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

Reproducibility and Sensitivity of Resting-State fMRI in Patients With Parkinson's Disease Using Cross Validation-Based Data Censoring

BACKGROUND

Uncontrollable body movements are typical symptoms of Parkinson's disease (PD), which results in inconsistent findings regarding resting-state functional connectivity (rsFC) networks, especially for group difference clusters. Systematically identifying the motion-associated data was highly demanded.

PURPOSE

To determine data censoring criteria using a quantitative cross validation-based data censoring (CVDC) method and to improve the detection of rsFC deficits in PD.

STUDY TYPE

Prospective.

SUBJECTS

Forty-one PD patients (68.63 ± 9.17 years, 44% female) and 20 healthy controls (66.83 ± 12.94 years, 55% female).

FIELD STRENGTH/SEQUENCE

3-T, T1-weighted gradient echo and EPI sequences.

ASSESSMENT

Clusters with significant differences between groups were found in three visual networks, default network, and right sensorimotor network. Five-fold cross-validation tests were performed using multiple motion exclusion criteria, and the selected criteria were determined based on cluster sizes, significance values, and Dice coefficients among the cross-validation tests. As a reference method, whole brain rsFC comparisons between groups were analyzed using a FMRIB Software Library (FSL) pipeline with default settings.

STATISTICAL TESTS

Group difference clusters were calculated using nonparametric permutation statistics of FSL-randomize. The family-wise error was corrected. Demographic information was evaluated using independent sample t-tests and Pearson's Chi-squared tests. The level of statistical significance was set at P < 0.05.

RESULTS

With the FSL processing pipeline, the mean Dice coefficient of the network clusters was 0.411, indicating a low reproducibility. With the proposed CVDC method, motion exclusion criteria were determined as frame-wise displacement >0.55 mm. Group-difference clusters showed a mean P-value of 0.01 and a 72% higher mean Dice coefficient compared to the FSL pipeline. Furthermore, the CVDC method was capable of detecting subtle rsFC deficits in the medial sensorimotor network and auditory network that were unobservable using the conventional pipeline.

DATA CONCLUSION

The CVDC method may provide superior sensitivity and improved reproducibility for detecting rsFC deficits in PD.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Mapping brain metabolism, connectivity and neurotransmitters topography in early and late onset dementia with lewy bodies

INTRODUCTION

Early-onset dementia with Lewy bodies (EO-DLB) is associated with rapid cognitive decline and severe neuropsychiatric symptoms at onset.

METHODS

Using FDG-PET imaging for 62 patients (21 EO-DLB, 41 LO (late-onset)-DLB), we explored brain hypometabolism, and metabolic connectivity in the whole-brain network and resting-state networks (RSNs). We also evaluated the spatial association between brain hypometabolism and neurotransmitter pathways topography.

RESULTS

Direct comparisons between the two clinical subgroups showed that EO-DLB was characterized by a lower metabolism in posterior cingulate/precuneus and occipital cortex. Metabolic connectivity analysis revealed significant alterations in posterior regions in both EO-DLB and LO-DLB. The EO-DLB, however, showed more severe loss of connectivity between occipital and parietal nodes and hyperconnectivity between frontal and cerebellar nodes. Spatial topography association analysis indicated significant correlations between neurotransmitter maps (i.e. acetylcholine, GABA, serotonin, dopamine) and brain hypometabolism in both EO and LO-DLB, with significantly higher metabolic correlation in the presynaptic serotonergic system for EO-DLB, supporting its major dysfunction.

CONCLUSIONS

Our study revealed greater brain hypometabolism and loss of connectivity in posterior brain region in EO- than LO-DLB. Serotonergic mapping emerges as a relevant factor for further investigation addressing clinical differences between DLB subtypes.

Resting state networks of awake adolescent and adult squirrel monkeys using ultra-high field (9.4T) functional magnetic resonance imaging

Resting state networks (RSNs) are increasingly forwarded as candidate biomarkers for neuropsychiatric disorders. Such biomarkers may provide objective measures for evaluating novel therapeutic interventions in nonhuman primates often used in translational neuroimaging research. This study aimed to characterize the RSNs of awake squirrel monkeys and compare the characteristics of those networks in adolescent and adult subjects. Twenty-seven squirrel monkeys (n=12 adolescents [6 male/6 female] ∼2.5 years and n=15 adults [7 male/8 female] ∼9.5 years) were gradually acclimated to awake scanning procedures; whole-brain fMRI images were acquired with a 9.4 Tesla scanner. Group level independent component (ICA) analysis (30 ICs) with dual regression was used to detect and compare RSNs. Twenty ICs corresponding to physiologically meaningful networks representing a range of neural functions, including motor, sensory, reward, and cognitive processes were identified in both adolescent and adult monkeys. The reproducibility of these RSNs was evaluated across several ICA model orders. Adults showed a trend for greater connectivity compared to adolescent subjects in two of the networks of interest: (1) in the right occipital region with the OFC network and (2) in the left temporal cortex, bilateral occipital cortex, and cerebellum with the posterior cingulate network. However, when age was entered into the above model, this trend for significance was lost. These results demonstrate that squirrel monkey RSNs are stable and consistent with RSNs previously identified in humans, rodents, and other nonhuman primate species. These data also identify several networks in adolescence that are conserved and others that may change into adulthood.Significance Statement Functional magnetic resonance imaging procedures have revealed important information about how the brain is modified by experimental manipulations, disease states, and aging throughout the lifespan. Preclinical neuroimaging, especially in nonhuman primates, has become a frequently used means to answer targeted questions related to brain resting-state functional connectivity. The present study characterized resting state networks (RSNs) in adult and adolescent squirrel monkeys; twenty RSNs corresponding to networks representing a range of neural functions were identified. The RSNs identified here can be utilized in future studies examining the effects of experimental manipulations on brain connectivity in squirrel monkeys. These data also may be useful for comparative analysis with other primate species to provide an evolutionary perspective for understanding brain function and organization.

Resting-state functional connectivity in children cooled for neonatal encephalopathy

Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.

