Decoding subject-driven cognitive states with whole-brain connectivity patterns

Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia

This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.

Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents

It is well accepted that the brain is functionally organized into multiple networks and extensive literature has demonstrated that the organization of these networks shows major changes during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically-developing adolescents, which is problematic as the reliable identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from 1391 typically-developing youth aged 8-17 years to create an adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm, we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity. Sex effects were not as widespread. We created a novel brain atlas, named Dev-Atlas, focused on a typically-developing sample, with the hope that this atlas can be used in future developmental neuroscience studies.

Risk Assessment of Maladaptive Behaviors in Adolescents: Nutrition, Screen Time, Prenatal Exposure, Childhood Adversities - Adolescent Brain Cognitive Development Study

PURPOSE

We aimed to identify significant contributing factors to the risk of maladaptive behaviors, such as alcohol use disorder or obesity, in children. To achieve this, we utilized the extensive adolescent brain cognitive development data set, which encompasses a wide range of environmental, social, and nutritional factors.

METHODS

We divided our sample into equal sets (test, validation; n = 3,415 each). On exploratory factor analysis, six factor domains were identified as most significant (fat/sugar intake, screen time, and prenatal alcohol exposure, parental aggressiveness, hyperactivity, family violence, parental education, and family income) and used to stratify the children into low- (n = 975), medium- (n = 967), high- (n = 977) risk groups. Regression models were used to analyze the relationship between identified risk groups, and differences in reward sensitivity, and behavioral problems at 2-year follow-up.

RESULTS

The functional magnetic resonance imaging analyses showed reduced activation in several brain regions during reward or loss anticipation in high/medium-risk (vs. low-risk) children on a monetary incentive delay task. High-risk children exhibited heightened middle frontal cortex activity when receiving large rewards. They also displayed increased impulsive and motivated reward-seeking behaviors, along with behavioral problems. These findings replicated in our validation set, and a negative correlation between middle frontal cortexthickness and impulsivity behavior was observed in high-risk children.

DISCUSSION

Our findings show altered reward function and increased impulsiveness in high-risk adolescents. This study has implications for early risk identification and the development of prevention strategies for maladaptive behaviors in children, particularly those at high risk.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Covariance-based decoding reveals a category-specific functional connectivity network for imagined visual objects

The coordination of different brain regions is required for the visual imagery of complex objects (e.g., faces and places). Short-range connectivity within sensory areas is necessary to construct the mental image. Long-range connectivity between control and sensory areas is necessary to re-instantiate and maintain the mental image. While dynamic changes in functional connectivity are expected during visual imagery, it is unclear whether a category-specific network exists in which the strength and the spatial destination of the connections vary depending on the imagery target. In this magnetoencephalography study, we used a minimally constrained experimental paradigm wherein imagery categories were prompted using visual word cues only, and we decoded face versus place imagery based on their underlying functional connectivity patterns as estimated from the spatial covariance across brain regions. A subnetwork analysis further disentangled the contribution of different connections. The results show that face and place imagery can be decoded from both short-range and long-range connections. Overall, the results show that imagined object categories can be distinguished based on functional connectivity patterns observed in a category-specific network. Notably, functional connectivity estimates rely on purely endogenous brain signals suggesting that an external reference is not necessary to elicit such category-specific network dynamics.

The role of sex in the association between cannabis use disorder and resting-state functional connectivity

While Cannabis use disorder (CUD) is twice as prevalent in males, females transition more quickly from heavy use to CUD and experience more severe withdrawal. These clinically relevant sex differences contrast the lack of knowledge about the underlying brain mechanisms. This study investigated the relationship between CUD and resting-state functional brain connectivity (RSFC), assessing potential sex differences herein. RSFC of the Salience Network (SN), Basal Ganglia Network (BGN), Executive Control Network (ECN), and Default Mode Network (DMN) was compared between 152 individuals (76 males) with CUD and 114 matched controls (47 males). Within the CUD group, relationships between RSFC and heaviness of cannabis use, age of onset, and CUD symptom severity, along with their associations with sex, were investigated. CUD and control groups showed similar RSFC across all networks, regardless of sex. In the CUD group, heavier cannabis use correlated with higher RSFC across all networks and earlier age of onset was related to higher RSFC in the anterior SN, BGN, left ECN, and dorsal DMN. These associations were similar for males and females. CUD severity was related to higher RSFC in the anterior SN, which was moderated by sex, with a positive association seen only in males. In conclusion, CUD may not necessarily be associated with altered RSFC. Individual use characteristics such age of onset and severity of use may determine the potential impact of cannabis use on RSFC in a largely similar way in males and females.

Spatial (mis)match between EEG and fMRI signal patterns revealed by spatio-spectral source-space EEG decomposition

This study aimed to directly compare electroencephalography (EEG) whole-brain patterns of neural dynamics with concurrently measured fMRI BOLD data. To achieve this, we aim to derive EEG patterns based on a spatio-spectral decomposition of band-limited EEG power in the source-reconstructed space. In a large dataset of 72 subjects undergoing resting-state hdEEG-fMRI, we demonstrated that the proposed approach is reliable in terms of both the extracted patterns as well as their spatial BOLD signatures. The five most robust EEG spatio-spectral patterns not only include the well-known occipital alpha power dynamics, ensuring consistency with established findings, but also reveal additional patterns, uncovering new insights into brain activity. We report and interpret the most reproducible source-space EEG-fMRI patterns, along with the corresponding EEG electrode-space patterns, which are better known from the literature. The EEG spatio-spectral patterns show weak, yet statistically significant spatial similarity to their functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signatures, particularly in the patterns that exhibit stronger temporal synchronization with BOLD. However, we did not observe a statistically significant relationship between the EEG spatio-spectral patterns and the classical fMRI BOLD resting-state networks (as identified through independent component analysis), tested as the similarity between their temporal synchronization and spatial overlap. This provides evidence that both EEG (frequency-specific) power and the BOLD signal capture reproducible spatio-temporal patterns of neural dynamics. Instead of being mutually redundant, these only partially overlap, providing largely complementary information regarding the underlying low-frequency dynamics.

The underlying neurobiological basis of gray matter volume alterations in schizophrenia with auditory verbal hallucinations: A meta-analytic investigation

Schizophrenia patients with auditory verbal hallucinations (AVH) frequently exhibit brain structural alterations, particularly reductions in gray matter volume (GMV).Understanding the neurobiological mechanisms underlying the changes is essential for advancing treatment strategies. To address this, a meta-analysis was conducted to identify GMV changes in schizophrenia patients with AVH and their associations with gene expression and neurotransmitter receptor profiles. The results indicated significant GMV reductions in the left and the right insula, as well as the left anterior cingulate cortex. Ontology analysis of genes associated with GMV alternations revealed enrichment in biological processes related to ion transport and synaptic transmission. Hub genes from the KCN, SCN, GN, and PRK families, along with neurotransmitter receptors such as D2, VAChT, and mGluR5, showed significant correlations with GMV changes. Furthermore, multivariate linear regression analysis demonstrated that GNB2, GNB4, PRKCG, D2, and mGluR5 significantly predicted GMV alternations. These findings suggest that GMV reductions in schizophrenia with AVH are linked to disruptions in neurobiological processes involving specific genes and neurotransmitter systems, highlighting the potential targets for therapeutic intervention.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.Significance Statement Alpha (8-12 Hz) oscillations and the default mode network (DMN) represent two major intrinsic activities of the brain. Prominent theories and extant findings converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Combining simultaneous electroencephalogram-functional-magnetic-resonance imaging (EEG-fMRI) with alpha-frequency transcranial alternating current stimulation (α-tACS), we demonstrated tightened coupling between alpha oscillations and DMN connectivity. These results lend credence to an inherent alpha-DMN link. Given DMN dysfunctions in multiple major neuropsychiatric conditions, the findings also highlight potential utility of α-tACS in clinical interventions by regulating the DMN.

