Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies

In this study, we analyze magnetoencephalographic (MEG) recordings from 48 clinically healthy subjects obtained from the Human Connectome Project (HCP) while they performed a working memory task and a motor task. Our results reveal a well-developed, stable interrelation pattern that spans the entire scalp and is nearly universal, being almost task- and subject-independent. Additionally, we demonstrate that this pattern closely resembles a stationary correlation pattern (SCP) observed in EEG signals under various physiological and pathological conditions (the distribution of Pearson correlations are centered at about 0.75). Furthermore, we identify the most effective EEG reference for studying the brain's functional network derived from lag-zero cross-correlations. We contextualize these findings within the theory of complex dynamical systems operating near a critical point of a phase transition.

Molecular mechanisms underlying the neural correlates of working memory

BACKGROUND

Working memory (WM), a core component of executive functions, relies on a dedicated brain system that maintains and stores information in the short term. While extensive neuroimaging research has identified a distributed set of neural substrates relevant to WM, their underlying molecular mechanisms remain enigmatic. This study investigated the neural correlates of WM as well as their underlying molecular mechanisms.

RESULTS

Our voxel-wise analyses of resting-state functional MRI data from 502 healthy young adults showed that better WM performance (higher accuracy and shorter reaction time of the 3-back task) was associated with lower functional connectivity density (FCD) in the left inferior temporal gyrus and higher FCD in the left anterior cingulate cortex. A combination of transcriptome-neuroimaging spatial correlation and the ensemble-based gene category enrichment analysis revealed that the identified neural correlates of WM were associated with expression of diverse gene categories involving important cortical components and their biological processes as well as sodium channels. Cross-region spatial correlation analyses demonstrated significant associations between the neural correlates of WM and a range of neurotransmitters including dopamine, glutamate, serotonin, and acetylcholine.

CONCLUSIONS

These findings may help to shed light on the molecular mechanisms underlying the neural correlates of WM.

Ventral Striatum is Preferentially Correlated with the Salience Network Including Regions in Dorsolateral Prefrontal Cortex

The ventral striatum (VS) receives input from the cerebral cortex and is modulated by midbrain dopaminergic projections in support of processing reward and motivation. Here we explored the organization of cortical regions linked to the human VS using within-individual functional connectivity MRI in intensively scanned participants. In two initial participants (scanned 31 sessions each), seed regions in the VS were preferentially correlated with distributed cortical regions that are part of the Salience (SAL) network. The VS seed region recapitulated SAL network topography in each individual including anterior and posterior midline regions, anterior insula, and dorsolateral prefrontal cortex (DLPFC) - a topography that was distinct from a nearby striatal seed region. The region of DLPFC linked to the VS is positioned adjacent to regions associated with domain-flexible cognitive control. The full pattern was replicated in independent data from the same two individuals and generalized to 15 novel participants (scanned 8 or more sessions each). These results suggest that the VS forms a cortico-basal ganglia loop as part of the SAL network. The DLPFC is a neuromodulatory target to treat major depressive disorder. The present results raise the possibility that the DLPFC may be an effective neuromodulatory target because of its preferential coupling to the VS and suggests a path toward further personalization.

fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review

Alzheimer's disease is a common brain disorder affecting many people worldwide. It is the primary cause of dementia and memory loss. The early diagnosis of Alzheimer's disease is essential to provide timely care to AD patients and prevent the development of symptoms of this disease. Various non-invasive techniques can be utilized to diagnose Alzheimer's in its early stages. These techniques include functional magnetic resonance imaging, electroencephalography, positron emission tomography, and diffusion tensor imaging. They are mainly used to explore functional and structural connectivity of human brains. Functional connectivity is essential for understanding the co-activation of certain brain regions co-activation. This systematic review scrutinizes various works of Alzheimer's disease detection by analyzing the learning from functional connectivity of fMRI datasets that were published between 2018 and 2024. This work investigates the whole learning pipeline including data analysis, standard preprocessing phases of fMRI, feature computation, extraction and selection, and the various machine learning and deep learning algorithms that are used to predict the occurrence of Alzheimer's disease. Ultimately, the paper analyzed results on AD and highlighted future research directions in medical imaging. There is a need for an efficient and accurate way to detect AD to overcome the problems faced by patients in the early stages.

Temporal dynamic alterations of regional homogeneity in major depressive disorder: a study integrating machine learning

Previous studies have found alterations in the local regional homogeneity of brain activity in individuals diagnosed with major depressive disorder. However, many studies have failed to consider that even during resting states, brain activity is dynamic and time-varying. The lack of investigation into the dynamic regional homogeneity has hindered the discovery of biomarkers for depression. This study aimed to assess the utility of the dynamic regional homogeneity by a machine learning model (support vector machine). Sixty-five individuals with dynamic regional homogeneity and 57 healthy controls participated in resting-state functional magnetic resonance rescanning and scale estimating. The dynamic regional homogeneity and receiver operating characteristic curve methods were used for analysis of the imaging data. Relative to healthy controls, major depressive disorder patients displayed increased dynamic regional homogeneity values in the left precuneus and right postcentral gyrus. Additionally, receiver operating characteristic curve results of the dynamic regional homogeneity values in the left precuneus and right postcentral gyrus could distinguish major depressive disorder patients from healthy controls; furthermore, changes in the dynamic regional homogeneity were correlated with depression severity.

The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis

BACKGROUND

Individuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.

METHODS

We recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.

RESULTS

The MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.

CONCLUSION

MUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

Subject-based assessment of large-scale integration dynamics in epileptic brain networks: insights from the intrinsic ignition framework

This study examined the dynamic properties of brain regions involved in the genesis and spread of seizures in 10 individuals diagnosed with pharmacoresistant focal epilepsy. The patients and 30 healthy controls underwent resting-state functional magnetic resonance imaging scans and the brain's functional network dynamics were analyzed using the intrinsic ignition framework. Comparative statistical analyses examined the differences in the integration and metastability measures in both groups in the whole brain and specific local brain regions. Invasive electroencephalography evaluations validated the findings of significant global and regional changes in the patient's brain network dynamics. There was a marked increase in global integration and metastability across the brain, reflecting substantial alterations in the overall connectivity and flexibility of the functional networks. Specific brain regions exhibited paradoxical dynamics within the seizure onset zone, with decreased intrinsic ignition and increased metastability. Increased intrinsic ignition was observed in remote brain regions, suggesting a reorganization of the brain network hubs and potential pathways for seizure propagation. Using the intrinsic ignition framework provided insights into dynamic alterations in the brain networks of patients with epilepsy. These have increased our understanding of the mechanisms underlying epileptic seizures and may guide the development of diagnostic biomarkers and targeted therapeutic interventions.