Investigating underlying brain structures and influence of mild and subjective cognitive impairment on dual-task performance in people with Parkinson's disease

Cognitive impairment can affect dual-task abilities in Parkinson's disease (PD), but it remains unclear whether this is also driven by gray matter alterations across different cognitive classifications. Therefore, we investigated associations between dual-task performance during gait and functional mobility and gray matter alterations and explored whether these associations differed according to the degree of cognitive impairment. Participants with PD were classified according to their cognitive function with 22 as mild cognitive impairment (PD-MCI), 14 as subjective cognitive impairment (PD-SCI), and 20 as normal cognition (PD-NC). Multiple regression models associated dual-task absolute and interference values of gait speed, step-time variability, and reaction time, as well as dual-task absolute and difference values for Timed Up and Go (TUG) with PD cognitive classification. We repeated these regressions including the nucleus basalis of Meynert, dorsolateral prefrontal cortex, and hippocampus. We additionally explored whole-brain regressions with dual-task measures to identify dual-task-related regions. There was a trend that cerebellar alterations were associated with worse TUG dual-task in PD-SCI, but also with higher dual-task gait speed and higher dual-task step-time variability in PD-NC. After multiple comparison corrections, no effects of interest were significant. In summary, no clear set of variables associated with dual-task performance was found that distinguished between PD cognitive classifications in our cohort. Promising but non-significant trends, in particular regarding the TUG dual-task, do however warrant further investigation in future large-scale studies.

Remapping and Reconnecting the Language Network after Stroke

Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

BACKGROUND

The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed.

METHODS

We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition.

RESULTS

Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity.

CONCLUSIONS

Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.

Neuroimaging Findings in US Government Personnel and Their Family Members Involved in Anomalous Health Incidents

Importance

US government personnel stationed internationally have reported anomalous health incidents (AHIs), with some individuals experiencing persistent debilitating symptoms.

Objective

To assess the potential presence of magnetic resonance imaging (MRI)-detectable brain lesions in participants with AHIs, with respect to a well-matched control group.

Design, Setting, and Participants

This exploratory study was conducted at the National Institutes of Health (NIH) Clinical Center and the NIH MRI Research Facility between June 2018 and November 2022. Eighty-one participants with AHIs and 48 age- and sex-matched control participants, 29 of whom had similar employment as the AHI group, were assessed with clinical, volumetric, and functional MRI. A high-quality diffusion MRI scan and a second volumetric scan were also acquired during a different session. The structural MRI acquisition protocol was optimized to achieve high reproducibility. Forty-nine participants with AHIs had at least 1 additional imaging session approximately 6 to 12 months from the first visit.

Exposure

AHIs.

Main Outcomes and Measures

Group-level quantitative metrics obtained from multiple modalities: (1) volumetric measurement, voxel-wise and region of interest (ROI)-wise; (2) diffusion MRI-derived metrics, voxel-wise and ROI-wise; and (3) ROI-wise within-network resting-state functional connectivity using functional MRI. Exploratory data analyses used both standard, nonparametric tests and bayesian multilevel modeling.

Results

Among the 81 participants with AHIs, the mean (SD) age was 42 (9) years and 49% were female; among the 48 control participants, the mean (SD) age was 43 (11) years and 42% were female. Imaging scans were performed as early as 14 days after experiencing AHIs with a median delay period of 80 (IQR, 36-544) days. After adjustment for multiple comparisons, no significant differences between participants with AHIs and control participants were found for any MRI modality. At an unadjusted threshold (P < .05), compared with control participants, participants with AHIs had lower intranetwork connectivity in the salience networks, a larger corpus callosum, and diffusion MRI differences in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior cerebellar peduncle, and amygdala. The structural MRI measurements were highly reproducible (median coefficient of variation <1% across all global volumetric ROIs and <1.5% for all white matter ROIs for diffusion metrics). Even individuals with large differences from control participants exhibited stable longitudinal results (typically, <±1% across visits), suggesting the absence of evolving lesions. The relationships between the imaging and clinical variables were weak (median Spearman ρ = 0.10). The study did not replicate the results of a previously published investigation of AHIs.

Conclusions and Relevance

In this exploratory neuroimaging study, there were no significant differences in imaging measures of brain structure or function between individuals reporting AHIs and matched control participants after adjustment for multiple comparisons.

Dual-task gait training improves cognition and resting-state functional connectivity in Parkinson's disease with postural instability and gait disorders

OBJECTIVES

To assess whether dual-task gait/balance training with action observation training (AOT) and motor imagery (MI) ameliorates cognitive performance and resting-state (RS) brain functional connectivity (FC) in Parkinson's disease (PD) patients with postural instability and gait disorders (PIGD).

METHODS

21 PD-PIGD patients were randomized into 2 groups: (1) DUAL-TASK + AOT-MI group performed a 6-week training consisting of AOT-MI combined with practicing observed-imagined gait and balance exercises; and (2) DUAL-TASK group performed the same exercises combined with landscape-videos observation. At baseline and after training, all patients underwent a computerized cognitive assessment, while 17 patients had also RS-fMRI scans. Cognitive and RS-FC changes (and their relationships) over time within and between groups were assessed.

RESULTS

After training, all PD-PIGD patients improved accuracy in a test assessing executive-attentive (mainly dual-task) skills. DUAL-TASK + AOT-MI patients showed increased RS-FC within the anterior salience network (aSAL), and reduced RS-FC within the anterior default mode network (aDMN), right executive control network and precuneus network. DUAL-TASK patients showed increased RS-FC within the visuospatial network, only. Group × Time interaction showed that, compared to DUAL-TASK group, DUAL-TASK + AOT-MI cases had reduced RS-FC within the aDMN, which correlated with higher accuracy in a dual-task executive-attentive test.

CONCLUSIONS

In PD-PIGD patients, both trainings promote cognitive improvement and brain functional reorganization. DUAL-TASK + AOT-MI training induced specific functional reorganization changes of extra-motor brain networks, which were related with improvement in dual-task performance.

Altered functional brain networks in problematic smartphone and social media use: resting-state fMRI study

Nowadays, the limitless availability to the World Wide Web can lead to general Internet misuse and dependence. Currently, smartphone and social media use belong to the most prevalent Internet-related behavioral addiction forms. However, the neurobiological background of these Internet-related behavioral addictions is not sufficiently explored. In this study, these addiction forms were assessed with self-reported questionnaires. Resting-state functional magnetic resonance imaging was acquired for all participants (n = 59, 29 males) to examine functional brain networks. The resting-state networks that were discovered using independent component analysis were analyzed to estimate within network differences. Significant negative associations with social media addiction and smartphone addiction were found in the language network, the lateral visual networks, the auditory network, the sensorimotor network, the executive network and the frontoparietal network. These results suggest that problematic smartphone and social media use are associated with sensory processing and higher cognitive functioning.

Cerebellar and thalamic connector hubs facilitate the involvement of visual and cognitive networks in essential tremor

INTRODUCTION

Connector hubs are specialized brain regions that connect multiple brain networks and therefore have the potential to affect the functions of multiple systems. This study aims to examine the involvement of connector hub regions in essential tremor.