First-person spoken narratives elicit consistent event structures in the angular gyrus

Event segmentation theory explains how we parse a stream of continuous information into meaningful event models. Narratives are useful stimuli for studying this phenomenon, as the flow of information and the way we make meaning of them mirrors how we comprehend and make sense of our daily lives. Many studies have investigated the segmentation of audiovisual stimuli, such as movies, but only a handful of studies focused on how the brain parses auditory-only narrative. Using two stories with rich narrative features, we asked participants to listen to the story-recordings while being scanned with fMRI. We then recruited two separate groups of behavioral participants to parse the stories, either via transcript (visual-only) or recording (audio-only). Annotated boundaries from the two modalities were analyzed and used as behavioral benchmarks for the neural-behavioral comparison of event structures. We examined four regions of interest (angular gyrus, posterior cingulate cortex, early auditory cortex, and early visual cortex) and found that only the angular gyrus produced neural event structures that significantly matched with the behavioral event structures across both modalities and both stories. Our results indicate that activity in the angular gyrus is associated with the neural processes involved in parsing continuous narratives, particularly when these narratives are audio-only and contain ambiguous event transitions, rather than with changes in sensory-related features.

A study of dynamic functional connectivity changes in flight trainees based on a triple network model

The time-varying functional connectivity of the Central Executive Network (CEN), Default Mode Network (DMN), and Salience Network (SN) in flight trainees during a resting state was investigated using dynamic functional network connectivity (dFNC). The study included 39 flight trainees and 37 age- and sex-matched healthy controls. Resting-state fMRI data and behavioral test outcomes were obtained from both groups. Independent component analysis (ICA), sliding window, and K-means clustering approaches were utilized for evaluating functional network connectivity (FNC) and temporal metrics based on the triple networks. Correlation analyses were performed on the behavioral assessments and these metrics. The flight trainees demonstrated a significantly enhanced functional connection linking the CEN and DMN in state 2 (P < 0.05, FDR corrected). Additionally, flight trainees spent less time in state 5, while they exhibited a protracted mean dwell time and fractional windows in state 2, which were significantly correlated with accuracy on the Berg Card Sorting Test (BCST) and Change Detection Test (all P < 0.05). The improved connectivity of flight trainees between the CEN and DMN following the completion of rigorous flight training resulted in increased stability. This enhancement may be relevant to cognitive abilities such as decision-making, memory, and information integration. When multitasking, flight trainees displayed superior visual processing skills and enhanced cognitive flexibility. dFNC research provides a unique perspective on the sophisticated cognitive capabilities that are required in high-demand, high-stress occupations such as piloting, thereby providing significant insights into the intricate brain mechanisms that are inherent in these domains.

Hubs and interaction: the brain's meta-loop

We must reconcile the needs of the internal world and the demands of the external world to make decisions relevant to homeostasis, well-being, and flexible behavior. Engagement with the internal (eg interoceptive) world is linked to medial brain systems, whereas the extrapersonal space (eg exteroceptive) is associated with lateral brain systems. Using Human Connectome Project data, we found three association tracts connecting the action-related frontal lobe with perception-related posterior lobes. A lateral dorsal tract and a medial dorsal tract interact independently with a ventral tract at frontal and posterior hubs. The two frontal and the two posterior hubs are interconnected, forming a meta-loop that integrates lateral and medial brain systems. The four anatomical hubs correspond to the common nodes of the intrinsic cognitive brain networks such as the default mode network. These functional networks depend on the integration of both realms. Thus, the positioning of functional cognitive networks can be understood as the intersection of long anatomical association tracts. The strength of structural connectivity within lateral and medial brain systems correlates with performance on behavioral tests assessing theory of mind. The meta-loop provides an anatomical framework to associate neurological and psychiatric symptoms with functional and structural changes.

Unveiling the dynamic effects of major depressive disorder and its rTMS interventions through energy landscape analysis

Introduction

Brain dynamics offer a more direct insight into brain function than network structure, providing a profound understanding of dysregulation and control mechanisms within intricate brain systems. This study investigates the dynamics of functional brain networks in major depressive disorder (MDD) patients to decipher the mechanisms underlying brain dysfunction during depression and assess the impact of repetitive transcranial magnetic stimulation (rTMS) intervention.

Methods

We employed energy landscape analysis of functional magnetic resonance imaging (fMRI) data to examine the dynamics of functional brain networks in MDD patients. The analysis focused on key dynamical indicators of the default mode network (DMN), the salience network (SN), and the central execution network (CEN). The effects of rTMS intervention on these networks were also evaluated.

Results

Our findings revealed notable dynamical alterations in the pDMN, the vDMN, and the aSN, suggesting their potential as diagnostic and therapeutic markers. Particularly striking was the altered activity observed in the dDMN in the MDD group, indicative of patterns associated with depressive rumination. Notably, rTMS intervention partially reverses the identified dynamical alterations.

Discussion

Our results shed light on the intrinsic dysfunction mechanisms of MDD from a dynamic standpoint and highlight the effects of rTMS intervention. The identified alterations in brain network dynamics provide promising analytical markers for the diagnosis and treatment of MDD. Future studies should further explore the clinical applications of these markers and the comprehensive dynamical effects of rTMS intervention.

Investigating the Consistency of Negative BOLD Responses to Combinations of Visual, Auditory, and Somatosensory Stimuli and Their Modulation by the Level of Task Demand

Negative BOLD fMRI responses (NBR) occur commonly in sensory cortex and default mode network regions but remain poorly utilized as a marker of brain function due to an incomplete understanding. To better understand how NBR manifest across the brain, compare between different sensory stimuli and how they are modulated by changes in task demand, we recorded fMRI during trials of visual, auditory, or somatosensory stimulation, delivered either alone or in concurrent pairs. Twenty young-adult participants were cued to attend to a single modality and detect targets in each trial. We found that NBR were consistently induced in all non-task-relevant primary sensory cortices and default mode regions during all stimuli. NBR were observed within the stimulated modality, in the cortex ipsilateral to the stimulus; as well as cross-modal responses bilaterally within the cortex of an unstimulated sensory modality. The NBR regions showed high spatial overlap with the primary sensory positive BOLD response (PBR) of the stimulated modality. The NBR occurred in spatially comparable regions across different modality stimuli such that the peak voxel location and spatial extent were comparable between within and cross-modal NBRs. Some specific differences were seen, such as stronger magnitude sensorimotor NBR to somatosensory stimuli than to visual or auditory. No significant relationships were found between subjects' PBR and NBR magnitude, but significant linear correlations were observed between NBRs indicating that subjects with high magnitude NBR within one sensory modality also displayed high magnitude cross-modal NBR in a different modality. These findings suggest that cortical NBR are largely consistent between different sensory stimuli but also contain stimulus-specific variability in magnitude and spatial extent. Finally, positive BOLD responses were stronger to dual stimuli in all contralateral primary sensory regions, whilst NBR were slightly increased in specific regions of ipsilateral visual and sensorimotor cortex. This finding suggests a strong contribution to NBR from bottom-up stimulus input that was further modulated by attention during dual conditions and that NBR is driven by a combination of bottom-up and top-down influences whereby contributions to its generation arise from both feed-forward signals from subcortical or activated sensory regions and feedback mechanisms such as higher-level attentional control.

Machine learning-based estimation of respiratory fluctuations in a healthy adult population using resting state BOLD fMRI and head motion parameters

PURPOSE

External physiological monitoring is the primary approach to measure and remove effects of low-frequency respiratory variation from BOLD-fMRI signals. However, the acquisition of clean external respiratory data during fMRI is not always possible, so recent research has proposed using machine learning to directly estimate respiratory variation (RV), potentially obviating the need for external monitoring. In this study, we propose an extended method for reconstructing RV waveforms directly from resting state BOLD-fMRI data in healthy adult participants with the inclusion of both BOLD signals and derived head motion parameters.

METHODS

In the proposed method, 1D convolutional neural networks (1D-CNNs) used BOLD signals and head motion parameters to reconstruct the RV waveform for the whole fMRI scan time. Resting-state fMRI data and associated respiratory records from the Human Connectome Project in Young Adults (HCP-YA) dataset are used to train and test the proposed method.

RESULTS

Compared to using only BOLD-fMRI data for a CNN input, this approach yielded improvements of 14% in mean absolute error, 24% in mean square error, 14% in correlation, and 12% in dynamic time warping. When tested on independent datasets, the method demonstrated generalizability, even in data with different TRs and physiological conditions.