An improved spectral clustering method for accurate detection of brain resting-state networks

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

Iron Deposition and Functional Connectivity Differences in Females With Migraine Without Aura: A Comparative Study of Headache Sides

BACKGROUND

The pathophysiological mechanisms underlying migraine without aura (MwoA) in females remain incompletely elucidated. Currently, the association between headache laterality and iron deposition (ID), and functional connectivity (FC) in female MwoA patients has not been fully studied.

METHODS

We prospectively recruited 63 female patients with MwoA and 31 matched healthy controls (HC) from the hospital. ID and FC among the four groups were analyzed using two-sample t-tests (with cluster-wise family-wise error [FWE] correction). Pearson correlation analysis was used to evaluate the relationships between clinical variables and both ID and FC values. Significance level: p < 0.05.

RESULTS

Compared to HC, left-sided MwoA exhibited differences in ID in various brain regions, including the cerebellum, left orbital inferior frontal gyrus, left calcarine gyrus, right putamen, and left caudate nucleus, as well as exhibited enhanced FC between the left lobule III of the cerebellum and the right superior temporal gyrus. Compared to bilateral MwoA, left-sided MwoA showed significantly enhanced in FC values in the left calcarine gyrus, the right precentral gyrus, the right postcentral gyrus, and the right lingual gyrus. Additionally, significant differences were observed in the Pearson correlations between clinical variables and both ID and FC in the female MwoA subgroups.

CONCLUSION

Our study provided preliminary evidence indicating significant differences in ID, FC, and correlations among subgroups of female MwoA. This provides neuroimaging references for further subclassifying MwoA patients. This offers valuable insights into potential pathophysiological mechanisms linked to the brain functional impairment in female MwoA.

Distributed network flows generate localized category selectivity in human visual cortex

A central goal of neuroscience is to understand how function-relevant brain activations are generated. Here we test the hypothesis that function-relevant brain activations are generated primarily by distributed network flows. We focused on visual processing in human cortex, given the long-standing literature supporting the functional relevance of brain activations in visual cortex regions exhibiting visual category selectivity. We began by using fMRI data from N = 352 human participants to identify category-specific responses in visual cortex for images of faces, places, body parts, and tools. We then systematically tested the hypothesis that distributed network flows can generate these localized visual category selective responses. This was accomplished using a recently developed approach for simulating - in a highly empirically constrained manner - the generation of task-evoked brain activations by modeling activity flowing over intrinsic brain connections. We next tested refinements to our hypothesis, focusing on how stimulus-driven network interactions initialized in V1 generate downstream visual category selectivity. We found evidence that network flows directly from V1 were sufficient for generating visual category selectivity, but that additional, globally distributed (whole-cortex) network flows increased category selectivity further. Using null network architectures we also found that each region's unique intrinsic "connectivity fingerprint" was key to the generation of category selectivity. These results generalized across regions associated with all four visual categories tested (bodies, faces, places, and tools), and provide evidence that the human brain's intrinsic network organization plays a prominent role in the generation of functionally relevant, localized responses.

Altered local intrinsic neural activity and molecular architecture in internet use disorders

BACKGROUND

Internet gaming disorder (IGD) is mainly characterized by its core dysfunction in higher-order brain cortices involved in inhibitory control, whose neurobiological basis remains unclear. Then, we will investigate local intrinsic neural activity (INA) alterations in IGD, ascertain whether these potential alterations are related to clinical characteristics, and further explore the underlying molecular architecture.

METHOD

In this study, we performed the fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) derived from resting-state functional magnetic resonance imaging (rs-fMRI) to explore the impact of IGD on local INA. Correlation analysis revealed the relationship between ReHo and fALFF in terms of group differences and clinical characteristics. Moreover, correlations between fALFF, ReHo, and PET- and SPECT-driven maps were investigated to elucidate the specific molecular architecture alternations in IGD. Finally, receiver operating characteristic curve (ROC) analysis was used to show the potential abilities of fALFF and ReHo in distinguishing individuals with IGD (IGDs) from healthy controls (HCs).

RESULT

Compared with HCs, IGDs revealed increased ReHo and fALFF in the prefrontal cortex. Significantly decreased ReHo was observed in the temporal lobe, occipital lobe, and cerebellum. In addition, the ReHo values in the cerebellum_7b_R were positively correlated with internet addiction severity. ROC curve analysis showed that ReHo and fALFF-altered brain regions could effectively distinguish IGDs from HCs. More importantly, cross-modal correlations revealed local INA changes in brain regions associated with the monoamine neurotransmitter system and the less studied cholinergic/GABAergic system.

CONCLUSION

These results suggest that local functional impairments are shown in the audiovisual and inhibitory control circuits in IGDs. This may be associated with underlying neurotransmitter system alterations. Therefore, this study provides the possibility of GABAergic receptor agonists and cholinergic receptor inhibitors for the treatment of IGD.

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism

BACKGROUND

Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood.

METHODS

Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors.

RESULTS

Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels.

