APOE ε4 carrier status, medical history, and blood pressure.

RESULTS

Our analysis showed a significantly higher prevalence of CMHs in the A4 cohort (17.3%) compared to the LEARN cohort (2.6%).

DISCUSSION

Factors such as male sex, age, Aβ markers, and APOE ε4 status were significantly associated with higher CMH prevalence in the A4 cohort. However, anti-thrombotic treatment did not show association with overall CMHs.

HIGHLIGHTS

Male sex, age > 75, amyloid beta (Aβ) burden, and apolipoprotein E (APOE) ε4 homozygosity are significantly associated with higher prevalence of CMHs (cerebral microhemorrhages) in a cohort of cognitively asymptomatic individuals. Male sex, age > 75, Aβ burden, and APOE ε4 homozygosity are significantly associated with higher prevalence of lobar CMHs in a cohort of cognitively asymptomatic individuals. Anti-platelet or anti-coagulant usage were not associated with an increased prevalence of CMHs in either brain location or overall, in a cohort of cognitively asymptomatic individuals. History of a lipid disorder is associated with a higher prevalence of lobar CMHs in a cohort of cognitively asymptomatic individuals.

The temporal-insula type of temporal plus epilepsy patients with different postoperative seizure outcomes have different cerebral blood flow patterns

PURPOSE

This study retrospectively analyzed preoperative arterial spin labeling (ASL) perfusion MRI data of patients with the temporal-insula type of temporal plus epilepsy (TI-TPE). We aimed to investigate the differences in presurgical cerebral blood flow (CBF) changes in TI-TPE patients with different surgical outcomes.

METHOD

A total of 48 TI-TPE patients confirmed by SEEG were meticulously reviewed for this study. Patients were divided into the seizure-free (SF) group (Engel IA) and the non-seizure-free (NSF) group (Engel IB to IV) according to the Engel seizure classification. The 3D-ASL data of all patients before surgery were analyzed using statistical parametric mapping (SPM) and graph theory analysis. These findings were then compared to healthy controls (HC) based on whole-brain voxel-level analysis and covariance network analysis.

RESULT

At the voxel-level, both SF and NSF groups showed significantly decreased CBF in the ipsilateral transverse temporal gyrus and insula (TTG/insula), contralateral middle cingulate gyrus, precuneus (MCG/precuneus), and increased CBF in the ipsilateral superior temporal gyrus and the superior temporal pole (STG/STP). Wherein the SF group showed more lower CBF in the contralateral MCG/precuneus, with unique increased CBF in the contralateral STG/insula and decreased CBF in the contralateral calcarine as well. In terms of network attributes, the NSF group showed a significantly higher clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), shorter shortest path length (Lp), and more extensive abnormal nodes compared to the SF and HC groups. While the SF group has higher synchronicity than the HC group.

CONCLUSION

Both SF and NSF groups had abnormal CBF changes at the voxel and network levels with different patterns. The SF group showed more obvious regional CBF changes, while the NSF group showed more extended network disruption, which might underlie different seizure outcomes after local surgical resection.

The generalizability of cortical area parcellations across early childhood

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity (FC) gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical areas across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fits the underlying FC in individuals during the first 3 years, adult area parcellations still have utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the area parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

Multi-Metric Approach for the Comparison of Denoising Techniques for Resting-State fMRI

Despite the increasing use of resting-state functional magnetic resonance imaging (rs-fMRI) data for studying the spontaneous functional interactions within the brain, the achievement of robust results is often hampered by insufficient data quality and by poor knowledge of the most effective denoising methods. The present study aims to define an appropriate denoising strategy for rs-fMRI data by proposing a robust framework for the quantitative and comprehensive comparison of the performance of multiple pipelines made available by the newly proposed HALFpipe software. This will ultimately contribute to standardizing rs-fMRI preprocessing and denoising steps. Fifty-three participants took part in the study by undergoing a rs-fMRI session. Synthetic rs-fMRI data from one subject were also generated. Nine different denoising pipelines were applied in parallel to the minimally preprocessed fMRI data. The comparison was conducted by computing previously proposed and novel metrics that quantify the degree of artifact removal, signal enhancement, and resting-state network identifiability. A summary performance index, accounting for both noise removal and information preservation, was proposed. The results confirm the performance heterogeneity of different denoising pipelines across the different quality metrics. In both real and synthetic data, the summary performance index favored the denoising strategy including the regression of mean signals from white matter and cerebrospinal fluid brain areas and global signal. This pipeline resulted in the best compromise between artifact removal and preservation of the information on resting-state networks. Our study provided useful methodological tools and key information on the effectiveness of multiple denoising strategies for rs-fMRI data. Besides providing a robust comparison approach that could be adapted to other fMRI studies, a suitable denoising pipeline for rs-fMRI data was identified, which could be used to improve the reproducibility of rs-fMRI findings.