CONCLUSION

This study shows that the respiratory variations could be reconstructed from BOLD-fMRI data in the young adult population, and its accuracy could be improved using supportive data such as head motion parameters. The method also performed well on independent datasets with different experimental conditions.

Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning

BACKGROUND

Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression.

METHODS

A total of 191 participants were included in this study, comprising 108 CSM patients and 83 healthy controls (HCs). Resting-state fMRI data were used to derive functional connectivity networks (FCNs), which were further analyzed to obtain sFNC and dFNC features. K-means clustering was applied to identify distinct dFNC states, and machine learning models, including support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), logistic regression (LR), and random forests (RF), were constructed to classify CSM patients and HCs based on FNC features.

RESULTS

The sFNC analysis revealed significant alterations in brain network connectivity in CSM patients, including enhanced connectivity between the posterior default mode network (pDMN) and ventral attention network (vAN), and between the right and left frontoparietal networks (rFPN and lFPN), alongside weakened connectivity in multiple other network pairs. K-means clustering of dFNC identified four distinct functional states, with CSM patients exhibiting altered connectivity in State 1 and State 3. Machine learning models based on sFNC demonstrated excellent classification performance, with the SVM model achieving an AUC of 0.92, accuracy of 85.86%, and sensitivity and specificity both exceeding 0.80. Models based on dFNC also performed well, with the State 3-based model yielding an AUC of 0.91 and accuracy of 84.97%.

CONCLUSIONS

Our findings highlight significant alterations in both sFNC and dFNC in CSM patients, suggesting that these connectivity changes may reflect underlying neural mechanisms of the disease. Machine learning models based on FNC features, particularly SVM, exhibit strong potential for classifying CSM patients and may serve as valuable neuroimaging biomarkers for diagnosis and monitoring disease progression. Future research should explore longitudinal studies and multimodal neuroimaging approaches to further validate these findings.

Attention and emotion in adolescents with ADHD; a time-varying functional connectivity study

BACKGROUND

This study assessed adolescent brain-behavior relationships between large-scale dynamic functional network connectivity (FNC) and an integrated attention-deficit/hyperactivity disorder (ADHD) phenotype, including measures of inattention, impulsivity/hyperactivity and emotional dysregulation. Despite emotion dysregulation being a core clinical feature of ADHD, studies rarely assess its impact on large-scale FNC.

METHODS

We conducted resting-state functional magnetic resonance imaging in 78 adolescents (34 with ADHD) and obtained experimental and self-reported measures of inattention, impulsivity/hyperactivity, and emotional reactivity. We used multivariate analyses to evaluate group differences in dynamic FNC between the default mode, salience and central executive networks, meta-state functional connectivity and ADHD symptomology.

RESULTS

We present two significant group*behavior effects. Compared to controls, adolescents with ADHD had 1) diminished salience network-centered dynamic FNC that was driven by an integrated ADHD phenotype (p < .004, r = 0.57) and 2) more variable patterns of global connectivity, as measured through meta-state analysis, which were driven by heightened emotional reactivity (p < .002, r = 0.63).

CONCLUSIONS

Atypical patterns of dynamic FNC in adolescents with ADHD are associated with the affective and cognitive components of ADHD symptomology. Limitations include sample size and self-reported measures of emotional reactivity.

Markers of positive affect and brain state synchrony discriminate melancholic from non-melancholic depression using naturalistic stimuli

Melancholia has been proposed as a qualitatively distinct depressive subtype associated with a characteristic symptom profile (psychomotor retardation, profound anhedonia) and a better response to biological therapies. Existing work has suggested that individuals with melancholia are blunted in their display of positive emotions and differ in their neural response to emotionally evocative stimuli. Here, we unify these brain and behavioural findings amongst a carefully phenotyped group of seventy depressed participants, drawn from an established Australian database (the Australian Genetics of Depression Study) and further enriched for melancholia (high ratings of psychomotor retardation and anhedonia). Melancholic (n = 30) or non-melancholic status (n = 40) was defined using a semi-structured interview (the Sydney Melancholia Prototype Index). Complex facial expressions were captured whilst participants watched a movie clip of a comedian and classified using a machine learning algorithm. Subsequently, the dynamics of sequential changes in brain activity were modelled during the viewing of an emotionally evocative movie in the MRI scanner. We found a quantitative reduction in positive facial expressivity amongst participants with melancholia, combined with differences in the synchronous expression of brain states during positive epochs of the movie. In non-melancholic depression, the display of positive affect was inversely related to the activity of cerebellar regions implicated in the processing of affect. However, this relationship was reduced in those with a melancholic phenotype. Our multimodal findings show differences in evaluative and motoric domains between melancholic and non-melancholic depression through engagement in ecologically valid tasks that evoke positive emotion. These findings provide new markers to stratify depression and an opportunity to support the development of targeted interventions.

Within-Individual Precision Mapping of Brain Networks Exclusively Using Task Data

Precision mapping of brain networks within individuals has become a widely used tool that prevailingly relies on functional connectivity analysis of resting-state data. Here we explored whether networks could be precisely estimated solely using data acquired during active task paradigms. The straightforward strategy involved extracting residualized data after application of a task-based general linear model (GLM) and then applying standard functional connectivity analysis. Functional correlation matrices estimated from task data were highly similar to those derived from traditional resting-state fixation data. The largest factor affecting similarity between correlation matrices was the amount of data. Networks estimated within-individual from task data displayed strong spatial overlap with those estimated from resting-state fixation data and predicted the same triple functional dissociation in independent data. The implications of these findings are that (1) existing task data can be reanalyzed to estimate within-individual network organization, (2) resting-state fixation and task data can be pooled to increase statistical power, and (3) future studies can exclusively acquire task data to both estimate networks and extract task responses. Most broadly, the present results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states.

Dysregulated connectivity configuration of triple-network model in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is signified by altered functional network connectivity (FNC), particularly within the default mode network (DMN), salience network (SAL), and fronto-parietal network (FPN). While previous studies suggest disruptions within triple networks, dynamic causal interactions across networks remain unaddressed. This study seeks to validate previous findings of static dysconnectivity between triple networks and further delineate the time-varying interactions and causal relationships among these networks in OCD. A resting-state functional magnetic resonance imaging study was performed on a relatively large and well-characterized clinical sample, comprising 88 medication-free OCD patients and 93 healthy controls (HC). Group independent component analysis, combined with a sliding window approach and k-means clustering analysis, was used to assess static and dynamic time-varying FNC within triple networks. Spectral dynamic causal modelling and parametric empirical Bayes framework were utilized to explore the abnormal effective connectivity among these networks in OCD patients. Our results proposed a novel dysregulated connectivity configuration of the triple-network model for OCD. With the self-inhibition increase in the left FPN, the excitatory effect onto the right FPN decrease, resulting in a weakened static connectivity between the left and right FPNs. Concurrently, time-varying hypoconnectivity patterns are observed between the left FPN and DMN, as well as the right FPN and SAL in OCD. Additionally, the excitatory influence from the DMN to the SAL suggests an atypical modulation within OCD's network pathology. These findings advance our understanding of the dysregulated information transfer and the complex interplay of brain networks in OCD, potentially guiding future therapeutic strategies.

Multi-parametric [18F]PI-2620 tau PET/MRI for the phenotyping of different Alzheimer's disease variants

PURPOSE

Heterogeneity in clinical phenotypes has led to the description of different phenotypes of Alzheimer's disease (AD). Besides the most frequent amnestic variant of AD (aAD), patients presenting with language deficits are diagnosed with logopenic variant primary progressive aphasia (lvPPA), whereas patients presenting with visual deficits are classified as posterior cortical atrophy (PCA).

METHODS

This study set out to investigate the value of a multi-parametric [18F]PI-2620 tau PET/MRI protocol to distinguish aAD, lvPPA and PCA to support clinical diagnosis in 32 patients. Phenotype-specific information about tau accumulation, relative perfusion, grey matter density, functional network alterations and white matter microstructural alterations was collected.