CONCLUSIONS

This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

Thalamocortical dysconnectivity is associated with pain in patients with knee osteoarthritis

Previous studies have suggested that the morphology and function of the thalamus and cortex are abnormal in patients with knee osteoarthritis (KOA). However, whether the thalamocortical network is differentially affected in this disorder is unknown. In this study, we examined functional and effective connectivity between the thalamus and major divisions of the cortex in 27 healthy controls and 27 KOA patients using functional magnetic resonance imaging. We also explored the topological features of the brain via graph theory analysis. The results suggested that patients with KOA had significantly reduced resting-state functional connectivity (rsFC) of the thalamo-sensorimotor pathway; enhanced rsFC of the thalamo-medial/lateral frontal cortex (mFC/LFC), parietal, temporal and occipital pathways; reduced effective connectivity of the left sensorimotor-to-thalamus pathway; and enhanced effective connectivity of the right thalamus-to-sensorimotor pathway compared with healthy controls. The functional connectivity of the thalamo-sensorimotor and thalamo-mFC pathways was enhanced when patients performed the multisource interference task. Moreover, patients with KOA presented altered nodal properties associated with thalamocortical circuits, including the thalamus, amygdala, and regions in default mode networks, compared with healthy controls. The correlation analysis suggested a significant negative correlation between thalamo-mFC rsFC and pain intensity, between thalamo-sensorimotor task-related connectivity and disease duration/depression scores, and a positive correlation between right frontal nodal properties and pain intensity in KOA patients. Taken together, these findings establish abnormal and differential alterations in the thalamocortical network associated with pain characteristics in KOA patients, which extends our understanding of their role in the pathophysiology of KOA.

Dynamic functional connectivity in anorexia nervosa: alterations in states of low connectivity and state transitions

BACKGROUND

The onset of anorexia nervosa (AN) frequently occurs during adolescence and is associated with preoccupation with body weight and shape and extreme underweight. Altered resting state functional connectivity in the brain has been described in individuals with AN, but only from a static perspective. The current study investigated the temporal dynamics of functional connectivity in adolescents with AN and how it relates to clinical features.

METHOD

99 female patients acutely ill with AN and 99 pairwise age-matched female healthy control (HC) participants were included in the study. Using resting-state functional MRI data and an established sliding-window analytic approach, we identified dynamic resting-state functional connectivity states and extracted dynamic indices such as dwell time (the duration spent in a state), fraction time (the proportion of the total time occupied by a state), and number of transitions (number of switches) from one state to another, to test for group differences.

RESULTS

Individuals with AN had relatively reduced fraction time in a mildly connected state with pronounced connectivity within the default mode network (DMN) and an overall reduced number of transitions between states.

CONCLUSIONS

These findings revealed by a dynamic, but not static analytic approach might hint towards a more "rigid" connectivity, a phenomenon commonly observed in internalizing mental disorders, and in AN possibly related to a reduction in energetic costs as a result of nutritional deprivation.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

BACKGROUND

Patients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.

METHODOLOGY

In a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.

RESULTS

FC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.

CONCLUSIONS

Differences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Probe into the treatment of tinnitus by acupuncture combined with medicine under the mechanism of pathophysiology: A review

Tinnitus is a common medical disorder. The risk factors include hearing loss, ototoxic medications, head injuries, and depression. Therefore, ear disorders, anxiety, and depression should be considered in the treatment of tinnitus. Although considerable research has been conducted on the pathogenesis and treatment of tinnitus, there is currently no effective treatment. Traditional Chinese medicine (TCM) has a certain effect on tinnitus and a large number of clinical trials have been conducted. Its treatment methods vary and include TCM, acupuncture, and music therapy. TCM treatment of tinnitus usually takes the method of comprehensive treatment, not only relying on drugs but also safety. Therefore, this review explores the treatment of tinnitus using acupuncture combined with medicine, based on the new pathophysiological mechanism of tinnitus.

Recovery of consciousness after acute brain injury: a narrative review

BACKGROUND

Disorders of consciousness (DoC) are frequently encountered in both, acute and chronic brain injuries. In many countries, early withdrawal of life-sustaining treatments is common practice for these patients even though the accuracy of predicting recovery is debated and delayed recovery can be seen. In this review, we will discuss theoretical concepts of consciousness and pathophysiology, explore effective strategies for management, and discuss the accurate prediction of long-term clinical outcomes. We will also address research challenges.

MAIN TEXT

DoC are characterized by alterations in arousal and/or content, being classified as coma, unresponsive wakefulness syndrome/vegetative state, minimally conscious state, and confusional state. Patients with willful modulation of brain activity detectable by functional MRI or EEG but not by behavioral examination is a state also known as covert consciousness or cognitive motor dissociation. This state may be as common as every 4th or 5th patient without behavioral evidence of verbal command following and has been identified as an independent predictor of long-term functional recovery. Underlying mechanisms are uncertain but intact arousal and thalamocortical projections maybe be essential. Insights into the mechanisms underlying DoC will be of major importance as these will provide a framework to conceptualize treatment approaches, including medical, mechanical, or electoral brain stimulation.

CONCLUSIONS

We are beginning to gain insights into the underlying mechanisms of DoC, identifying novel advanced prognostication tools to improve the accuracy of recovery predictions, and are starting to conceptualize targeted treatments to support the recovery of DoC patients. It is essential to determine how these advancements can be implemented and benefit DoC patients across a range of clinical settings and global societal systems. The Curing Coma Campaign has highlighted major gaps knowledge and provides a roadmap to advance the field of coma science with the goal to support the recovery of patients with DoC.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Altered resting-state and dynamic functional connectivity of hypothalamic in first-episode depression: A functional magnetic resonance imaging study

The hypothalamus is an important component of the hypothalamic-pituitary-adrenal axis and an important brain region of the limbic system. Twenty-four first depressive episode(FDE) patients and 25 healthy controls were recruited for this study. The hypothalamus was used as a seed to observe the characteristics of resting state and dynamic functional connectivity (FC) changes in FDE patients, and further observed the correlation between the different brain regions and clinical symptoms. The results found that compared with the HC group, the FDE group showed sFC was increased of the left hypothalamus with right superior parietal gyrus and right middle temporal gyrus, and dFC was increased of the left hypothalamus with left inferior occipital gyrus. And sFC was increased of the right hypothalamus with right orbital part of inferior frontal gyrus, right supplementary motor area, and right middle temporal gyrus, and the dFC was also increased of right hypothalamus with right superior parietal gyrus and left middle temporal gyrus. In addition,there was a negative correlation between dFC values of the right hypothalamus with the right superior parietal gyrus and clinical symptoms in the FDE group. This study provides new insights into understanding the altered neuropathological mechanisms of the hypothalamic circuit in FDE.