Neural correlates of semantic and phonemic variants of verbal fluency tasks: A combined MEG and fMRI study

OBJECTIVE

The neural correlates of verbal fluency tasks (VFT) have been characterized by functional magnetic resonance imaging (fMRI). Still, the spatio-spectral neural oscillatory dynamics elicited by VFT and the differences between their semantic and phonologic variants are unsettled. We investigate, using fMRI and magnetoencephalography (MEG), the neural correlates of VFT and the differences in neural oscillatory dynamics between phonological (PFT) and semantic (SFT) fluency tasks.

METHODS

Thirty right-handed healthy adults underwent MEG and fMRI recordings while performing covert PFT and SFT.

RESULTS

fMRI showed different neural networks for PFT (left-dominant lexical-semantic control network) and SFT (nodes of the left-dominant semantic network). MEG showed beta-band power suppression in the left operculum in both VFT, with no difference between PFT and SFT.

CONCLUSIONS

MEG and fMRI detect distinct task-induced neural activity changes during VFT. MEG findings likely reflect the neural consequences of covert word production initiated at the inferior/middle frontal gyri, as identified by fMRI.

SIGNIFICANCE

This study demonstrates the added value of combining MEG and fMRI to fully characterize VFT network dynamics. It paves the way for the use of VFT for non-invasive presurgical language mapping using a method free of neurovascular uncoupling.

Neural Associations between Inhibitory Control and Counterintuitive Reasoning in Science and Maths in Primary School Children

Emerging evidence suggests that inhibitory control (IC) plays a pivotal role in science and maths counterintuitive reasoning by suppressing incorrect intuitive concepts, allowing correct counterintuitive concepts to come to mind. Neuroimaging studies have shown greater activation in the ventrolateral and dorsolateral pFCs when adults and adolescents reason about counterintuitive concepts, which has been interpreted as reflecting IC recruitment. However, the extent to which neural systems underlying IC support science and maths reasoning remains unexplored in children. This developmental stage is of particular importance, as many crucial counterintuitive concepts are learned in formal education in middle childhood. To address this gap, fMRI data were collected while fifty-six 7- to 10-year-olds completed counterintuitive science and math problems, plus IC tasks of interference control (Animal Size Stroop) and response inhibition (go/no-go). Univariate analysis showed large regional overlap in activation between counterintuitive reasoning and interference control, with more limited activation observed in the response inhibition task. Multivariate similarity analysis, which explores fine-scale patterns of activation across voxels, revealed neural activation similarities between (i) science and maths counterintuitive reasoning and interference control tasks in frontal, parietal, and temporal regions, and (ii) maths reasoning and response inhibition tasks in the precuneus/superior parietal lobule. Extending previous research in adults and adolescents, this evidence is consistent with the proposal that IC, specifically interference control, supports children's science and maths counterintuitive reasoning, although further research will be needed to demonstrate the similarities observed do not reflect more general multidemand processes.

Differences in Glucose Metabolism Between Single Memory Domain and Multidomain Subjective Cognitive Decline: A Longitudinal Study From SILCODE

BACKGROUND

Glucose metabolism and plasma biomarkers have emerged as important early markers in Alzheimer's disease. Different subtypes (single memory domain, multidomain) of subjective cognitive decline (SCD) may represent distinct stages of disease progression, but the differences in glucose metabolism remain unclear. This study focused on exploring the differences in glucose metabolism between different SCD subtypes and the correlation with plasma biomarkers based on 18F-FDG PET.