RESULTS

The aAD patients showed significantly higher tau accumulation, relative hypoperfusion and grey matter density loss in the temporal lobes compared to PCA and lvPPA patients. PCA patients, on the other hand, showed significantly higher tau accumulation in the occipital lobe as compared to aAD patients. Relative hypoperfusion in the occipital lobe and loss of functional connectivity of the posterior cingulate cortex to supplementary visual cortical regions helped to distinguish PCA from lvPPA. Tau accumulation in the cerebellum and microstructural changes in the cingulum were found to help differentiate lvPPA from aAD.

CONCLUSION

This study highlights structural and functional differences between patients with different AD phenotypes. Differences in regional tau PET signals suggest that refinements in the Braak staging system are needed for the non-aAD cases. These patterns of tau accumulation align with the cascading network failure hypothesis, though more research is needed to warrant the here presented results in larger patient cohorts.

Test-retest reliability of resting-state EEG intrinsic neural timescales

Intrinsic neural timescales, which reflect the duration of neural information storage within local brain regions and capacity for information integration, are typically measured using autocorrelation windows (ACWs). Extraction of intrinsic neural timescales from resting-state brain activity has been extensively applied in psychiatric disease research. Given the potential of intrinsic neural timescales as a neural marker for psychiatric disorders, investigating their reliability is crucial. This study, using an open-source database, aimed to evaluate the test-retest reliability of ACW-0 and ACW-50 under both eyes-open and eyes-closed conditions across three sessions. The intraclass correlation coefficients (ICCs) were employed to quantify the reliability of the intrinsic neural timescales. Our results showed that intrinsic neural timescales exhibited good reliability (ICC > 0.6) at the whole-brain level across different index types and eye states. Spatially, except for the right temporal region in the eyes-open condition, all other regions showed moderate-to-high ICCs. Over 60% of the electrodes demonstrated moderate-to-high intrinsic neural timescale ICCs under both eyes-open and eyes-closed conditions, with ACW-0 being more stable than ACW-50. Moreover, in the new dataset, the above results were consistently reproduced. The present study comprehensively assessed the reliability of intrinsic neural timescale under various conditions, providing robust evidence for their stability in neuroscience and psychiatry.

Cortical modulation of BOLD signals in white matter

The relationship of BOLD signals in white matter to cortical neural activity remains unclear. We quantified the degree to which spontaneous neural activities in the cortex, which are reflected in low frequency fluctuations in cortical BOLD signals, modulate BOLD signals in white matter. From measurements of resting state correlations we find cortical networks of more basic level functions tend to contribute more to correlated fluctuations in white matter than those of higher level functions. In addition, each cortical network exhibits distinct, structurally interpretable spatial distribution patterns of white matter projections. Moreover, the myelination level of cortical networks is found to be strongly correlated with the white matter projection of cortical BOLD signals. Our findings confirm that BOLD signals in white matter encode neural activity in proportion to the spontaneous activity of individual cortical networks, and with network-specific spatial distribution patterns, which could be mediated by the microstructure of the brain cortex.

Crochet increases attention through a requiring motor skill learning

In this study, we compared the effects promoted by a brief single session of crochet in a group of skilled knitters (CRO) and a control group (CRT) on the Attentional Network Test (ANT) and the whole brain Functional Connectivity (FC) revealed by Magnetoencephalography (MEG). Data revealed that crochet determined a significant effect (before, T0, vs after, T1, the crochet session) on reaction times (for all cue and stimulus types), improving alertness and orienting networks (but not executive control) only in the CRO group. Data of FC are coherent with the behavioural ones. We observed that the Betweenness Centrality maximum (BCmax) index in the beta band significantly increased, and global FC in the alpha band significantly increased at T1 for the CRO group but not for the CTR group. Increased global BCmax in the beta band after the crochet activity correlated with better performance (reduced reaction times), suggesting that the brain has become more efficiently integrated, thus increasing the information exchange between different brain areas. Decreased global FC in the alpha band may reflect a transition from a quiet, global rest to a condition of increased alertness and readiness to stimuli. Finally, we discuss the hypothesis that these results could be the reinforcement of connections between motor and attentional networks promoted by learning the complex motor skills of crochet.

Evaluation of mild cognitive impairment in older patients with essential tremor

INTRODUCTION

Recent studies have reported that essential tremor (ET) presents with not only motor symptoms but also cognitive dysfunction. However, detailed pathological mechanisms remain unclear. Here, we evaluate the characteristics of cognitive changes in older patients.

METHODS

Eighty-five patients aged 65 years or older with ET but without dementia were evaluated for cognitive function using the Addenbrooke Cognitive Examination Revised (ACE-R). The patients were compared with healthy controls (HCs), and the characteristics of cognitive dysfunction were examined. Age at onset and correlations with tremor severity were also investigated. Moreover, we performed resting-state network (RSNs) analysis in a subset of these patients, and the functional connectivity (FC) within the networks was compared with age-matched controls.

RESULTS

Compared to HCs, older patients with ET showed a clear reduction in the total (p = 0.001), attention (p = 0.005), verbal fluency (p = 0.001), and memory (p = 0.001) ACE-R scores. Older-onset patients showed significant cognitive dysfunction compared with younger-onset patients. Verbal fluency correlated with tremor severity in the multiple regression analysis (p < 0.001). RSNs showed an increase in FC in the frontal lobes within the language network in patients with ET compared to HCs (p < 0.05, FWE-corrected).

CONCLUSION

Older patients with ET showed obvious cognitive dysfunction compared to HCs, indicating that cognitive dysfunction varies by age of onset and correlates with tremor severity. The results of the RSNs analysis suggest that the pathological mechanism of cognitive dysfunction in ET patients involves network changes similar to those in the early stages of Alzheimer's disease.

Alpha-frequency stimulation strengthens coupling between temporal fluctuations in alpha oscillation power and default mode network connectivity

Alpha (8-12 Hz) oscillations and default mode network (DMN) activity dominate the brain's intrinsic activity in the temporal and spatial domains, respectively. They are thought to play crucial roles in the spatiotemporal organization of the complex brain system. Relatedly, both have been implicated, often concurrently, in diverse neuropsychiatric disorders, with accruing electroencephalogram/magnetoencephalogram (EEG/MEG) and functional magnetic resonance imaging (fMRI) data linking these two neural activities both at rest and during key cognitive operations. Prominent theories and extant findings thus converge to suggest a mechanistic relationship between alpha oscillations and the DMN. Here, we leveraged simultaneous EEG-fMRI data acquired before and after alpha-frequency transcranial alternating current stimulation (α-tACS) and observed that α-tACS tightened the dynamic coupling between spontaneous fluctuations in alpha power and DMN connectivity (especially, in the posterior DMN, between the posterior cingulate cortex and the bilateral angular gyrus). In comparison, no significant changes were observed for temporal correlations between power in other oscillatory frequencies and connectivity in other major networks. These results thus suggest an inherent coupling between alpha and DMN activity in humans. Importantly, these findings highlight the efficacy of α-tACS in regulating the DMN, a clinically significant network that is challenging to target directly with non-invasive methods.

Situating the salience and parietal memory networks in the context of multiple parallel distributed networks using precision functional mapping

Brain networks serving higher cognitive functions are widely distributed across frontal and posterior association zones. Two exceptions have been the parietal memory network (PMN) and salience network (SAL), which are typically restricted to posterior (e.g., posterior cingulate and lateral parietal cortex) and anterior (medial prefrontal and anterior insular cortex) areas, respectively. Using high-resolution neuroimaging, we show that individualized estimates of the PMN extend beyond the posterior set and encompass frontal and insula regions canonically ascribed to the SAL. This suggests that the SAL and PMN form a unified network: "SAL/PMN." Task-based analyses confirm that both anterior and posterior components of the SAL/PMN show recognition-related activity. Comparison of 3T and 7T data suggests that high-resolution data more readily revealed the unified network, underscoring the importance of fine-scale distinctions for veridical representation of brain networks. Importantly, the unified network better matches the expected parallel distributed network organization that is characteristic of association cortex.