Altered functional connectivity of resting-state networks and the correlation with clinical characteristics in intermittent exotropia adult patients: a resting-state magnetic resonance imaging study

BACKGROUND

The pathogenesis of intermittent exotropia (IXT) remains unclear. The study aims to investigate alterations of resting-state networks (RSNs) in IXT adult patients using resting-state functional magnetic resonance imaging (rs-fMRI) data to explore the potential neural mechanisms.

METHODS

Twenty-six IXT adult patients and 22 age-, sex-, handedness-, and education-matched healthy controls (HCs) underwent fMRI scanning and ophthalmological examinations. Brain areas with significant functional connectivity (FC) differences between the IXT and HC groups were selected as regions of interest (ROI) and mean z-scores were calculated to control for individual differences.

RESULTS

Compared with HCs, IXT patients exhibited altered FC in various brain regions within RSNs involved in binocular fusion, stereopsis, ocular movement, emotional processes and social cognition, including the default mode network (DMN), the dorsal attention network (DAN), the visual network (VN), the sensorimotor network (SMN), the executive control network (ECN), the frontoparietal network (FPN) and the auditory network (AN). The degree of exodeviation was positively correlated with FC value of left middle occipital gyrus (MOG) within the VN. Correspondingly, we found a negative correlation between the degree of exodeviation and the FC value of left angular gyrus (AG) within FPN (P < 0.05). The FNC analysis between different RSNs also provides evidence on visual-motor cortical plasticity.

CONCLUSIONS

IXT patients showed widespread changes of brain activity within RSNs related to binocular fusion, stereopsis, oculomotor control, emotional processes, and social cognition. These findings extend our current understanding of the neuropathological mechanisms of IXT.

TRIAL REGISTRATION

Beginning date of the trial: 2021-09-01. Date of registration:2021-07-18. Trial registration number: ChiCTR 2,100,048,852. Trial registration site: http://www.chictr.org.cn/index.aspx .

Frontoparietal network topology as a neural marker of musical perceptual abilities

Why are some individuals more musical than others? Neither cognitive testing nor classical localizationist neuroscience alone can provide a complete answer. Here, we test how the interplay of brain network organization and cognitive function delivers graded perceptual abilities in a distinctively human capacity. We analyze multimodal magnetic resonance imaging, cognitive, and behavioral data from 200+ participants, focusing on a canonical working memory network encompassing prefrontal and posterior parietal regions. Using graph theory, we examine structural and functional frontoparietal network organization in relation to assessments of musical aptitude and experience. Results reveal a positive correlation between perceptual abilities and the integration efficiency of key frontoparietal regions. The linkage between functional networks and musical abilities is mediated by working memory processes, whereas structural networks influence these abilities through sensory integration. Our work lays the foundation for future investigations into the neurobiological roots of individual differences in musicality.

The Contribution of Cognitive Control Networks in Word Selection Processing in Parkinson's Disease: Novel Insights from a Functional Connectivity Study

Parkinson's disease (PD) patients are impaired in word production when the word has to be selected among competing alternatives requiring higher attentional resources. In PD, word selection processes are correlated with the structural integrity of the inferior frontal gyrus, which is critical for response selection, and the uncinate fasciculus, which is necessary for processing lexical information. In early PD, we investigated the role of the main cognitive large-scale networks, namely the salience network (SN), the central executive networks (CENs), and the default mode network (DMN), in word selection. Eighteen PD patients and sixteen healthy controls were required to derive nouns from verbs or generate verbs from nouns. Participants also underwent a resting-state functional MRI. Functional connectivity (FC) was examined using independent component analysis. Functional seeds for the SN, CENs, and DMN were defined as spheres, centered at the local activation maximum. Correlations were calculated between the FC of each functional seed and word production. A significant association between SN connectivity and task performance and, with less evidence, between CEN connectivity and the task requiring selection among a larger number of competitors, emerged in the PD group. These findings suggest the involvement of the SN and CEN in word selection in early PD, supporting the hypothesis of impaired executive control.

Contracted functional connectivity profiles in autism

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.

METHODS

We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.

RESULTS

Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.

LIMITATIONS

Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.

CONCLUSIONS

Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Efficacy Assessment of Cerebral Perfusion Augmentation through Functional Connectivity in an Acute Canine Stroke Model

BACKGROUND AND PURPOSE

Ischemic stroke disrupts functional connectivity within the brain's resting-state networks (RSNs), impacting recovery. This study evaluates the effects of norepinephrine and hydralazine (NEH), a cerebral perfusion augmentation therapy, on RSN integrity in a hyperacute canine stroke model.

MATERIALS AND METHODS

Fifteen adult purpose-bred mongrel canines, divided into treatment and control (natural history) groups, underwent endovascular induction of acute middle cerebral artery occlusion (MCAO). Postocclusion, the treatment group received intra-arterial norepinephrine (0.1-1.52 µg/kg/min, adjusted for 25-45 mm Hg above baseline mean arterial pressure) and hydralazine (20 mg). Resting-state fMRI (rs-fMRI) data were acquired with a 3T scanner by using a blood oxygen level dependent-EPI sequence (TR/TE = 1400 ms/20 ms, 2.5 mm slices, 300 temporal positions). Preprocessing included motion correction, spatial smoothing (2.5 mm full width at half maximum), and high-pass filtering (0.01 Hz cutoff). Functional connectivity within RSNs were analyzed through group-level independent component analysis and weighted whole-brain ROI-to-ROI connectome, pre- and post-MCAO.

RESULTS

NEH therapy significantly maintained connectivity post-MCAO in the higher-order visual and parietal RSNs, as evidenced by thresholded statistical mapping (threshold-free cluster enhancement P corr > .95). However, this preservation was network-dependent, with no significant (P corr < .95) changes in the primary visual and sensorimotor networks.

CONCLUSIONS

NEH demonstrates potential as a proof-of-concept therapy for maintaining RSN functional connectivity after ischemic stroke, emphasizing the therapeutic promise of perfusion augmentation. These insights reinforce the role of functional connectivity as a measurable end point for stroke intervention efficacy, suggesting clinical translatability for patients with insufficient collateral circulation.

Principles of cortical areas and their implications for neuroimaging

Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.