METHODS

We examined whole-brain functional connectivity alterations across multiple brain networks in 27 patients with essential tremor and 27 age- and sex-matched healthy controls to identify affected hub regions using a network metric called functional connectivity overlap ratio estimated from resting-state functional MRI. We also evaluated the relationships of affected hubs with cognitive and tremor scores in all patients and with motor function improvement scores in 15 patients who underwent postoperative follow-up evaluations after focused ultrasound thalamotomy.

RESULTS

We have identified affected connector hubs in the cerebellum and thalamus. Specifically, the dentate nucleus in the cerebellum and the dorsomedial thalamus exhibited more extensive connections with the sensorimotor network in patients. Moreover, the connections of the thalamic pulvinar with the visual network were also significantly widespread in the patient group. The connections of these connector hub regions with cognitive networks were negatively associated (FDR q < 0.05) with cognitive, tremor, and motor function improvement scores.

CONCLUSION

In patients with essential tremor, connector hub regions within the cerebellum and thalamus exhibited widespread functional connections with sensorimotor and visual networks, leading to alternative pathways outside the classical tremor axis. Their connections with cognitive networks also affect patients' cognitive function.

Dynamic Dysregulation of the Triple Network of the Brain in Mild Traumatic Brain Injury and Its Relationship With Cognitive Performance

A triple network model consisting of a default network, a salience network, and a central executive network has recently been used to understand connectivity patterns in cognitively normal versus dysfunctional brains. This study aimed to explore changes in the dynamic connectivity of triplet network in mild traumatic brain injury (mTBI) and its relationship to cognitive performance. In this work, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 30 mTBI patients and 30 healthy controls (HCs). Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Further, we analyzed the relationship between changes in dynamic functional connectivity (FC) parameters and clinical variables in mTBI patients. The results showed that the dynamic functional connectivity of the brain triple network was clustered into five states. Compared with HC, mTBI patients spent longer in state 1, which is characterized by weakened dorsal default mode network (DMN) and anterior salience network (SN) connectivity, and state 3, which is characterized by a positive correlation between DMN and SN internal connectivity. Mild TBI patients had fewer metastases in different states than HC patients. In addition, the mean residence time in state 1 correlated with Montreal Cognitive Assessment scores in mTBI patients; the number of transitions between states correlated with Glasgow Coma Score in mTBI patients. Taken together, our findings suggest that the dynamic properties of FC in the triple network of mTBI patients are abnormal, and provide a new perspective on the pathophysiological mechanism of cognitive impairment from the perspective of dynamic FC.

Effects of escitalopram therapy on effective connectivity among core brain networks in major depressive disorder

BACKGROUND

Patients with major depressive disorder (MDD) have abnormal functional interaction among large-scale brain networks, indicated by aberrant effective connectivity of the default mode network (DMN), salience network (SN), and dorsal attention network (DAN). However, it remains unclear whether antidepressants can normalize the altered effective connectivity in MDD.

METHODS

In this study, we collected resting-state functional magnetic resonance imaging data from 46 unmedicated patients with MDD at baseline and after 12 weeks of escitalopram treatment. We also collected data from 58 healthy controls (HCs) at the same time point with the same interval. Using spectral dynamic causal modeling and parametric empirical Bayes, we examined group differences, time effect and their interaction on the casual interactions among the regions of interest in the three networks.

RESULTS

Compared with HCs, patients with MDD showed increased positive (excitatory) connections within the DMN, decreased positive connections within the SN and DAN, decreased absolute value of negative (inhibitory) connectivity from the SN and DAN to the DMN, and decreased positive connections between the DAN and the SN. Furthermore, we found that six connections related to the DAN showed decreased group differences in effective connectivity between MDD and HCs during follow-up compared to the baseline.

CONCLUSIONS

Our findings suggest that escitalopram therapy can partly improve the disrupted effective connectivity among high-order brain functional networks in MDD. These findings deepened our understanding of the neural basis of antidepressants' effect on brain function in patients with MDD.

Investigating the different mechanisms in related neural activities: a focus on auditory perception and imagery

Neuroimaging studies have shown that the neural representation of imagery is closely related to the perception modality; however, the undeniable different experiences between perception and imagery indicate that there are obvious neural mechanism differences between them, which cannot be explained by the simple theory that imagery is a form of weak perception. Considering the importance of functional integration of brain regions in neural activities, we conducted correlation analysis of neural activity in brain regions jointly activated by auditory imagery and perception, and then brain functional connectivity (FC) networks were obtained with a consistent structure. However, the connection values between the areas in the superior temporal gyrus and the right precentral cortex were significantly higher in auditory perception than in the imagery modality. In addition, the modality decoding based on FC patterns showed that the FC network of auditory imagery and perception can be significantly distinguishable. Subsequently, voxel-level FC analysis further verified the distribution regions of voxels with significant connectivity differences between the 2 modalities. This study complemented the correlation and difference between auditory imagery and perception in terms of brain information interaction, and it provided a new perspective for investigating the neural mechanisms of different modal information representations.

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.

Targeted non-invasive brain stimulation boosts attention and modulates contralesional brain networks following right hemisphere stroke

Right hemisphere stroke patients frequently present with a combination of lateralised and non-lateralised attentional deficits characteristic of the neglect syndrome. Attentional deficits are associated with poor functional outcome and are challenging to treat, with non-lateralised deficits often persisting into the chronic stage and representing a common complaint among patients and families. In this study, we investigated the effects of non-invasive brain stimulation on non-lateralised attentional deficits in right-hemispheric stroke. In a randomised double-blind sham-controlled crossover study, twenty-two patients received real and sham transcranial Direct Current Stimulation (tDCS) whilst performing a non-lateralised attentional task. A high definition tDCS montage guided by stimulation modelling was employed to maximise current delivery over the right dorsolateral prefrontal cortex, a key node in the vigilance network. In a parallel study, we examined brain network response to this tDCS montage by carrying out concurrent fMRI during stimulation in healthy participants and patients. At the group level, stimulation improved target detection in patients, reducing overall error rate when compared with sham stimulation. TDCS boosted performance throughout the duration of the task, with its effects briefly outlasting stimulation cessation. Exploratory lesion analysis indicated that response to stimulation was related to lesion location rather than volume. In particular, reduced stimulation response was associated with damage to the thalamus and postcentral gyrus. Concurrent stimulation-fMRI revealed that tDCS did not affect local connectivity but influenced functional connectivity within large-scale networks in the contralesional hemisphere. This combined behavioural and functional imaging approach shows that brain stimulation targeted to surviving tissue in the ipsilesional hemisphere improves non-lateralised attentional deficits following stroke. This effect may be exerted via contralesional network effects.