METHODS

In this study, thirty-three normal controls (NCs), thirty-five individuals with single memory domain SCD (sd-SCD), thirty-nine individuals with multidomain SCD (md-SCD), and twenty-one cognitively impaired (CI) individuals were involved. We investigated the standardized uptake value ratio (SUVR) and voxel differences between the sd-SCD and md-SCD groups followed by FDR and GRF corrections, with an average follow-up time of 44.98 ± 16.49 months. Correlation analyses were employed to assess relationships between FDG-PET SUVR and neuropsychological scales as well as plasma biomarkers. Finally, Kaplan-Meier survival analysis was used to investigate the risk of cognitive decline conversion among SCD subgroups.

RESULTS

After controlling for the effects of covariates, the following brain regions showed voxel differences and lower SUVR in md-SCD groups, including right anterior cingulate and paracingulate gyri (ACG.R, p = 0.003), left anterior cingulate and paracingulate gyri (ACG.L, p = 0.003), right middle temporal gyrus (MTG.R, p = 0.004), and right inferior temporal gyrus (ITG.R, p = 0.001), compared to the sd-SCD group. SUVR of ACG.R was correlated with plasma Aβ42/40 (r = 0.435, p = 0.006) and AVLT-N7 score (r = 0.347, p = 0.031) in the md-SCD group while none of the correlations existed in the sd-SCD group. SUVR of MTG.R was also correlated with the AVLT-N7 score (r = 0.246, p = 0.035) across SCD individuals. The SCD individuals with positive plasma Aβ42/40, p-tau181, and glucose metabolism in above four regions, or those in the md-SCD group showed an elevated risk of cognitive conversion in comparison to the controls.

CONCLUSIONS

Differences in glucose metabolism could be observed between the md-SCD and sd-SCD groups. SCD participants in the md-SCD group, or those with positive biomarkers, might represent a higher risk of cognitive decline conversion.

Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification

PURPOSE

This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.

PATIENTS AND METHODS

A retrospective dataset of PET/CT and T1-weighted MRI pairs from 226 individuals (157 with mild cognitive impairment or dementia and 69 healthy controls) was used. The dataset was split into training/validation (60%) and test (40%) sets. Utilizing auto-generated segmentation labels, 3 UNets were independently trained for multiplanar brain parcellation on CT and subsequently ensembled. Amyloid load was measured across 46 volumes of interest (VOIs), derived from the Desikan-Killiany-Tourville atlas. Dice similarity coefficient between the proposed CT-based DL model and MRI-based (FreeSurfer) method was calculated, with SUVR comparison using linear regression analysis and intraclass correlation coefficient. Global SUVRs were also compared within groups with clinical dementia ratings (CDRs) of 0, 0.5, and 1.

RESULTS

The DL-based CT parcellation achieved mean Dice similarity coefficients of 0.80 for all 46 VOIs, 0.72 for 16 cortical and limbic VOIs, and 0.83 for 30 subcortical VOIs. For regional and global SUVR comparisons, the linear regression yielded a slope, y-intercept, and R2 of 1 ± 0.027, 0 ± 0.040, and ≧0.976, respectively ( P  < 0.001), and the intraclass correlation coefficient was ≧0.988 ( P  < 0.001). For global SUVRs in each CDR group, these values were 1 ± 0.020, 0 ± 0.026, ≧0.993, and ≧0.996, respectively ( P  < 0.001). Both MRI-based and CT-based global SUVR showed a consistent increase as the CDR score increased.

CONCLUSIONS

The DL-based CT parcellation agrees strongly with MRI-based methods for amyloid PET quantification.

Impact of non-cyanotic congenital heart disease on Children's brain studied by voxel-based morphometry: A case-control study

BACKGROUND

Considerable research has shown brain injury during surgery for patients with cyanotic congenital heart disease (CHD), but the preoperative neurodevelopment and brain injury in children with non-cyanotic CHD are not well understood. The aim of this study is to investigate changes in global and local grey matter (GM) volumes of pediatric patients with non-cyanotic CHD before catheter-based procedure using voxel-based morphometry (VBM).

METHODS

One-to three-year-old toddlers with acyanotic CHD (n = 54) hospitalized for treatment were prospectively enrolled. Each toddler underwent a 3D T1-weighted brain Magnetic Resonance Imaging (MRI) scan before catheter-based procedure. Meanwhile, 3D T1-weighted brain MR images of age- and sex-matched healthy controls (n = 35) were retrospectively analyzed. The volume of GM and total intracranial volume (TIV) were assessed by VBM within the SPM 12 (Statistical Parametric Mapping software), and regional differences in GM volume were analyzed by two-sample t-test and familywise error (FWE) rate correction.