Dynamics of spontaneous thoughts: Exploration, attentional profile and the segmentation of the stream of thoughts

For a long time, clinical knowledge and first-person reports have pointed to individual differences in the dynamics of spontaneous thoughts, in particular in the extreme case of psychiatric conditions (e.g. racing thoughts in Attention Deficit / Hyperactivity Disorder, ADHD; rumination in depression). We used a novel procedure to investigate this individual variability by combining verbal fluency tasks and introspective reports of thought content. Our goal was twofold. First, we tested the hypothesis that a greater segmentation of the stream of thoughts would be associated with trait inattention, in line with subjective reports of ADHD patients. Second, we tested whether the segmentation of the stream of thoughts increased with an increased tendency for exploratory behavior, following recent theoretical claims on the mechanisms underpinning the generation of spontaneous thoughts. Our results support both hypotheses, shedding light on the factors contributing to the individual variability in the dynamics of the stream of thought.

Impact of Electric Field Magnitude in the Left Dorsolateral Prefrontal Cortex on Changes in Intrinsic Functional Connectivity Using Transcranial Direct Current Stimulation: A Randomized Crossover Study

This study investigated whether the electric field magnitude (E-field) delivered to the left dorsolateral prefrontal cortex (L-DLPFC) changes resting-state brain activity and the L-DLPFC resting-state functional connectivity (rsFC), given the variability in tDCS response and lack of understanding of how rsFC changes. Twenty-one healthy participants received either 2 mA anodal or sham tDCS targeting the L-DLPFC for 10 min. Brain imaging was conducted before and after stimulation. The fractional amplitude of low-frequency fluctuation (fALFF), reflecting resting brain activity, and the L-DLPFC rsFC were analyzed to investigate the main effect of tDCS, main effect of time, and interaction effects. The E-field was estimated by modeling tDCS-induced individual electric fields and correlated with fALFF and L-DLPFC rsFC. Anodal tDCS increased fALFF in the left rostral middle frontal area and decreased fALFF in the midline frontal area (FWE p < 0.050), whereas sham induced no changes. Overall rsFC decreased after sham (positive and negative connectivity, p = 0.001 and 0.020, respectively), with modest and nonsignificant changes after anodal tDCS (p = 0.063 and 0.069, respectively). No significant differences in local rsFC were observed among the conditions. Correlations were observed between the E-field and rsFC changes in the L-DLPFC (r = 0.385, p = 0.115), left inferior parietal area (r = 0.495, p = 0.037), and right lateral visual area (r = 0.683, p = 0.002). Single-session tDCS induced resting brain activity changes and may help maintain overall rsFC. The E-field in the L-DLPFC is associated with rsFC changes in both proximal and distally connected brain regions to the L-DLPFC.

COVID-19 is associated with changes in brain function and structure: A multimodal meta-analysis of neuroimaging studies

The actual role of coronavirus disease 2019 (COVID-19) in brain damage has been increasingly reported, necessitating a meta-analysis to collate and summarize the inconsistent findings from functional imaging and voxel-based morphometry (VBM) studies. A comprehensive voxel-wise meta-analysis of the whole brain was conducted to identify alterations in functional activity and gray matter volume (GMV) between COVID-19 patients and healthy controls (HCs) by using Seed-based d Mapping software. We included 15 functional imaging studies (484 patients with COVID-19, 534 HCs) and 9 VBM studies (449 patients with COVID-19, 388 HCs) in the analysis. Overall, patients with COVID-19 exhibited decreased functional activity in the right superior temporal gyrus (STG) (extending to the right middle and inferior temporal gyrus, insula, and temporal pole [TP]), left insula, right orbitofrontal cortex (OFC) (extending to the right olfactory cortex), and left cerebellum compared to HCs. For VBM, patients with COVID-19, relative to HCs, showed decreased GMV in the bilateral anterior cingulate cortex/medial prefrontal cortex (extending to the bilateral OFC), and left cerebellum, and increased GMV in the bilateral amygdala (extending to the bilateral hippocampus, STG, TP, MTG, and right striatum). Moreover, overlapping analysis revealed that patients with COVID-19 exhibited both decreased functional activity and increased GMV in the right TP (extending to the right STG). The multimodal meta-analysis suggests that brain changes of function and structure in the temporal lobe, OFC and cerebellum, and functional or structural alterations in the insula and the limbic system in COVID-19. These findings contribute to a better understanding of the pathophysiology of brain alterations in COVID-19. SIGNIFICANCE STATEMENT: This first large-scale multimodal meta-analysis collates existing neuroimaging studies and provides voxel-wise functional and structural whole-brain abnormalities in COVID-19. Findings of this meta-analysis provide valuable insights into the dynamic brain changes (from infection to recovery) and offer further explanations for the pathophysiological basis of brain alterations in COVID-19.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Abnormalities in spontaneous brain activity and functional connectivity are associated with cognitive impairments in children with type 1 diabetes mellitus

BACKGROUND

It remains unclear whether alterations in brain function occur in the early stage of pediatric type 1 diabetes mellitus(T1DM). We aimed to examine changes in spontaneous brain activity and functional connectivity (FC) in children with T1DM using resting-state functional magnetic resonance imaging (rs-fMRI), and to pinpoint potential links between neural changes and cognitive performance.

METHODS

In this study, 22 T1DM children and 21 age-, sex-matched healthy controls underwent rs-fMRI. The amplitude of low frequency fluctuations (ALFF) and seed-based FC analysis were performed to examine changes in intrinsic brain activity and functional networks in T1DM children. Partial correlation analyses were utilized to explore the correlations between ALFF values and clinical parameters.

RESULTS

The ALFF values were significantly lower in the lingual gyrus (LG) and higher in the left medial superior frontal gyrus (MSFG) in T1DM children compared to controls. Subsequent FC analysis indicated that the LG had decreased FC with bilateral inferior occipital gyrus, and the left MSFG had decreased FC with right precentral gyrus, right inferior parietal gyrus and right postcentral gyrus in children with T1DM. The ALFF values of LG were positively correlated with full-scale intelligence quotient and age at disease onset in T1DM children, while the ALFF values of left MSFG were positively correlated with working memory scores.

CONCLUSION

Our findings revealed abnormal spontaneous activity and FC in brain regions related to visual, memory, default mode network, and sensorimotor network in the early stage of T1DM children, which may aid in further understanding the mechanisms underlying T1DM-associated cognitive dysfunction.