Modeling brain network flexibility in networks of coupled oscillators: a feasibility study

Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.

Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole-brain analysis

Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating-based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time-resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.

Abnormal characteristic static and dynamic functional network connectivity in idiopathic normal pressure hydrocephalus

AIMS

Idiopathic Normal pressure hydrocephalus (iNPH) is a neurodegenerative disease characterized by gait disturbance, dementia, and urinary dysfunction. The neural network mechanisms underlying this phenomenon is currently unknown.

METHODS

To investigate the resting-state functional connectivity (rs-FC) abnormalities of iNPH-related brain connectivity from static and dynamic perspectives and the correlation of these abnormalities with clinical symptoms before and 3-month after shunt. We investigated both static and dynamic functional network connectivity (sFNC and dFNC, respectively) in 33 iNPH patients and 23 healthy controls (HCs).

RESULTS

The sFNC and dFNC of networks were generally decreased in iNPH patients. The reduction in sFNC within the default mode network (DMN) and between the somatomotor network (SMN) and visual network (VN) were related to symptoms. The temporal properties of dFNC and its temporal variability in state-4 were sensitive to the identification of iNPH and were correlated with symptoms. The temporal variability in the dorsal attention network (DAN) increased, and the average instantaneous FC was altered among networks in iNPH. These features were partially associated with clinical symptoms.

CONCLUSION

The dFNC may be a more sensitive biomarker for altered network function in iNPH, providing us with extra information on the mechanisms of iNPH.

Changes in Resting-State Networks in Children with Growth Hormone Deficiency

Purpose: Growth hormone deficiency (GHD) refers to the partial or complete lack of growth hormone. Short stature and slow growth are characteristic of patients with GHD. Previous neuroimaging studies have suggested that GHD may cause cognitive and behavioral impairments in patients. Resting-state networks (RSNs) are regions of the brain that exhibit synchronous activity and are closely related to our cognition and behavior. Therefore, the purpose of the current study was to explore cognitive and behavioral abnormalities in children with GHD by investigating changes in RSNs. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 26 children with GHD and 15 healthy controls (HCs) were obtained. Independent component analysis was used to identify seven RSNs from rs-fMRI data. Group differences in RSNs were estimated using two-sample t-tests. Correlation analysis was employed to investigate the associations among the areas of difference and clinical measures. Results: Compared with HCs, children with GHD had significant differences in the salience network (SN), default mode network (DMN), language network (LN), and sensorimotor network (SMN). Moreover, within the SN, the functional connectivity (FC) value of the right posterior supramarginal gyrus was negatively correlated with the adrenocorticotropic hormone and the FC value of the left anterior inferior parietal gyrus was positively correlated with insulin-like growth factor 1. Conclusions: These results suggest that alterations in RSNs may account for abnormal cognition and behavior in children with GHD, such as decreased motor function, language withdrawal, anxiety, and social anxiety. These findings provide neuroimaging support for uncovering the pathophysiological mechanisms of GHD in children. Impact statement Children with growth hormone deficiency (GHD) generally experience cognitive and behavioral abnormalities. However, there are few neuroimaging studies on children with GHD. Moreover, prior research has not investigated the aberrant brain function in patients with GHD from the perspective of brain functional networks. Therefore, this study employed the independent component analysis method to investigate alterations within seven commonly observed resting-state networks due to GHD. The results showed that children with GHD had significant differences in the salience network, default mode network, language network, and sensorimotor network. This provides neuroimaging support for revealing the pathophysiological mechanisms of GHD in children.

Resting state functional connectivity in dependent cannabis users: The moderating role of cannabis attitudes

BACKGROUND

The global increase in lenient cannabis policy has been paralleled by reduced harm perception, which has been associated with cannabis use initiation and persistent use. However, it is unclear how cannabis attitudes might affect the brain processes underlying cannabis use.

METHODS

Resting state functional connectivity (RSFC) within and between the executive control network (ECN), salience network (SN), and default mode network (DMN) was assessed in 110 near-daily cannabis users with cannabis use disorder (CUD) and 79 controls from The Netherlands and Texas, USA. Participants completed a questionnaire assessing the perceived benefits and harms of cannabis use from their personal, friends-family's, and country-state's perspectives and reported on their cannabis use (gram/week), CUD severity, and cannabis-related problems.

RESULTS

RSFC within the dorsal SN was lower in cannabis users than controls, while no group differences in between-network RSFC were observed. Furthermore, heavier cannabis use was associated with lower dorsal SN RSFC in the cannabis group. Perceived benefits and harms of cannabis - from personal, friends-family's, and country-state's perspectives - moderated associations of cannabis use, CUD severity, and cannabis use-related problems with within-network RSFC of the SN, ECN, and DMN. Personal perceived benefits and country-state perceived harms moderated the association between CUD severity and RSFC between the ventral and dorsal DMN.

CONCLUSIONS

This study highlights the importance of considering individual differences in the perceived harms and benefits of cannabis use as a factor in the associations between brain functioning and cannabis use, CUD severity, and cannabis use-related problems.

Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study

BACKGROUND

Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy.

METHODS

Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN.

RESULTS

Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables.

CONCLUSIONS

Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.

Dynamic functional network connectivity analysis in schizophrenia based on a spatiotemporal CPD framework

Objective.Dynamic functional network connectivity (dFNC), based on data-driven group independent component (IC) analysis, is an important avenue for investigating underlying patterns of certain brain diseases such as schizophrenia. Canonical polyadic decomposition (CPD) of a higher-way dynamic functional connectivity tensor, can offer an innovative spatiotemporal framework to accurately characterize potential dynamic spatial and temporal fluctuations. Since multi-subject dFNC data from sliding-window analysis are also naturally a higher-order tensor, we propose an innovative sparse and low-rank CPD (SLRCPD) for the three-way dFNC tensor to excavate significant dynamic spatiotemporal aberrant changes in schizophrenia.Approach.The proposed SLRCPD approach imposes two constraints. First, the L1regularization on spatial modules is applied to extract sparse but significant dynamic connectivity and avoid overfitting the model. Second, low-rank constraint is added on time-varying weights to enhance the temporal state clustering quality. Shared dynamic spatial modules, group-specific dynamic spatial modules and time-varying weights can be extracted by SLRCPD. The strength of connections within- and between-IC networks and connection contribution are proposed to inspect the spatial modules. K-means clustering and classification are further conducted to explore temporal group difference.Main results.82 subject resting-state functional magnetic resonance imaging (fMRI) dataset and opening Center for Biomedical Research Excellence (COBRE) schizophrenia dataset both containing schizophrenia patients (SZs) and healthy controls (HCs) were utilized in our work. Three typical dFNC patterns between different brain functional regions were obtained. Compared to the spatial modules of HCs, the aberrant connections among auditory network, somatomotor, visual, cognitive control and cerebellar networks in 82 subject dataset and COBRE dataset were detected. Four temporal states reveal significant differences between SZs and HCs for these two datasets. Additionally, the accuracy values for SZs and HCs classification based on time-varying weights are larger than 0.96.Significance.This study significantly excavates spatio-temporal patterns for schizophrenia disease.