RESULTS

There was no difference in gross GM volume and TIV between the two groups (p > 0.05), but VBM analysis showed reduced structures of GM in middle frontal gyrus (both sides), inferior frontal gyrus, orbital gyrus, subcallosal gyrus, thalamus (both sides), medial globus pallidus (both sides) and culmen (both sides) of the non-cyanotic CHD group compared with the controls (p < 0.05, FWE correction).

CONCLUSION

Toddlers aged 1-3 years with acyanotic CHD suffer a decrease in local GM volume before catheter-based procedure, which tends to be distributed across the bilateral frontal lobe, thalamus, globus pallidus, and cerebellum.

Prediction of longitudinal synaptic loss in Alzheimer's disease using tau PET and plasma biomarkers

INTRODUCTION

We investigated the associations of longitudinal synaptic loss and cognitive decline with tau burden and plasma biomarkers in Alzheimer's disease (AD).

METHODS

Twenty cognitively impaired (CI) individuals and 16 healthy controls (HC) underwent cognitive and plasma biomarker assessments, amyloid positron emission tomography (PET), tau PET, and synaptic density PET; after 1 year, tau and synaptic density PET were repeated. The relationships among tau burden, plasma biomarkers, synaptic density, and cognition were investigated.

RESULTS

The CI group had more longitudinal synapse loss and tau deposition than HCs. Longitudinal synaptic loss was positively associated with longitudinal cognitive decline, negatively with longitudinal tau deposition. Plasma glial fibrillary acidic protein (GFAP) mediates the relationship between longitudinal tau deposition and longitudinal synaptic loss. Tau burden, plasma phosphorylated tau181, and GFAP could predict longitudinal synaptic loss and cognitive decline.

CONCLUSIONS

The CI group had more longitudinal synapse loss and tau burden increases than HCs. Tau pathology and plasma GFAP could predict longitudinal synapse loss and cognitive decline.

HIGHLIGHTS

Cognitively impaired individuals had more longitudinal synapse loss in the medial temporal lobe, and increased tau burden in the widespread neocortex than healthy controls. The longitudinal change of synaptic density was negatively associated with the longitudinal change of tau burden, and positively associated with longitudinal cognitive decline. Plasma glial fibrillary acidic protein (GFAP) mediates the relationship between longitudinal tau deposition and longitudinal synaptic loss. Tau burden, plasma phosphorylated tau181, and GFAP could predict longitudinal synaptic loss and cognitive decline.

fMRI-based data-driven brain parcellation using independent component analysis

BACKGROUND

Studies using functional magnetic resonance imaging (fMRI) broadly require a method of parcellating the brain into regions of interest (ROIs). Parcellations can be based on standardized brain anatomy, such as the Montreal Neurological Institute's (MNI) 152 atlas, or an individual's functional activity patterns, such as the Personode software.

NEW METHOD

This work outlines and tests the independent component analysis (ICA)-based parcellation algorithm (IPA) when applied to a hypertension study (n = 48) that uses the independent components (ICs) output from group ICA (gICA) to build ROIs which are ideally spatially consistent and functionally homogeneous. After regression of ICs to all subjects, the IPA builds individualized parcellations while simultaneously obtaining a gICA-derived parcellation.

RESULTS

ROI spatial consistency quantified by dice similarity coefficients (DSCs) show individualized parcellations exhibit mean DSCs of 0.69 ± 0.14. Functional homogeneity, calculated as mean Pearson correlation value of all voxels comprising a ROI, shows individualized parcellations with a mean of 0.30 ± 0.14 and gICA-derived parcellations' mean of 0.38 ± 0.15.

COMPARISON WITH EXISTING METHOD(S)

Individualized Personode parcellations show decreased mean DSCs (0.43 ± 0.11) with the individualized parcellations, gICA-derived parcellations, and the MNI atlas having decreased homogeneity values of 0.28 ± 0.14, 0.31 ± 0.15, and 0.20 ± 0.11 respectively.