Global Functional Connectivity is Associated with Mind Wandering Domain of Comfort

The resting-state paradigm is frequently applied to study spontaneous activity of the brain in normal and clinical conditions. To assess the relationship between brain activity and subjective experiences, various questionnaires are used. Previous studies using Amsterdam Resting State Questionnaire were focusing on fMRI functional connectivity or EEG microstates and spectral aspect. Here, we utilized Global Field Synchronization as the parameter to estimate global functional connectivity. By re-analyzing the resting-state data from 226 young healthy participants we showed a strong evidence of relationship between ARSQ domain of Comfort and GFS values in the alpha range (r = 0.210, BF10 = 12.338) and substantial evidence for positive relationship between ARSQ domain of Comfort and GFS in the beta frequency range (r = 196, BF10 = 6.307). Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the individual intrinsic electrical brain activity.

Physical activity and frontoparietal network connectivity in traumatic brain injury

BACKGROUND

Prolonged changes to functional network connectivity as a result of a traumatic brain injury (TBI) may relate to long-term cognitive complaints reported by TBI survivors. No interventions have proven to be effective at treating long-term cognitive complaints after TBI but physical activity has been shown to promote cognitive function and modulate functional network connectivity in non-injured adults. Therefore, the objective of this study was to test if physical activity engagement was associated with functional connectivity of the cognitively relevant frontoparietal control network (FPCN) in adults with a TBI history.

METHODS

In a case-control study design, resting state function magnetic resonance imaging and physical activity data from a subset of participants (18-81 years old) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study was analyzed. Fifty-seven participants reported a prior head injury with loss of consciousness and 57 age and sex matched controls were selected. Seed-based functional connectivity analyses were performed using seeds in the dorsolateral prefrontal cortex and the inferior parietal lobule, to test for differences in functional connectivity between groups, associations between physical activity and functional connectivity within TBI as well as differential associations between physical activity and functional connectivity between TBI and controls.

RESULTS

Seed-based connectivity analyses from the dorsolateral prefrontal cortex showed that those with a history of TBI had decreased positive connectivity between dorsolateral prefrontal cortex and intracalcarine cortex, lingual gyrus, and cerebellum, and increased positive connectivity between dorsolateral prefrontal cortex and cingulate gyrus and frontal pole in the TBI group. Results showed that higher physical activity was positively associated with increased connectivity between the dorsolateral prefrontal cortex and inferior temporal gyrus. Differential associations were observed between groups whereby the strength of the physical activity-functional connectivity association was different between the inferior parietal lobule and inferior temporal gyrus in TBI compared to controls.

DISCUSSION

Individuals with a history of TBI show functional connectivity alterations of the FPCN. Moreover, engagement in physical activity is associated with functional network connectivity of the FPCN in those with a TBI. These findings are consistent with the evidence that physical activity affects FPCN connectivity in non-injured adults; however, this effect presents differently in those with a history of TBI.

Abnormal functional connectivity of the putamen in obsessive-compulsive disorder

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

The functional and structural alterations in brain regions related to the fear network model in panic disorder: A resting-state fMRI and T1-weighted imaging study

Abnormal functional connectivity (FC) within the fear network model (FNM) has been identified in panic disorder (PD) patients, but the specific local structural and functional properties, as well as effective connectivity (EC), remain poorly understood in PD. The purpose of this study was to investigate the structural and functional patterns of the FNM in PD. Magnetic resonance imaging data were collected from 33 PD patients and 35 healthy controls (HCs). Gray matter volume (GMV), degree centrality (DC), regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) were used to identify the structural and functional characteristics of brain regions within the FNM in PD. Subsequently, FC and EC of abnormal regions, based on local structural and functional features, and their correlation with clinical features were further examined. PD patients exhibited preserved GMV, ReHo, and ALFF in the brain regions of the FNM compared with HCs. However, increased DC in the bilateral amygdala was observed in PD patients. The amygdala and its subnuclei exhibited altered EC with rolandic operculum, insula, medial superior frontal gyrus, supramarginal gyrus, opercular part of inferior frontal gyrus, and superior temporal gyrus. Additionally, Hamilton Anxiety Scale score was positively correlated with EC from left lateral nuclei (dorsal portion) of amygdala to right rolandic operculum and left superior temporal gyrus. Our findings revealed a reorganized functional network in PD involving brain regions regulating exteroceptive-interoceptive signals, mood, and somatic symptoms. These results enhance our understanding of the neurobiological underpinnings of PD, suggesting potential biomarkers for diagnosis and targets for therapeutic intervention.

Disruption of Resting-State Functional Connectivity in Acute Ischemic Stroke: Comparisons Between Right and Left Hemispheric Insults

Few resting-state functional magnetic resonance imaging (RS-fMRI) studies evaluated the impact of acute ischemic changes on cerebral functional connectivity (FC) and its relationship with functional outcomes after acute ischemic stroke (AIS), considering the side of lesions. To characterize alterations of FC of patients with AIS by analyzing 12 large-scale brain networks (NWs) with RS-fMRI. Additionally, we evaluated the impact of the side (right (RH) or left (LH) hemisphere) of insult on the disruption of brain NWs. 38 patients diagnosed with AIS (17 RH and 21 LH) who performed 3T MRI scans up to 72 h after stroke were compared to 44 healthy controls. Images were processed and analyzed with the software toolbox UF2C with SPM12. For the first level, we generated individual matrices based on the time series extraction from 70 regions of interest (ROIs) from 12 functional NWs, constructing Pearson's cross-correlation; the second-level analysis included an analysis of covariance (ANCOVA) to investigate differences between groups. The statistical significance was determined with p < 0.05, after correction for multiple comparisons with false discovery rate (FDR) correction. Overall, individuals with LH insults developed poorer clinical outcomes after six months. A widespread pattern of lower FC was observed in the presence of LH insults, while a contralateral pattern of increased FC was identified in the group with RH insults. Our findings suggest that LH stroke causes a severe and widespread pattern of reduction of brain networks' FC, presumably related to the impairment in their long-term recovery.