A neural signature for the subjective experience of threat anticipation under uncertainty

Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.

Similarity and difference in large-scale functional network alternations between behavioral addictions and substance use disorder: a comparative meta-analysis

Behavioral addiction (BA) and substance use disorder (SUD) share similarities and differences in clinical symptoms, cognitive functions, and behavioral attributes. However, little is known about whether and how functional networks in the human brain manifest commonalities and differences between BA and SUD. Voxel-wise meta-analyses of resting-state functional connectivity (rs-FC) were conducted in BA and SUD separately, followed by quantitative conjunction analyses to identify the common and distinct alterations across both the BA and SUD groups. A total of 92 datasets with 2444 addicted patients and 2712 healthy controls (HCs) were eligible for the meta-analysis. Our findings demonstrated that BA and SUD exhibited common alterations in rs-FC between frontoparietal network (FPN) and other high-level neurocognitive networks (i.e. default mode network (DMN), affective network (AN), and salience network (SN)) as well as hyperconnectivity between SN seeds and the Rolandic operculum in SSN. In addition, compared with BA, SUD exhibited several distinct within- and between-network rs-FC alterations mainly involved in the DMN and FPN. Further, altered within- and between-network rs-FC showed significant association with clinical characteristics such as the severity of addiction in BA and duration of substance usage in SUD. The common rs-FC alterations in BA and SUD exhibited the relationship with consistent aberrant behaviors in both addiction groups, such as impaired inhibition control and salience attribution. By contrast, the distinct rs-FC alterations might suggest specific substance effects on the brain neural transmitter systems in SUD.

Non-alcoholic fatty liver disease is associated with brain function disruption in type 2 diabetes patients without cognitive impairment

AIMS

To investigate the neural static and dynamic intrinsic activity of intra-/inter-network topology among patients with type 2 diabetes (T2D) with non-alcoholic fatty liver disease (NAFLD) and those without NAFLD (T2NAFLD group and T2noNAFLD group, respectively) and to assess the relationship with metabolism.

METHODS

Fifty-six patients with T2NAFLD, 78 with T2noNAFLD, and 55 healthy controls (HCs) were recruited to the study. Participants had normal cognition and underwent functional magnetic resonance imaging scans, clinical measurements, and global cognition evaluation. Independent component analysis was used to identify frequency spectrum parameters, static functional network connectivity, and temporal properties of dynamic functional network connectivity (P < 0.05, false discovery rate-corrected). Statistical analysis involved one-way analysis of covariance with post hoc, partial correlation and canonical correlation analyses.

RESULTS

Our findings showed that: (i) T2NAFLD patients had more disordered glucose and lipid metabolism, had more severe insulin resistance, and were more obese than T2noNAFLD patients; (ii) T2D patients exhibited disrupted brain function, as evidenced by alterations in intra-/inter-network topology, even without clinically measurable cognitive impairment; (iii) T2NAFLD patients had more significant reductions in the frequency spectrum parameters of cognitive executive and visual networks than those with T2noNAFLD; and (iv) altered brain function in T2D patients was correlated with postprandial glucose, high-density lipoprotein cholesterol, and waist-hip ratio.

CONCLUSION

This study may provide novel insights into neuroimaging correlates for underlying pathophysiological processes inducing brain damage in T2NAFLD. Thus, controlling blood glucose levels, lipid levels and abdominal obesity may reduce brain damage risk in such patients.

Functional neuroimaging and behavioral correlates of multisite tDCS as an add-on to language training in a person with post-stroke non-fluent aphasia: a year-long case study

Mary, who experienced non-fluent aphasia as a result of an ischemic stroke, received 10 years of personalized language training (LT), resulting in transient enhancements in speech and comprehension. To enhance these effects, multisite transcranial Direct Current Stimulation (tDCS) was added to her LT regimen for 15 sessions. Assessment using the Reliable Change Index showed that this combination improved her left inferior frontal connectivity and speech production for two months and significantly improved comprehension after one month. The results indicate that using multisite transcranial direct current stimulation (tDCS) can improve the effectiveness of language therapy (LT) for individuals with non-fluent aphasia.

Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling

Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Top-down modulation of DLPFC in visual search: a study based on fMRI and TMS

Effective visual search is essential for daily life, and attention orientation as well as inhibition of return play a significant role in visual search. Researches have established the involvement of dorsolateral prefrontal cortex in cognitive control during selective attention. However, neural evidence regarding dorsolateral prefrontal cortex modulates inhibition of return in visual search is still insufficient. In this study, we employed event-related functional magnetic resonance imaging and dynamic causal modeling to develop modulation models for two types of visual search tasks. In the region of interest analyses, we found that the right dorsolateral prefrontal cortex and temporoparietal junction were selectively activated in the main effect of search type. Dynamic causal modeling results indicated that temporoparietal junction received sensory inputs and only dorsolateral prefrontal cortex →temporoparietal junction connection was modulated in serial search. Such neural modulation presents a significant positive correlation with behavioral reaction time. Furthermore, theta burst stimulation via transcranial magnetic stimulation was utilized to modulate the dorsolateral prefrontal cortex region, resulting in the disappearance of the inhibition of return effect during serial search after receiving continuous theta burst stimulation. Our findings provide a new line of causal evidence that the top-down modulation by dorsolateral prefrontal cortex influences the inhibition of return effect during serial search possibly through the retention of inhibitory tagging via working memory storage.