CONCLUSIONS

Results show that the IPA can more reliably define a ROI and does so with higher functional homogeneity. Given these findings, the IPA shows promise as a novel parcellation technique that could aid the analysis of fMRI data.

A Rapid Cortical Learning Process Supporting Students' Knowledge Construction During Real Classroom Teaching

Classroom teaching is essential for cognitive development and cultural evolution, yet its neurocognitive mechanisms remain unclear. Here, this is explored in a university graduate course by combining wearable functional near-infrared spectroscopy (fNIRS) and machine learning models. The results show that blended teaching involving both students' recalling and teachers' lecturing leads to better learning outcomes than lecturing alone. Moreover, during the same lecturing phase, blended teaching induces knowledge construction in the middle frontal cortex (MFC), while lecturing alone induces knowledge representation in the right temporoparietal junction (TPJ), with the former significantly correlating with the final learning outcomes. Additionally, the MFC's construction begins during earlier recalling but is significantly facilitated by later lecturing. Finally, when teacher's TPJ activity precedes that of students' MFC, significant teacher-student neural synchronization is observed during lecturing of blended teaching and is correlated with learning outcomes. These findings suggest that, in the real classroom teaching, the MFC serves as a hub of a rapid cortical learning process, supporting knowledge construction through a projection from the teacher's TPJ.

The cortical architecture representing the linguistic hierarchy of the conversational speech

Recent studies demonstrate that the brain parses natural language into smaller units represented in lower-order regions and larger units in higher-order regions. Most of these studies, however, have been conducted on unidirectional narrative speech, leaving the linguistic hierarchy and its cortical representation in bidirectional conversational speech unexplored. To address this gap, we simultaneously measured brain activity from two individuals using functional near-infrared spectroscopy (fNIRS) hyperscanning while they engaged in a naturalistic conversation. Using a Pre-trained Language Model (PLM) and Representational Similarity Analysis (RSA), we demonstrated that conversational speech, jointly produced by two interlocutors in a turn-taking manner, exhibits a linguistic hierarchy, characterized by a boundary effect between linguistic units and an incremental context effect. Furthermore, a gradient pattern of shared cortical representation of the linguistic hierarchy was identified at the dyadic rather than the individual level. Interpersonal neural synchronization (INS) in the left superior temporal cortex was associated with turn representation, whereas INS in the medial prefrontal cortex was linked to topic representation. These findings further validated the distinctiveness of linguistic units of different sizes. Together, our results provide original evidence for the linguistic hierarchy and the underlying cortical architecture during a naturalistic conversation, extending the hierarchical nature of natural language from unidirectional narrative speech to bidirectional conversational speech.

Pathological features of post-stroke pain: a comprehensive analysis for subtypes

Post-stroke pain is heterogeneous and includes both nociceptive and neuropathic pain. These subtypes can be comprehensively assessed using several clinical tools, such as pain-related questionnaires, quantitative somatosensory tests and brain imaging. In the present study, we conducted a comprehensive assessment of patients with central post-stroke pain and non-central post-stroke pain and analysed their clinical features. We also performed a detailed analysis of the relationships between brain lesion areas or structural disconnection of the white matter and somatosensory dysfunctions. In this multicentre cross-sectional study, 70 patients were divided into 24 with central post-stroke pain, 26 with non-central post-stroke pain and 20 with no-pain groups. Multiple logistic regression analysis was used to summarize the relationships between each pathological feature (for the central post-stroke pain and non-central post-stroke pain groups) and pain-related factors or the results of quantitative somatosensory tests. Relationships between somatosensory dysfunctions and brain lesion areas were analysed using voxel-based lesion-symptom mapping and voxel-based disconnection-symptom mapping. All pathology feature models indicated that central post-stroke pain was associated with cold hypoesthesia at 8°C (β = 2.98, odds ratio = 19.6, 95% confidence interval = 2.7-141.8), cold hyperalgesia at 8°C (β = 2.61, odds ratio = 13.6, 95% confidence interval = 1.13-163.12) and higher Neuropathic Pain Symptom Inventory scores (for spontaneous and evoked pain items only; β = 0.17, odds ratio = 1.19, 95%, confidence interval = 1.07-1.32), whereas non-central post-stroke pain was associated with joint pain (β = 5.01, odds ratio = 149.854, 95%, confidence interval = 19.93-1126.52) and lower Neuropathic Pain Symptom Inventory scores (β = -0.17, odds ratio = 0.8, 95%, confidence interval = 0.75-0.94). In the voxel-based lesion-symptom mapping, the extracted lesion areas indicated mainly voxels significantly associated with cold hyperalgesia, allodynia at 8°C and 22°C and heat hypoesthesia at 45°C. These extracted areas were mainly in the putamen, insular cortex, hippocampus, Rolandic operculum, retrolenticular part of internal and external capsules and sagittal stratum. In voxel-based disconnection-symptom mapping, the extracted disconnection maps were significantly associated with cold hyperalgesia at 8°C, and heat hypoesthesia at 37°C and 45°C. These structural disconnection patterns were mainly in the cingulum frontal parahippocampal tract, the reticulospinal tract and the superior longitudinal fasciculus with a widespread interhemispheric disconnection of the corpus callosum. These findings serve as important indicators to facilitate decision-making and optimize precision treatments through data dimensionality reduction when diagnosing post-stroke pain using clinical assessments, such as bedside quantitative sensory testing, pain-related factors, pain questionnaires and brain imaging.