Abnormal Local Brain Activity and Cognitive Impairments in Young Non-Disabled Patients With Intracerebral Hemorrhage: A Resting-State Functional MRI Study

BACKGROUND

Resting-state functional MRI (rs-fMRI) has identified static changes of local brain activity among patients with intracerebral hemorrhage (ICH). However, the dynamic and concordance-related characteristics of brain activity remain unclear.

PURPOSE

To investigate static, dynamic, and concordance-related features of the regional brain activity of young non-disabled ICH patients.

STUDY TYPE

Prospective.

SUBJECTS

Thirty-three ICH patients (modified Rankin Scale score ≤2, 21% female, 46.36 ± 6.53) and 33 matched healthy controls (HCs) (21% female, 47.64 ± 6.16).

FIELD STRENGTH/SEQUENCE

3-T, rs-fMRI using gradient echo-planar imaging, T1-weighted imaging.

ASSESSMENT

Neuropsychological and rs-fMRI data were acquired from all participants. Amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity, global signal correlation (GSCorr) and degree centrality (DC), and their dynamic and concordance-related changes with sliding window analysis were calculated based on rs-fMRI data at a whole-brain level. The burden of cerebral small vascular diseases (cSVD) was assessed by cSVD scores. All hemorrhage lesions were delineated on normalized T1 images.

STATISTICAL TESTS

Multiple regression models, a voxel-level uncorrected P < 0.001, a cluster-level false discovery rate (FDR) corrected q < 0.05, a re-corrected qFDR <0.05 were considered significant. Pearson or Spearman correlation analyses between fMRI data and neurocognitive performance were performed.

RESULTS

Compared to HCs, ICH patients showed significant abnormal changes of ALFF, dynamic ALFF, fALFF, ReHo, dynamic ReHo, GSCorr, DC, and voxel-wise concordance in multiple brain regions mainly including the bilateral cerebellar hemispheres, ipsilesional thalamus, and bilateral middle cingulum gyrus. The ALFF in the cerebellar posterior lobe and thalamus were significantly associated with attention (r = -0.481) and executive function (rho = -0.521) in ICH patients.

DATA CONCLUSION

Young non-disabled ICH patients exhibit static, dynamic, and concordance-related alterations of local brain activity, part of which shows associations with cognitive functions. These findings may help comprehensively understand the mechanism of cognitive impairment after ICH.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 3.

Relationship Between Interoception and Autistic Traits: A Resting-State Functional Connectivity Study

Interoception, the sense of the physiological condition of our body, is impaired in individuals with autism spectrum disorders. Evidence suggests that subclinical autistic traits are mild manifestations of autistic symptoms, present in the general population. We examined the resting-state functional connectivity (rsFC) associating with interoception and autistic traits in 62 healthy young adults. Autistic traits correlated negatively with the rsFC between the lateral ventral anterior insula and anterior cingulate cortex. Interoceptive accuracy and sensibility correlated positively with the rsFC between interoceptive brain networks and the cerebellum, supplementary motor area, and visual regions. The results suggest that a negative relationship between interoception and autistic traits is largely accounted for by both self-report measures and decreased rsFC amongst the interoceptive brain network.

Language networks of normal-hearing infants exhibit topological differences between resting and steady states: An fNIRS functional connectivity study

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

Oscillatory mechanisms of intrinsic human brain networks

Non-invasive neuroimaging has revealed specific network-based resting-state dynamics in the human brain, yet the underlying neurophysiological mechanism remains unclear. We employed intracranial electroencephalography to characterize local field potentials within the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in 42 participants. We identified stronger within-network phase coherence at low frequencies (θ and α band) within the DMN, and at high frequencies (γ band) within the FPN. Hidden Markov modeling indicated that the DMN exhibited preferential low frequency phase coupling. Phase-amplitude coupling (PAC) analysis revealed that the low-frequency phase in the DMN modulated the high-frequency amplitude envelopes of the FPN, suggesting frequency-dependent characterizations of intrinsic brain networks at rest. These findings provide intracranial electrophysiological evidence in support of the network model for intrinsic organization of human brain and shed light on the way brain networks communicate at rest.

Functional alterations in overweight/obesity: focusing on the reward and executive control network

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity

Oscillatory complex networks in the metastable regime have been used to study the emergence of integrated and segregated activity in the brain, which are hypothesised to be fundamental for cognition. Yet, the parameters and the underlying mechanisms necessary to achieve the metastable regime are hard to identify, often relying on maximising the correlation with empirical functional connectivity dynamics. Here, we propose and show that the brain's hierarchically modular mesoscale structure alone can give rise to robust metastable dynamics and (metastable) chimera states in the presence of phase frustration. We construct unweighted 3-layer hierarchical networks of identical Kuramoto-Sakaguchi oscillators, parameterized by the average degree of the network and a structural parameter determining the ratio of connections between and within blocks in the upper two layers. Together, these parameters affect the characteristic timescales of the system. Away from the critical synchronization point, we detect the emergence of metastable states in the lowest hierarchical layer coexisting with chimera and metastable states in the upper layers. Using the Laplacian renormalization group flow approach, we uncover two distinct pathways towards achieving the metastable regimes detected in these distinct layers. In the upper layers, we show how the symmetry-breaking states depend on the slow eigenmodes of the system. In the lowest layer instead, metastable dynamics can be achieved as the separation of timescales between layers reaches a critical threshold. Our results show an explicit relationship between metastability, chimera states, and the eigenmodes of the system, bridging the gap between harmonic based studies of empirical data and oscillatory models.