Altered dynamic functional and effective connectivity in drug-naive children with Tourette syndrome

Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by repetitive, stereotyped, involuntary tics, the neurological basis of which remains unclear. Although traditional resting-state MRI (rfMRI) studies have identified abnormal static functional connectivity (FC) in patients with TS, dynamic FC (dFC) remains relatively unexplored. The rfMRI data of 54 children with TS and 46 typically developing children (TDC) were analyzed using group independent component analysis to obtain independent components (ICs), and a sliding-window approach to generate dFC matrices. All dFC matrices were clustered into two reoccurring states, the state transition metrics were obtained. We conducted Granger causality and nodal topological analyses to further investigate the brain regions that may play the most important roles in driving whole-brain switching between different states. We found that children with TS spent more time in state 2 (PFDR < 0.001), a state characterized by strong connectivity between ICs, and switched more quickly between states (PFDR = 0.025) than TDC. The default mode network (DMN) may play an important role in abnormal state transitions because the FC that changed the most between the two states was between the DMN and other networks. Additionally, the DMN had increased degree centrality, efficiency and altered causal influence on other networks. Certain alterations related to executive function (r = -0.309, P < 0.05) and tic symptom ratings (r = 0.282; 0.413, P < 0.05) may represent important aspects of the pathophysiology of TS. These findings facilitate our understanding of the neural basis for the clinical presentation of TS.

Neurocognitive correlates of semantic memory navigation in Parkinson's disease

Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.

No changes in triple network engagement following (combined) noradrenergic and glucocorticoid stimulation in healthy men

Successful recovery from stress is integral for adaptive responding to the environment. At a cellular level, this involves (slow genomic) actions of cortisol, which alter or reverse rapid effects of noradrenaline and cortisol associated with acute stress. At the network scale, stress recovery is less well understood but assumed to involve changes within salience-, executive control-, and default mode networks. To date, few studies have investigated this phase and directly tested these assumptions. Here, we present results from a double-blind, placebo-controlled, between-group paradigm (N = 165 healthy males) administering 10 mg oral yohimbine and/or 10 mg oral hydrocortisone two hours prior to resting state scanning. We found no changes in within-network connectivity of the three networks, both after single and combined drug administration. We further report the results of Bayesian parameter inference to provide evidence for the null hypothesis. Our results contrast with previous findings, which may be attributable to systematic differences between paradigms, highlighting the need to isolate paradigm-specific effects from those related to stress.

Reconfigurations of Dynamic Functional Network Connectivity in Large-scale Brain Network after Prolonged Abstinence in Heroin Users

BACKGROUND

Brain recovery phenomenon after long-term abstinence had been reported in substance use disorders. Yet, few longitudinal studies have been conducted to observe the abnormal dynamic functional connectivity (dFNC) of large-scale brain networks and recovery after prolonged abstinence in heroin users.

OBJECTIVE

The current study will explore the brain network dynamic connection reconfigurations after prolonged abstinence in heroin users (HUs).

METHODS

The 10-month longitudinal design was carried out for 40 HUs. The 40 healthy controls (HCs) were also enrolled. Group independent component analysis (GICA) and dFNC analysis were employed to detect the different dFNC patterns of addiction-related ICNs between HUs and HCs. The temporal properties and the graph-theoretical properties were calculated. Whether the abnormalities would be reconfigured in HUs after prolonged abstinence was then investigated.

RESULTS

Based on eight functional networks extracted from GICA, four states were identified by the dFNC analysis. Lower mean dwell time and fraction rate in state4 were found for HUs, which were increased toward HCs after prolonged abstinence. In this state, HUs at baseline showed higher dFNC of RECN-aSN, aSN- aSN and dDMN-pSN, which decreased after protracted abstinence. A similar recovery phenomenon was found for the global efficiency and path length in abstinence HUs. Mean while, the abnormal dFNC strength was correlated with craving both at baseline and after abstinence.

CONCLUSION

Our longitudinal study observed the large-scale brain network reconfiguration from the dynamic perspective in HUs after prolonged abstinence and improved the understanding of the neurobiology of prolonged abstinence in HUs.

Brain network integration underpins differential susceptibility of adolescent anxiety

BACKGROUND

Parenting is a common and potent environmental factor influencing adolescent anxiety. Yet, the underlying neurobiological susceptibility signatures remain elusive. Here, we used a longitudinal twin neuroimaging study to investigate the brain network integration and its heritable relation to underpin the neural differential susceptibility of adolescent anxiety to parenting environments.

METHODS

216 twins from the Beijing Twin Study completed the parenting and anxiety assessments and fMRI scanning. We first identified the brain network integration involved in the influences of parenting at age 12 on anxiety symptoms at age 15. We then estimated to what extent heritable sensitive factors are responsible for the susceptibility of brain network integration.

RESULTS

Consistent with the differential susceptibility theory, the results showed that hypo-connectivity within the central executive network amplified the impact of maternal hostility on anxiety symptoms. A high anti-correlation between the anterior salience and default mode networks played a similar modulatory role in the susceptibility of adolescent anxiety to paternal hostility. Genetic influences (21.18%) were observed for the connectivity pattern in the central executive network.

CONCLUSIONS

Brain network integration served as a promising neurobiological signature of the differential susceptibility to adolescent anxiety. Our findings deepen the understanding of the neural sensitivity in the developing brain and can inform early identification and personalized interventions for adolescents at risk of anxiety disorders.

Resting state network connectivity is associated with cognitive flexibility performance in youth in the Adolescent Brain Cognitive Development Study

Cognitive flexibility is an executive functioning skill that develops in childhood, and when impaired, has transdiagnostic implications for psychiatric disorders. To identify how intrinsic neural architecture at rest is linked to cognitive flexibility performance, we used the data-driven method of independent component analysis (ICA) to investigate resting state networks (RSNs) and their whole-brain connectivity associated with levels of cognitive flexibility performance in children. We hypothesized differences by cognitive flexibility performance in RSN connectivity strength in cortico-striatal circuitry, which would manifest via the executive control network, right and left frontoparietal networks (FPN), salience network, default mode network (DMN), and basal ganglia network. We selected participants from the Adolescent Brain Cognitive Development (ABCD) Study who scored at the 25th, ("CF-Low"), 50th ("CF-Average"), or 75th percentiles ("CF-High") on a cognitive flexibility task, were early to middle puberty, and did not exhibit significant psychopathology (n = 967, 47.9% female; ages 9-10). We conducted whole-brain ICA, identifying 14 well-characterized RSNs. Groups differed in connectivity strength in the right FPN, anterior DMN, and posterior DMN. Planned comparisons indicated CF-High had stronger connectivity between right FPN and supplementary motor/anterior cingulate than CF-Low. CF-High had more anti-correlated connectivity between anterior DMN and precuneus than CF-Average. CF-Low had stronger connectivity between posterior DMN and supplementary motor/anterior cingulate than CF-Average. Post-hoc correlations with reaction time by trial type demonstrated significant associations with connectivity. In sum, our results suggest childhood cognitive flexibility performance is associated with DMN and FPN connectivity strength at rest, and that there may be optimal levels of connectivity associated with task performance that vary by network.

Evidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks

The two hemispheres of the human brain are functionally asymmetric. At the network level, the language network exhibits left-hemisphere lateralization. While this asymmetry is widely replicated, the extent to which other functional networks demonstrate lateralization remains a subject of Investigation. Additionally, it is unknown how the lateralization of one functional network may affect the lateralization of other networks within individuals. We quantified lateralization for each of 17 networks by computing the relative surface area on the left and right cerebral hemispheres. After examining the ecological, convergent, and external validity and test-retest reliability of this surface area-based measure of lateralization, we addressed two hypotheses across multiple datasets (Human Connectome Project = 553, Human Connectome Project-Development = 343, Natural Scenes Dataset = 8). First, we hypothesized that networks associated with language, visuospatial attention, and executive control would show the greatest lateralization. Second, we hypothesized that relationships between lateralized networks would follow a dependent relationship such that greater left-lateralization of a network would be associated with greater right-lateralization of a different network within individuals, and that this pattern would be systematic across individuals. A language network was among the three networks identified as being significantly left-lateralized, and attention and executive control networks were among the five networks identified as being significantly right-lateralized. Next, correlation matrices, an exploratory factor analysis, and confirmatory factor analyses were used to test the second hypothesis and examine the organization of lateralized networks. We found general support for a dependent relationship between highly left- and right-lateralized networks, meaning that across subjects, greater left lateralization of a given network (such as a language network) was linked to greater right lateralization of another network (such as a ventral attention/salience network) and vice versa. These results further our understanding of brain organization at the macro-scale network level in individuals, carrying specific relevance for neurodevelopmental conditions characterized by disruptions in lateralization such as autism and schizophrenia.

Altered dynamic functional network connectivity in drug-naïve Parkinson's disease patients with excessive daytime sleepiness

Background

Excessive daytime sleepiness (EDS) is a frequent nonmotor symptoms of Parkinson's disease (PD), which seriously affects the quality of life of PD patients and exacerbates other nonmotor symptoms. Previous studies have used static analyses of these resting-state functional magnetic resonance imaging (rs-fMRI) data were measured under the assumption that the intrinsic fluctuations during MRI scans are stationary. However, dynamic functional network connectivity (dFNC) analysis captures time-varying connectivity over short time scales and may reveal complex functional tissues in the brain.

Purpose

To identify dynamic functional connectivity characteristics in PD-EDS patients in order to explain the underlying neuropathological mechanisms.

Methods

Based on rs-fMRI data from 16 PD patients with EDS and 41 PD patients without EDS, we applied the sliding window approach, k-means clustering and independent component analysis to estimate the inherent dynamic connectivity states associated with EDS in PD patients and investigated the differences between groups. Furthermore, to assess the correlations between the altered temporal properties and the Epworth sleepiness scale (ESS) scores.

Results

We found four distinct functional connectivity states in PD patients. The patients in the PD-EDS group showed increased fractional time and mean dwell time in state IV, which was characterized by strong connectivity in the sensorimotor (SMN) and visual (VIS) networks, and reduced fractional time in state I, which was characterized by strong positive connectivity intranetwork of the default mode network (DMN) and VIS, while negative connectivity internetwork between the DMN and VIS. Moreover, the ESS scores were positively correlated with fraction time in state IV.

Conclusion

Our results indicated that the strong connectivity within and between the SMN and VIS was characteristic of EDS in PD patients, which may be a potential marker of pathophysiological features related to EDS in PD patients.

A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression

Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.

Neural mechanisms of emotional health in traumatic brain injury patients undergoing rTMS treatment

Emotional dysregulation such as that seen in depression, are a long-term consequence of mild traumatic brain injury (TBI), that can be improved by using neuromodulation treatments such as repetitive transcranial magnetic stimulation (rTMS). Previous studies provide insights into the changes in functional connectivity related to general emotional health after the application of rTMS procedures in patients with TBI. However, these studies provide little understanding of the underlying neuronal mechanisms that drive the improvement of the emotional health in these patients. The current study focuses on inferring the effective (causal) connectivity changes and their association with emotional health, after rTMS treatment of cognitive problems in TBI patients (N = 32). Specifically, we used resting state functional magnetic resonance imaging (fMRI) together with spectral dynamic causal model (spDCM) to investigate changes in brain effective connectivity, before and after the application of high frequency (10 Hz) rTMS over left dorsolateral prefrontal cortex. We investigated the effective connectivity of the cortico-limbic network comprised of 11 regions of interest (ROIs) which are part of the default mode, salience, and executive control networks, known to be implicated in emotional processing. The results indicate that overall, among extrinsic connections, the strength of excitatory connections decreased while that of inhibitory connections increased after the neuromodulation. The cardinal region in the analysis was dorsal anterior cingulate cortex (dACC) which is considered to be the most influenced during emotional health disorders. Our findings implicate the altered connectivity of dACC with left anterior insula and medial prefrontal cortex, after the application of rTMS, as a potential neural mechanism underlying improvement of emotional health. Our investigation highlights the importance of these brain regions as treatment targets in emotional processing in TBI.

Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals

An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.

Neural patterns differentiate traumatic from sad autobiographical memories in PTSD

For people with post-traumatic stress disorder (PTSD), recall of traumatic memories often displays as intrusions that differ profoundly from processing of 'regular' negative memories. These mnemonic features fueled theories speculating a unique cognitive state linked with traumatic memories. Yet, to date, little empirical evidence supports this view. Here we examined neural activity of patients with PTSD who were listening to narratives depicting their own memories. An intersubject representational similarity analysis of cross-subject semantic content and neural patterns revealed a differentiation in hippocampal representation by narrative type: semantically similar, sad autobiographical memories elicited similar neural representations across participants. By contrast, within the same individuals, semantically similar trauma memories were not represented similarly. Furthermore, we were able to decode memory type from hippocampal multivoxel patterns. Finally, individual symptom severity modulated semantic representation of the traumatic narratives in the posterior cingulate cortex. Taken together, these findings suggest that traumatic memories are an alternative cognitive entity that deviates from memory per se.

Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions

Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study

OBJECTIVE

Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine.

METHODS

We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results.

RESULTS

The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86.

INTERPRETATION

Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.