A deep neural network for adaptive spatial smoothing of task fMRI data

Over the past decade, functional magnetic resonance imaging (fMRI) has emerged as a widely adopted in vivo imaging technique for examining neural activity in the brain. A common preprocessing step in fMRI analysis is spatial smoothing, which helps in detecting cluster-like active regions. The use of a heuristically selected Gaussian filter for spatial smoothing is frequently preferred due to its simplicity and computational efficiency. Neurons in the cerebral cortex are located within a thin sheet of gray matter at the surface of the brain, and the human brain's gyrification results in a complex gray matter anatomy. For task-based fMRI activation analysis, isotropic Gaussian smoothing can reduce spatial specificity, introducing spatial blurring artifacts where inactive voxels near active regions are mistakenly identified as active. This blurring is beneficial for group-level analysis as it helps mitigate anatomical variability across subjects and inaccuracies in spatial normalization. However, it poses challenges in subject-level analysis, particularly in clinical applications such as presurgical planning and fMRI fingerprinting, which demand high spatial specificity. Previous studies have proposed several adaptive spatial smoothing techniques to address these issues. In this study, we introduce a versatile deep neural network (DNN) that builds on the strengths of previous approaches while overcoming their limitations. This method can incorporate additional neighboring voxels for estimating optimal spatial smoothing without significantly increasing computational costs, making it suitable for ultrahigh-resolution (sub-millimeter) task fMRI data. Furthermore, the proposed neural network incorporates brain tissue properties, enabling more accurate characterization of brain activation at the individual level.

Post-stroke lesion correlates of errors in verbal and spatial production tasks

Introduction

Traditional lateralization models assign post-stroke verbal impairments to the left hemisphere and spatial impairments to the right hemisphere. When considering error measures, this dichotomy may be too simplistic, as performance monitoring may involve domain-general and domain-specific components. Furthermore, the error-monitoring hypothesis predicts domain-incongruent specialization, with left hemisphere dominance for spatial and right hemisphere dominance for verbal errors.

Methods

We performed voxel-based lesion-behavior mapping in N = 110 acute stroke patients who completed a cognitively demanding, error-prone, five-point spatial design fluency task and a verbal word-fragment completion task.

Results

Significant associations were found between lesion location and error rates in both tasks, spatial fluency (correlation = 0.36, p < 0.001) and verbal completion (correlation = 0.31, p = 0.001). Right inferior frontal lesions correlated with errors in both tasks. In addition, left frontal white matter (WM) lesions were associated with spatial errors, whereas right frontal WM lesions were associated with verbal errors. After adjusting for demographics, the left WM cluster remained significant for spatial errors and the right WM cluster for verbal errors, while the right inferior frontal association with spatial errors was no longer significant.

Discussion

Post-stroke performance monitoring involves two distinct neural systems. One is a domain-general system, probably centered in the right inferior frontal region, that supports overall accuracy. The other is a widely distributed, reverse lateralized system, with left lesions associated with spatial accuracy and right lesions associated with verbal accuracy. This suggests that performance monitoring relies on more complex hemispheric interactions than traditional models suggest.