Resting-state BOLD temporal variability of the default mode network predicts spontaneous mind wandering, which is negatively associated with mindfulness skills

Mind wandering (MW) encompasses both a deliberate and a spontaneous disengagement of attention from the immediate external environment to unrelated internal thoughts. Importantly, MW has been suggested to have an inverse relationship with mindfulness, a state of nonjudgmental awareness of present-moment experience. Although they are, respectively, associated with increased and decreased activity in the default mode network (DMN), the specific contributions of deliberate and spontaneous MW, and their relationships with mindfulness abilities and resting-state macro networks remain to be elucidated. Therefore, resting-state MRI scans from 76 participants were analyzed with group independent component analysis to decompose brain networks into independent macro-networks and to see which of them predicted specific aspects of spontaneous and deliberate MW or mindfulness traits. Our results show that temporal variability of the resting-state DMN predicts spontaneous MW, which in turn is negatively associated with the acting with awareness facet of mindfulness. This finding shows that the DMN is not directly associated with overall mindfulness, but rather demonstrates that there exists a close relationship between DMN and MW, and furthermore, that the involvement of mindfulness abilities in this dynamic may be secondary. In sum, our study contributes to a better understanding of the neural bases of spontaneous MW and its relationship with mindfulness. These results open up the possibility of intervening on specific aspects of our cognitive abilities: for example, our data suggest that training the mindfulness facet acting with awareness would allow lessening our tendency for MW at inopportune times.

Alterations of triple network dynamic connectivity and repetitive behaviors after mini-basketball training program in children with autism spectrum disorder

Physical exercise has been demonstrated to effectively mitigate repetitive behaviors in children with autism spectrum disorder (ASD), but the underlying dynamic brain network mechanisms are poorly understood. The triple network model consists of three brain networks that jointly regulate cognitive and emotional processes and is considered to be the core network underlying the aberrant manifestations of ASD. This study investigated whether a mini-basketball training program (MBTP) could alter repetitive behaviors and the dynamic connectivity of the triple network. 28 male children with ASD were scanned twice with resting-state functional MRI and assessed for repetitive behaviors using the repetitive behavior scale (RBS-R). 15 children in the exercise group participated in a 12-week MBTP, while 13 in the control group maintained their regular routines. The feature of Dynamic independent component analysis (dyn-ICA) is its ability to capture the rate of change in connectivity between brain regions. In this study, it was specifically employed to examine the triple network dynamic connectivity in both groups. Compared to the control group, the exercise group exhibited distinct dynamic connectivity patterns in two networks: Network 1 involved cross-network dynamic connectivity changes within the triple network, and Network 2 pertained to dynamic connectivity alterations within the default mode network. Furthermore, a reduction in the RBS-R Total score was observed in the exercise group, reflecting improvements in self-injurious behavior and restricted behavior. Correlation analysis revealed that the amelioration of repetitive behaviors was associated with enhanced dynamic connectivity in parts of the triple network. These findings suggest that MBTP can improve repetitive behaviors in ASD children and is linked to changes in triple network dynamic connectivity.

Effect of metacognitive interpersonal therapy on brain structural connectivity in borderline personality disorder: Results from the CLIMAMITHE randomized clinical trial

BACKGROUND

Recently, we showed that Metacognitive Interpersonal Therapy (MIT) is effective in improving clinical symptoms in borderline personality disorder (BPD). Here, we investigated whether the effect of MIT on clinical features is associated to microstructural changes in brain circuits supporting core BPD symptoms.

METHODS

Forty-seven BPD were randomized to MIT or structured clinical management, and underwent a clinical assessment and diffusion-weighted imaging before and after the intervention. Fractional anisotropy (FA), mean, radial, and axial diffusivities maps were computed using FSL toolbox. Microstructural changes were assessed (i) voxel-wise, with tract based spatial statistics (TBSS) and (ii) ROI-wise, in the triple network system (default mode, salience, and executive control networks). The effect of MIT on brain microstructure was assessed with paired tests using FSL PALM (voxel-wise), Linear Mixed-Effect Models or Generalized Linear Mixed Models (ROI-wise). Associations between microstructural and clinical changes were explored with linear regression (voxel-wise) and correlations (ROI-wise).

RESULTS

The voxel-wise analysis showed that MIT was associated with increased FA in the bilateral thalamic radiation and left associative tracts (p < .050, family-wise error rate corrected). At network system level, MIT increased FA and both interventions reduced AD in the executive control network (p = .05, uncorrected).

LIMITATIONS

The DTI metrics can't clarify the nature of axonal changes.

CONCLUSIONS

Our results indicate that MIT modulates brain structural connectivity in circuits related to associative and executive control functions. These microstructural changes may denote activity-dependent plasticity, possibly representing a neurobiological mechanism underlying MIT effects.

TRIAL REGISTRATION

ClinicalTrials.govNCT02370316 (https://clinicaltrials.gov/study/NCT02370316).

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach

INTRODUCTION

Sex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN).

METHODS

Using an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns.

RESULTS

Our findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns.

CONCLUSION

This study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Functional connectivity changes in males with nicotine addiction: A triple network model study

BACKGROUND

Nicotine addiction (NA) is recognized as a significant neurobehavioral disorder that affects both individuals and society. It is suggested that alterations in functional network connectivity (FNC) within specific brain networks underlie its neurobiological basis.

METHODS

The default mode network (DMN), executive control network (ECN), and salience network (SN) are identified using data from the Human Connectome Project. The study includes 47 individuals with NA and 35 normal controls (NC), all of whom undergo resting-state fMRI alongside smoking-related clinical assessments. A sliding window analysis is employed to assess connectivity metrics, including static functional network connectivity (FNC), standard deviation (SD), and coefficient of variation (CV), to compare information integration between the groups. Participants with NA are classified based on longitudinal changes in Fagerström Test for Nicotine Dependence (FTND) scores over six years into three categories: addiction tendency (AT), withdrawal tendency (WT), and stable tendency (ST). Correlation analyses are conducted to explore relationships between FNC abnormalities and clinical assessments.

RESULTS

Individuals with NA exhibit reduced static FNC (p_FDR = 0.029) between the dorsal DMN and the right ECN, accompanied by increased SD (p_FDR = 0.029) and CV (p_FDR = 0.029). A significant increase in SD (p_FDR = 0.049) is also observed in the dorsal DMN and left ECN. Correlations indicate that the SD of the dorsal DMN and right ECN relates to the pharmacological dimension of the Russell Smoking Reasons Questionnaire (RRSQ) scale (r = 0.416, p_FDR = 0.044), while CV correlates with changes in the FTND over six years (r = -0.391, p_FDR = 0.044) and the pharmacological dimension of the RRSQ scale (r = 0.402, p_FDR = 0.044). Post-hoc subgroup analyses reveal that these FNC intensity changes are present among WT patients (p_FDR < 0.05).

CONCLUSIONS

Alterations in brain network function within the DMN and ECN are suggested to precede behavioral changes in NA. These findings are interpreted as potential neurobiological markers of nicotine addiction.

Levodopa therapy affects brain functional network dynamics in Parkinson's disease

Levodopa (L-dopa) therapy is the most effective pharmacological treatment for motor symptoms of Parkinson's disease (PD). However, its effect on brain functional network dynamics is still unclear. Here, we recruited 26 PD patients and 24 healthy controls, and acquired their resting-state functional MRI data before (PD-OFF) and after (PD-ON) receiving 400 mg L-dopa. Using the independent component analysis and the sliding-window approach, we estimated the dynamic functional connectivity (dFC) and examined the effect of L-dopa on the temporal properties of dFC states, the variability of dFC and functional network topological organization. We found that PD-ON showed decreased mean dwell time in sparsely connected State 2 than PD-OFF, the transformation of the dFC state is more frequent and the variability of dFC was decreased within the auditory network and sensorimotor network in PD-ON. Our findings provide new insights to understand the dynamic neural activity induced by L-dopa therapy in PD patients.

Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke

Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients. Fifty stroke patients and 50 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Initially, the independent component analysis (ICA) method was utilized to extract the resting-state network (RSN). Subsequently, the disparities in static functional network connectivity both within and between networks among the two groups were computed and juxtaposed. Following this, five consistent and robust dynamic functional network connectivity (dFNC) states were derived by integrating the sliding time window method with k-means cluster analysis, and the distinctions in dFNC between the groups across different states, along with the intergroup variations in three dynamic temporal metrics, were assessed. Finally, a support vector machine (SVM) approach was employed to discriminate stroke patients from HCs using FC and FNC as classification features. Comparing the stroke group to the healthy control (HC) group, the stroke group exhibited reduced intra-network functional connectivity (FC) in the right superior temporal gyrus of the ventral attention network (VAN), the left calcarine of the visual network (VN), and the left precuneus of the default mode network (DMN). Regarding static functional network connectivity (FNC), we identified increased connectivity between the executive control network (ECN) and dorsal attention network (DAN), salience network (SN) and DMN, SN-ECN, and VN-ECN, along with decreased connectivity between DAN-DAN, ECN-SN, SN-SN, and DAN-VN between the two groups. Noteworthy differences in dynamic FNC (dFNC) were observed between the groups in states 3 to 5. Moreover, stroke patients demonstrated a significantly higher proportion of time and longer mean dwell time in state 4, alongside a decreased proportion of time in state 5 compared to HC. Finally, utilizing FC and FNC as features, stroke patients could be distinguished from HC with an accuracy exceeding 70% and an area under the curve ranging from 0.8284 to 0.9364. In conclusion, our study reveals static and dynamic changes in large-scale brain networks in stroke patients, potentially linked to abnormalities in visual, cognitive, and motor functions. This investigation offers valuable insights into the neural mechanisms underpinning the functional deficits observed in stroke, thereby aiding in the diagnosis and development of targeted therapeutic interventions for affected individuals.

Multi-omics characterization of improved cognitive functions in Parkinson's disease patients after the combined metabolic activator treatment: a randomized, double-blinded, placebo-controlled phase II trial

Parkinson's disease is primarily marked by mitochondrial dysfunction and metabolic abnormalities. We recently reported that the combined metabolic activators improved the immunohistochemical parameters and behavioural functions in Parkinson's disease and Alzheimer's disease animal models and the cognitive functions in Alzheimer's disease patients. These metabolic activators serve as the precursors of nicotinamide adenine dinucleotide and glutathione, and they can be used to activate mitochondrial metabolism and eventually treat mitochondrial dysfunction. Here, we designed a randomized, double-blinded, placebo-controlled phase II study in Parkinson's disease patients with 84 days combined metabolic activator administration. A single dose of combined metabolic activator contains L-serine (12.35 g), N-acetyl-L-cysteine (2.55 g), nicotinamide riboside (1 g) and L-carnitine tartrate (3.73 g). Patients were administered either one dose of combined metabolic activator or a placebo daily for the initial 28 days, followed by twice-daily dosing for the next 56 days. The main goal of the study was to evaluate the clinical impact on motor functions using the Unified Parkinson's Disease Rating Scale and to determine the safety and tolerability of combined metabolic activator. A secondary objective was to assess cognitive functions utilizing the Montreal Cognitive Assessment and to analyse brain activity through functional MRI. We also performed comprehensive plasma metabolomics and proteomics analysis for detailed characterization of Parkinson's disease patients who participated in the study. Although no improvement in motor functions was observed, cognitive function was shown to be significantly improved (P < 0.0000) in Parkinson's disease patients treated with the combined metabolic activator group over 84 days, whereas no such improvement was noted in the placebo group (P > 0.05). Moreover, a significant reduction (P = 0.001) in Montreal Cognitive Assessment scores was observed in the combined metabolic activator group, with no decline (P > 0.05) in the placebo group among severe Parkinson's disease patients with lower baseline Montreal Cognitive Assessment scores. We showed that improvement in cognition was associated with critical brain network alterations based on functional MRI analysis, especially relevant to areas with cognitive functions in the brain. Finally, through a comprehensive multi-omics analysis, we elucidated the molecular mechanisms underlying cognitive improvements observed in Parkinson's disease patients. Our results show that combined metabolic activator administration leads to enhanced cognitive function and improved metabolic health in Parkinson's disease patients as recently shown in Alzheimer's disease patients. The trial was registered in ClinicalTrials.gov NCT04044131 (17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131).

Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks

The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities among chronic marijuana users. HOGANN integrates dynamic intrinsic functional brain networks, estimated from functional magnetic resonance imaging (fMRI), using graph attention-based long short-term memory (GAT-LSTM) to capture temporal network dynamics. We employ a high-order attention module for information fusion and message passing among neighboring nodes, enhancing the network community analysis. Our model is validated across two distinct data cohorts, yielding substantially higher classification accuracy than benchmark algorithms. Furthermore, we discern the most pertinent subnetworks and cognitive regions affected by persistent marijuana consumption, indicating adverse effects on functional brain networks, particularly within the dorsal attention and frontoparietal networks. Intriguingly, our model demonstrates superior performance in cohorts exhibiting prolonged dependence, implying that prolonged marijuana usage induces more pronounced alterations in brain networks. The model proficiently identifies craving brain maps, thereby delineating critical brain regions for analysis.

Distinct Behavioural and Brain Response Profiles Between Arithmetic Word Problem Solving and Sentence Comprehension in Third and Fourth Graders

Word problems are essential for math learning and education, bridging numerical knowledge with real-world applications. Despite their importance, the neural mechanisms underlying word problem solving, especially in children, remain poorly understood. Here, we examine children's cognitive and brain response profiles for arithmetic word problems (AWPs), which involve one-step mathematical operations, and compare them with nonarithmetic word problems (NWPs), structured as parallel narratives without numerical operations. Behavioural results suggested that AWP performance was associated with both reading comprehension and arithmetic fluency, whereas NWP performance correlated only with reading comprehension. Neuroimaging results revealed distinct neural substrates: AWP solving primarily activated the anterior insula, middle frontal gyrus and intraparietal sulcus, whereas NWP solving engaged in the inferior frontal gyrus, middle temporal gyrus and angular gyrus. Critically, we observed a developmental shift: Children showed heightened prefrontal activation during AWP solving, contrasting with increased posterior parietal engagement in adults. Moreover, although adults demonstrated brain-behaviour associations, with slower AWP solving linked to stronger parietal activation, this relationship was absent in children. Taken together, these findings suggest that AWP solving recruits specialized arithmetic brain circuits that undergo a frontal-to-parietal trajectory. Our study thus provides a neurological basis for AWP solving in children, emphasizing the crucial role of the fronto-insular-parietal network. These insights into brain-based contributions to developmental differences may guide the development of targeted remediation strategies and educational interventions tailored to individual learning needs.

Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning

Emotion and cognition involve an intricate crosstalk of neural and endocrine systems that support dynamic reallocation of neural resources and optimal adaptation for upcoming challenges, an active process analogous to allostasis. As a hallmark of human endocrine activity, the cortisol awakening response (CAR) is recognized to play a critical role in proactively modulating emotional and executive functions. Yet, the underlying mechanisms of such proactive effects remain elusive. By leveraging pharmacological neuroimaging and hidden Markov modeling of brain state dynamics, we show that the CAR proactively modulates rapid spatiotemporal reconfigurations (state) of large-scale brain networks involved in emotional and executive functions. Behaviorally, suppression of CAR proactively impaired performance of emotional discrimination but not working memory (WM), while individuals with higher CAR exhibited better performance for both emotional and WM tasks. Neuronally, suppression of CAR led to a decrease in fractional occupancy and mean lifetime of task-related brain states dominant to emotional and WM processing. Further information-theoretic analyses on sequence complexity of state transitions revealed that a suppressed or lower CAR led to higher transition complexity among states primarily anchored in visual-sensory and salience networks during emotional task. Conversely, an opposite pattern of transition complexity was observed among states anchored in executive control and visuospatial networks during WM, indicating that CAR distinctly modulates neural resources allocated to emotional and WM processing. Our findings establish a causal link of CAR with brain network dynamics across emotional and executive functions, suggesting a neuroendocrine account for CAR proactive effects on human emotion and cognition.

Similarities and differences in dynamic properties of brain networks between internet gaming disorder and tobacco use disorder

BACKGROUND

Internet gaming disorder (IGD) and tobacco use disorder (TUD) are two major addiction disorders that result in substantial financial loss. Identifying the similarities and differences between these two disorders is important to understand substance addiction and behavioral addiction. The current study was designed to compare these two disorders utilizing dynamic analysis.