Altered dynamic functional network connectivity and topological organization variance in patients with white matter hyperintensities

White matter hyperintensities (WMHs) of presumed vascular origin are important imaging biomarkers of cerebral small vessel disease (CSVD). Previous studies have verified abnormal functional brain networks in CSVD. However, most of these studies rely on static functional connectivity, and only a few focus on the varying severity of the WMHs. Hence, our study primarily explored the disrupted dynamic functional network connectivity (dFNC) and topological organization variance in patients with WMHs. This study included 38 patients with moderate WMHs, 47 with severe WMHs, and 68 healthy controls (HCs). Ten independent components were chosen using independent component analysis based on resting-state functional magnetic resonance imaging. The dFNC of each participant was estimated using sliding windows and k-means clustering. We identified three reproducible dFNC states. Among them, patients with WMHs had a significantly higher occurrence in the sparsely connected State 1, but a lower occurrence and shorter duration in the positive and stronger connected State 3. Regarding topological organization variance, patients with WMHs showed higher variance in local efficiency but not global efficiency compared to HCs. Among the WMH subgroups, patients with severe WMHs showed similar but more obvious alterations than those with moderate WMHs. These altered network characteristics indicated an imbalance between the functional segregation and integration of brain networks, which was correlated with global cognition, memory, executive functions, and visuospatial abilities. Our study confirmed aberrant dFNC state metrics and topological organization variance in patients with moderate-to-severe WMHs; thus, it might provide a new pathway for exploring the pathogenesis of cognitive impairment.

Visuo-vestibular and cognitive connections of the vestibular neuromatrix are conserved across age and injury populations

BACKGROUND AND PURPOSE

Given the prevalence of vestibular dysfunction in pediatric concussion, there is a need to better understand pathophysiological disruptions within vestibular and associated cognitive, affective, and sensory-integrative networks. Although current research leverages established intrinsic connectivity networks, these are nonspecific for vestibular function, suggesting that a pathologically guided approach is warranted. The purpose of this study was to evaluate the generalizability of the previously identified "vestibular neuromatrix" in adults with and without postconcussive vestibular dysfunction to young athletes aged 14-17.

METHODS

This retrospective study leveraged resting-state functional MRI data from two sites. Site A included adults with diagnosed postconcussive vestibular impairment and healthy adult controls and Site B consisted of young athletes with preseason, postconcussion, and postseason time points (prospective longitudinal data). Adjacency matrices were generated from preprocessed resting-state data from each sample and assessed for overlap and network structure in MATLAB.

RESULTS

Analyses indicated the presence of a conserved "core" network of vestibular regions as well as areas subserving visual, spatial, and attentional processing. Other vestibular connections were also conserved across samples but were not linked to the "core" subnetwork by regions of interest included in this study.

CONCLUSIONS

Our results suggest that connections between central vestibular, visuospatial, and known intrinsic connectivity networks are conserved across adult and pediatric participants with and without concussion, evincing the significance of this expanded, vestibular-associated network. Our findings thus support this network as a workable model for investigation in future studies of dysfunction in young athlete populations.

Alterations in Resting-State MR Functional Connectivity of the Central Autonomic Network in Multiple System Atrophy and Relationship with Disease Severity

BACKGROUND

The central autonomic network (CAN) plays a critical role in the body's sympathetic and parasympathetic control. However, functional connectivity (FC) changes of the CAN in patients with multiple system atrophy (MSA) remain unknown.

PURPOSE

To investigate FC alterations of CAN in MSA patients.

STUDY TYPE

Prospective.

POPULATION

Eighty-two subjects (47 patients with MSA [44.7% female, 60.5 ± 6.9 years], 35 age- and sex-matched healthy controls [HC] [57.1% female, 62.5 ± 6.6 years]).

FIELD STRENGTH/SEQUENCE

3-T, resting-state functional magnetic resonance imaging (rs-fMRI) using gradient echo-planar imaging (EPI), T1-weighted three-dimensional magnetization-prepared rapid gradient echo (3D MPRAGE) structural MRI.

ASSESSMENT

FC alterations were explored by using core modulatory regions of CAN as seeds, including midcingulate cortex, insula, amygdala, and ventromedial prefrontal cortex. Bartlett factor score (BFS) derived from a factor analysis of clinical assessments on disease severity was used as a grouping factor for moderate MSA (mMSA: BFS < 0) and severe MSA (sMSA: BFS > 0).

STATISTICAL TESTS

For FC analysis, the one-way ANCOVA with cluster-level family-wise error correction (statistical significance level of P < 0.025), and post hoc t-testing with Bonferroni correction or Tamhane's T2 correction (statistical significance level of adjusted-P < 0.05) were adopted. Correlation was assessed using Pearson correlation or Spearman correlation (statistical significance level of P < 0.05).

RESULTS

Compared with HC, patients with MSA exhibited significant FC aberrances between the CAN and brain areas of sensorimotor control, limbic network, putamen, and cerebellum. For MSA patients, most FC alterations of CAN, especially concerning FC between the right anterior insula and right primary sensorimotor cortices, were found to be significantly correlated with disease severity. FC changes were found to be more significant in sMSA group than in mMSA group when compared with HCs.

DATA CONCLUSION

MSA shows widespread FC changes of CAN, suggesting that abnormal functional integration of CAN may be involved in disease pathogenesis of MSA.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

Executive function deficit in betel quid-dependence: Evidence from functional and effective connectivity of executive control network

Betel quid (BQ) ranks fourth in global self-administered psychoactive agents, after caffeine, alcohol and nicotine, with 600 million consumers. Patients with BQ dependence (BQD) disorder demonstrate deficits in executive function. However, the neural correlates of the resting-state executive control network (ECN) and BQD-related pathopsychological characteristics still remain unclear. The present study aimed to assess the functional and effective connectivity of the ECN using resting-state functional magnetic resonance imaging (rs-fMRI). Fifty-five BQD individuals and 54 healthy controls (HCs) were recruited in this study. The executive function of all participants was tested by three tasks. Independent component and Granger causal analysis were employed to investigate the functional connectivity within ECN and ECN-related directional effective connectivity, separately. Behavioural results suggested a marked deficit of executive function in BQD individuals. Compared with HCs, BQD individuals showed overall weaker functional connectivity in the ECN, mainly including dorsolateral prefrontal cortex (DLPFC), inferior parietal lobule (IPL) and middle frontal gyrus (MFG). We observed decreased outflow of information from the right DLPFC and IPL to the precentral/pre-supplement motor area (SMA) and increased outflow of information from the MFG to the middle occipital gyrus in BQD individuals. Correlation analysis revealed that the effective connectivity from IPL to precentral/pre-SMA was negatively correlated to the BQD scales in BQD individuals. Our findings revealed impaired executive function, functional connectivity of the ECN and causal interaction between networks in patients with BQD. These results could potentially direct future targets for the prevention and intervention of BQD.

High-accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different functional connectome (FC) matrix construction pipelines are compared in order to characterize the effects of different aspects of the production of FCs on the accuracy of subject and task classification, and to identify possible confounds.