METHOD

Resting-state data were collected from 35 individuals with IGD, 35 individuals with TUD and 35 healthy controls (HCs). Dynamic coactivation pattern analysis was employed to decipher their dynamic patterns.

RESULTS

IGD participants showed decreased coactivation patterns within the default mode network (DMN) and between the DMN and the salience network (SN). The SN showed reduced coactivation patterns with the executive control network (ECN) and DMN, and the ECN showed decreased coactivation patterns with the DMN. In the TUD group, the DMN exhibited decreased coactivation patterns with the SN, the SN exhibited reduced coactivation patterns with the DMN and ECN, and the ECN showed decreased coactivation patterns with the DMN and within the ECN. Furthermore, the triple network model was fitted to the dynamic properties of the two addiction disorders. Decoding analysis results indicated that addiction-related memory and memory retrieval displayed similar dysfunctions in both addictions.

CONCLUSION

The dynamic characteristics of IGD and TUD suggest that there are similarities in the dynamic features between the SN and DMN and differences in the dynamic features between the DMN and ECN. Our results revealed that the two addiction disorders have dissociable brain mechanisms, indicating that future studies should consider these two addiction disorders as having two separate mechanisms to achieve precise treatment for their individualized targets.

Effects of childhood trauma on sustained attention in major depressive disorder: the mediating role of brain activity and functional connectivity

BACKGROUND

Sustained attention deficits were reported more significant in patients with major depressive disorder (MDD) than in healthy controls (HCs), and are pivotal in both the development and aggravation of depression. Childhood trauma is also common in MDD and the exposure to childhood trauma may impede sustained attention and increase the treatment resistance in MDD. However, the underlying neuro-mechanisms link the childhood trauma to sustained attention deficits in MDD remain unclear.

METHODS

We collected resting-state functional magnetic resonance imaging data, and measured childhood trauma severity using the Childhood Trauma Questionnaire and sustained attention using the Continuous Performance Test, Identical Pairs version. After excluding subjects with significant head movement, 45 MDDs and 54 HCs were included in the analysis. We compared fractional amplitude of low-frequency fluctuation (fALFF) between the groups, conducted whole-brain correlation analysis between the fALFF and sustained attention in the MDD group, and defined significant regions as the regions of interest for the seed-to-whole brain functional connectivity (FC) analysis. We further performed mediation analyses to investigate the relationships among the childhood trauma, fALFF and FC values, and the level of sustained attention in the MDD group.

RESULTS

Compared with HCs, MDDs exhibited higher fALFF in the right middle frontal gyrus and left inferior frontal gyrus, and lower fALFF in the bilateral insular cortex, left medial orbital superior frontal gyrus and left angular gyrus (ANG.L). Whole-brain correlation analysis showed that impaired sustained attention was associated with increased fALFF in the left postcentral gyrus (PoCG.L), and FC of PoCG.L-left precentral gyrus (PreCG.L) and ANG.L-right superior temporal gyrus (STG.R) in the MDD group. Furthermore, mediation analyses showed that the fALFF in PoCG.L, and FC of PoCG.L-PreCG.L and ANG.L-STG.R mediated the relationship between the childhood trauma and sustained attention in the MDD group.

CONCLUSION

The fALFF in PoCG.L, and FC of PoCG.L-PreCG.L and ANG.L-STG.R might be potential neural substrate in the association between the childhood trauma and poor sustained attention in the MDDs, and might serve as potential intervention targets for the treatment of sustained attention deficits in MDDs with childhood trauma history.

Mapping the mentalizing brain: An ALE meta-analysis to differentiate the representation of social scenes and ages on theory of mind

Theory of mind (ToM) involves understanding others' mental states and relies on brain regions like the temporoparietal junction (TPJ) and medial prefrontal cortex (mPFC). This meta-analytic review categorizes ToM studies into six sub-components across three pairs: (1) Theory of collective mind (ToCM) and individualized theory of mind (iToM), (2) Social intention ToM and private intention ToM, and (3) ToM in adults and ToM in children. We conducted coordinate-based activation likelihood estimation (ALE) analyses and meta-analytic connectivity modeling (MACM) for each sub-component. We found that the ToM components utilized in social or group situations were associated with both the dorsomedial PFC (dmPFC) and right superior temporal sulcus (STS), whereas the ToM components focused on personal concentration were associated with both the lateral PFC and the left STS. The coactivation patterns for the group and age sub-component pairs showed significant spatial overlap with the language networks. These findings indicate that ToM is a multidimensional construct that is related to distinct functional networks for processing each of the ToM sub-components.

Resting State Functional Networks in Gliomas: Validation With Direct Electric Stimulation Using a New Tool for Planning Brain Resections

BACKGROUND AND OBJECTIVES

Precise mapping of functional networks in patients with brain tumor is essential for tailoring personalized treatment strategies. Resting-state functional MRI (rs-fMRI) offers an alternative to task-based fMRI, capable of capturing multiple networks within a single acquisition, without necessitating task engagement. This study demonstrates a strong concordance between preoperative rs-fMRI maps and the gold standard intraoperative direct electric stimulation (DES) mapping during awake surgery.

METHODS

We conducted an analysis involving 28 patients with glioma who underwent awake surgery with DES mapping. A total of 100 DES recordings were collected to map sensorimotor (SMN), language (LANG), visual (VIS), and speech articulation cognitive domains. Preoperative rs-fMRI maps were generated using an updated version of the ReStNeuMap software, specifically designed for rs-fMRI data preprocessing and automatic detection of 7 resting-state networks (SMN, LANG, VIS, speech articulation, default mode, frontoparietal, and visuospatial). To evaluate the agreement between these networks and those mapped with invasive cortical mapping, we computed patient-specific distances between them and intraoperative DES recordings.

RESULTS

Automatically detected preoperative functional networks exhibited excellent agreement with intraoperative DES recordings. When we spatially compared DES points with their corresponding networks, we found that SMN, VIS, and speech articulatory DES points fell within the corresponding network (median distance = 0 mm), whereas for LANG a median distance of 1.6 mm was reported.

CONCLUSION

Our findings show the remarkable consistency between key functional networks mapped noninvasively using presurgical rs-fMRI and invasive cortical mapping. This evidence highlights the utility of rs-fMRI for personalized presurgical planning, particularly in scenarios where awake surgery with DES is not feasible to protect eloquent areas during tumor resection. We have made the updated tool for automated functional network estimation publicly available, facilitating broader utilization of rs-fMRI mapping in various clinical contexts, including presurgical planning, functional reorganization over follow-up periods, and informing future treatments such as radiotherapy.

Hyperbolic Graph Embedding of MEG Brain Networks to Study Brain Alterations in Individuals With Subjective Cognitive Decline

An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.

Altered Dynamics of Brain Spontaneous Activity and Functional Networks Associated With Cognitive Impairment in Patients With Type 2 Diabetes

BACKGROUND

Cognitive impairment is increasingly recognized as an important comorbidity and complication of type 2 diabetes (T2D), affecting patients' quality of life and diabetes management. Dynamic brain activity indicators can reflect changes in key neural activity patterns of cognition and behavior.

PURPOSE

To investigate dynamic functional connectivity (DFC) changes and spontaneous brain activity based on resting-state functional magnetic resonance imaging (rs-fMRI) in patients with T2D, exploring their correlations with clinical features.

STUDY TYPE

Retrospective.

SUBJECTS

Forty-five healthy controls (HCs) (22 males and 23 females) and 102 patients with T2D (57 males and 45 females).

FIELD STRENGTH/SEQUENCE

3.0 T/T1-weighted imaging and rs-fMRI with gradient-echo planar imaging sequence.

ASSESSMENT

Functional networks were created using independent component analysis. DFC states were determined using sliding window approach and k-means clustering. Spontaneous brain activity was assessed using dynamic regional homogeneity (dReHo) variability.

STATISTICAL TESTS

One-way analysis of variance and post hoc analysis were used to compare the essential information including demographics, clinical data, and features of DFC and dReHo among groups. Diagnostic performance was assessed using receiver operating characteristic (ROC) curve. P-values <0.05 were taken to indicate statistical significance.

RESULTS

T2D group had significantly decreased mean dwell time and fractional windows in state 4 compared to HC. T2D with mild cognitive impairment showed significantly increased dReHo variability in left superior occipital gyrus compared to T2D with normal cognition. Mean dwell time and number of fractional windows of state 4 both showed significant positive correlations with the Montreal cognitive assessment scores (r = 0.309; r = 0.308, respectively) and the coefficient of variation of dReHo was significantly positively correlated with high-density lipoprotein cholesterol (r = 0.266). The integrated index had an area under the curve of 0.693 (95% confidence interval = 0.592-0.794).

DATA CONCLUSION

Differences in DFC and dynamic characteristic of spontaneous brain activity associated with T2D-related functional impairment may serve as indicators for predicting symptom progression and assessing cognitive dysfunction.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

Frontoparietal network integrity supports cognitive function in pre-symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion

INTRODUCTION

Genetic mutation carriers of frontotemporal dementia can remain cognitively well despite neurodegeneration. A better understanding of brain structural, perfusion, and functional patterns in the pre-symptomatic stage could inform accurate staging and potential mechanisms.

METHODS

We included 207 pre-symptomatic genetic mutation carriers and 188 relatives without mutations. The gray matter volume, cerebral perfusion, and resting-state functional network maps were co-analyzed using linked independent component analysis (LICA). Multiple regression analysis was used to investigate the relationship of LICA components to genetic status and cognition.

RESULTS

Pre-symptomatic mutation carriers showed an age-related decrease in the left frontoparietal network integrity, while non-carriers did not. Executive functions of mutation carriers became dependent on the left frontoparietal network integrity in older age.

DISCUSSION

The frontoparietal network integrity of pre-symptomatic mutation carriers showed a distinctive relationship to age and cognition compared to non-carriers, suggesting a contribution of the network integrity to brain resilience.

HIGHLIGHTS

A multimodal analysis of structure, perfusion, and functional networks. The frontoparietal network integrity decreases with age in pre-symptomatic carriers only. Executive functions of pre-symptomatic carriers dissociated from non-carriers.

Resting-state functional connectivity abnormalities in subjective cognitive decline: A 7T MRI study

Resting-state functional connectivity (FC) MRI is sensitive to brain changes in Alzheimer's disease in preclinical stages, however studies in persons with subjective cognitive decline (SCD) have reported conflicting findings, and no study is available at 7T MRI. In this study, we investigated FC alterations in sixty-six participants recruited at the Geneva Memory Center (24 controls, 14 SCD, 28 cognitively impaired [CI]). Participants were classified as SCD if they reported cognitive complaints without objective cognitive deficits, and underwent 7T fMRI to assess FC in canonical brain networks and their association with cognitive/clinical features. SCD showed normal cognition, a trend for higher depressive symptoms, and normal AD biomarkers. Compared to the other two groups, SCD showed higher FC in frontal default mode network (DMN) and insular and superior temporal nodes of ventral attention network (VAN). Higher FC in the DMN and VAN was associated with worse cognition but not depression, suggesting that hyper-connectivity in these networks may be a signature of age-related cognitive decline in SCD at low risk of developing AD.

How Distributed Subcortical Integration of Reward and Threat May Inform Subsequent Approach-Avoidance Decisions

Healthy and successful living involves carefully navigating rewarding and threatening situations by balancing approach and avoidance behaviors. Excessive avoidance to evade potential threats often leads to forfeiting potential rewards. However, little is known about how reward and threat information is integrated neurally to inform approach or avoidance. In this preregistered study, participants (N behavior = 31, 17F; N MRI = 28, 15F) made approach-avoidance decisions under varying reward (monetary gains) and threat (electrical stimulations) prospects during functional MRI scanning. In contrast to theorized parallel subcortical processing of reward and threat information before cortical integration, Bayesian multivariate multilevel analyses revealed subcortical reward and threat integration prior to indicating approach-avoidance decisions. This integration occurred in the ventral striatum, thalamus, and bed nucleus of the stria terminalis (BNST). When reward was low, risk-diminishing avoidance decisions dominated, which was linked to more positive tracking of threat magnitude prior to indicating avoidance than approach decisions. In contrast, the amygdala exhibited dual sensitivity to reward and threat. While anticipating outcomes of risky approach decisions, we observed positive tracking of threat magnitude within the salience network (dorsal anterior cingulate cortex, thalamus, periaqueductal gray, BNST). Conversely, after risk-diminishing avoidance, characterized by reduced reward prospects, we observed more negative tracking of reward magnitude in the ventromedial prefrontal cortex and ventral striatum. These findings shed light on the temporal dynamics of approach-avoidance decision-making. Importantly, they demonstrate the role of subcortical integration of reward and threat information in balancing approach and avoidance, challenging theories positing predominantly separate subcortical processing prior to cortical integration.

Inferring neurocognition using artificial intelligence on brain MRIs

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition. A core mystery is the MRI explanation of individual differences in neurocognition and its manifestation in intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation must be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on artificial intelligence inferring human neurocognition.

Identifying brain targets for real-time fMRI neurofeedback in chronic pain: insights from functional neurosurgery

Background

The lack of clearly defined neuromodulation targets has contributed to the inconsistent results of real-time fMRI-based neurofeedback (rt-fMRI-NF) for the treatment of chronic pain. Functional neurosurgery (funcSurg) approaches have shown more consistent effects in reducing pain in patients with severe chronic pain.

Objective

This study aims to redefine rt-fMRI-NF targets for chronic pain management informed by funcSurg studies.

Methods

Based on independent systematic reviews, we identified the neuromodulation targets of the rt-fMRI-NF (in acute and chronic pain) and funcSurg (in chronic pain) studies. We then characterized the underlying functional networks using a subsample of the 7 T resting-state fMRI dataset from the Human Connectome Project. Principal component analyses (PCA) were used to identify dominant patterns (accounting for a cumulative explained variance >80%) within the obtained functional maps, and the overlap between these PCA maps and canonical intrinsic brain networks (default, salience, and sensorimotor) was calculated using a null map approach.

Results

The anatomical targets used in rt-fMRI-NF and funcSurg approaches are largely distinct, with the middle cingulate cortex as a common target. Within the investigated canonical rs-fMRI networks, these approaches exhibit both divergent and overlapping functional connectivity patterns. Specifically, rt-fMRI-NF approaches primarily target the default mode network (P value range 0.001-0.002) and the salience network (P = 0.002), whereas funcSurg approaches predominantly target the salience network (P = 0.001) and the sensorimotor network (P value range 0.001-0.023).

Conclusion

Key hubs of the salience and sensorimotor networks may represent promising targets for the therapeutic application of rt-fMRI-NF in chronic pain.

Multimodal state-dependent connectivity analysis of arousal and autonomic centers in the brainstem and basal forebrain

Vigilance is a continuously altering state of cortical activation that influences cognition and behavior and is disrupted in multiple brain pathologies. Neuromodulatory nuclei in the brainstem and basal forebrain are implicated in arousal regulation and are key drivers of widespread neuronal activity and communication. However, it is unclear how their large-scale brain network architecture changes across dynamic variations in vigilance state (i.e., alertness and drowsiness). In this study, we leverage simultaneous EEG and 3T multi-echo functional magnetic resonance imaging (fMRI) to elucidate the vigilance-dependent connectivity of arousal regulation centers in the brainstem and basal forebrain. During states of low vigilance, most of the neuromodulatory nuclei investigated here exhibit a stronger global correlation pattern and greater connectivity to the thalamus, precuneus, and sensory and motor cortices. In a more alert state, the nuclei exhibit the strongest connectivity to the salience, default mode, and auditory networks. These vigilance-dependent correlation patterns persist even after applying multiple preprocessing strategies to reduce systemic vascular effects. To validate our findings, we analyze two large 3T and 7T fMRI datasets from the Human Connectome Project and demonstrate that the static and vigilance-dependent connectivity profiles of the arousal nuclei are reproducible across 3T multi-echo, 3T single-echo, and 7T single-echo fMRI modalities. Overall, this work provides novel insights into the role of neuromodulatory systems in vigilance-related brain activity.