Integrative role of attention networks in frequency-dependent modular organization of human brain

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103 Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

Intra- and Inter-Network connectivity of the default mode network differentiates Treatment-Resistant depression from Treatment-Sensitive depression

Understanding why some patients with depression remain resistant to antidepressant medication could be elucidated by investigating their associated neural features. Although research has consistently demonstrated abnormalities in the anterior cingulate cortex (ACC) - a region that is part of the default mode network (DMN) - in treatment-resistant depression (TRD), a considerable research gap exists in discerning how these neural networks distinguish TRD from treatment-sensitive depression (TSD). We aimed to evaluate the resting-state functional connectivity (rsFC) of the ACC with other regions of the DMN to better understand the role of this structure in the pathophysiology of TRD. 35 TRD patients, 35 TSD patients, and 38 healthy controls (HC) underwent a resting-state functional MRI protocol. Seed-based functional connectivity analyses were performed, comparing the three groups for the connectivity between two subregions of the ACC (the subgenual ACC (sgACC) and the rostral ACC (rACC)) and the DMN (p < 0.05 FWE corrected). Furthermore, inter-network connectivity of the DMN with other neural networks was explored by independent component (ICA) analyses (p < 0.01, FDR corrected). The results demonstrated hyperconnectivity between the rACC and the posterior cingulate cortex in TRD relative to TSD and HC (F(2,105) = 5.335, p < 0.05). ICA found DMN connectivity to regions of the visual network (TRDTSD), differentiating the two clinical groups. These results provide confirmatory evidence of DMN hyperconnectivity and preliminary evidence for its interactions with other neural networks as key neural mechanisms underlying treatment non-responsiveness.

Functional connectivity across the human subcortical auditory system using an autoregressive matrix-Gaussian copula graphical model approach with partial correlations

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

A visual representation of the hand in the resting somatomotor regions of the human brain

Hand visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of somatomotor control. Spontaneous brain activity, i.e., at rest, in the absence of an active task, is correlated among somatomotor regions that are jointly activated during motor tasks. Recent studies suggest that spontaneous activity patterns not only replay task activation patterns but also maintain a model of the body's and environment's statistical regularities (priors), which may be used to predict upcoming behavior. Here, we test whether spontaneous activity in the human somatomotor cortex as measured using fMRI is modulated by visual stimuli that display hands vs. non-hand stimuli and by the use/action they represent. A multivariate pattern analysis was performed to examine the similarity between spontaneous activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex, we observed a stronger (multivoxel) spatial correlation between resting state activity and natural hand picture patterns compared to other stimuli. No task-rest similarity was found in the visual cortex. Spontaneous activity patterns in somatomotor brain regions code for the visual representation of human hands and their use.

Regional brain activity and connectivity associated with childhood trauma in drug-naive patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors, with childhood trauma recognized as a contributing factor to its pathophysiology. This study aimed to delineate brain functional aberrations in OCD patients and explore the association between these abnormalities and childhood trauma, to gain insights into the neural underpinnings of OCD. Forty-eight drug-naive OCD patients and forty-two healthy controls (HC) underwent resting-state functional magnetic resonance imaging and clinical assessments, including the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and Childhood Trauma Questionnaire-Short Form (CTQ-SF). Compared to HCs, OCD patients exhibited significantly decreased amplitude of low-frequency fluctuations (ALFF) in the right cerebellum, decreased regional homogeneity (ReHo) in the right cerebellum and right superior occipital lobes (FWE-corrected p < 0.05), which negatively correlated with Y-BOCS scores (p < 0.05). Furthermore, cerebellar ALFF negatively correlated with the CTQ emotional abuse subscale (r = - 0.514, p < 0.01). Mediation analysis revealed that cerebellar ALFF mediated the relationship between CTQ-emotional abuse and Y-BOCS (good model fit: R2 = 0.231, MSE = 14.311, F = 5.721, p < 0.01; direct effect, c' = 0.153, indirect effect, a*b = 0.191). Findings indicated abnormal spontaneous and regional cerebellar activity in OCD, suggesting childhood trauma impacts OCD symptoms through cerebellar neural remodeling, highlighting its importance for clinical treatment selection.

More widespread functionality of posterior language area in patients with brain tumors

Damage to the posterior language area (PLA), or Wernicke's area causes cortical reorganization in the corresponding regions of the contralateral hemisphere. However, the details of reorganization within the ipsilateral hemisphere are not fully understood. In this context, direct electrical stimulation during awake surgery can provide valuable opportunities to investigate neuromodulation of the human brain in vivo, which is difficult through the non-invasive approaches. Thus, in this study, we aimed to investigate the characteristics of the cortical reorganization of the PLA within the ipsilateral hemisphere. Sixty-two patients with left hemispheric gliomas were divided into groups depending on whether the lesion extended to the PLA. All patients underwent direct cortical stimulation with a picture-naming task. We further performed functional connectivity analyses using resting-state functional magnetic resonance imaging (MRI) in a subset of patients and calculated betweenness centrality, an index of the network importance of brain areas. During direct cortical stimulation, the regions showing positive (impaired) responses in the non-PLA group were localized mainly in the posterior superior temporal gyrus (pSTG), whereas those in the PLA group were widely distributed from the pSTG to the posterior supramarginal gyrus (pSMG). Notably, the percentage of positive responses in the pSMG was significantly higher in the PLA group (47%) than in the non-PLA group (8%). In network analyses of functional connectivity, the pSMG was identified as a hub region with high betweenness centrality in both the groups. These findings suggest that the language area can spread beyond the PLA to the pSMG, a hub region, in patients with lesion progression to the pSTG. The change in the pattern of the language area may be a compensatory mechanism to maintain efficient brain networks.

Comparision of spontaneous brain activity between hippocampal sclerosis and MRI-negative temporal lobe epilepsy

BACKGROUND

Hippocampal sclerosis (HS) is a prevalent cause of temporal lobe epilepsy (TLE). However, up to 30% of individuals with TLE present negative magnetic resonance imaging (MRI) findings. A comprehensive grasp of the similarities and differences in brain activity among distinct TLE subtypes holds significant clinical and scientific importance.

OBJECTIVE

To comprehensively examine the similarities and differences between TLE with HS (TLE-HS) and MRI-negative TLE (TLE-N) regarding static and dynamic abnormalities in spontaneous brain activity (SBA). Furthermore, we aimed to determine whether these alterations correlate with epilepsy duration and cognition, and to determine a potential differential diagnostic index for clinical utility.

METHODS

We measured 12 SBA metrics in 38 patients with TLE-HS, 51 with TLE-N, and 53 healthy volunteers. Voxel-wise analysis of variance (ANOVA) and post-hoc comparisons were employed to compare these metrics. The six static metrics included amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), degree centrality (DC), and global signal correlation (GSCorr). Additionally, six corresponding dynamic metrics were assessed: dynamic ALFF (dALFF), dynamic fALFF (dfALFF), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr). Receiver operating characteristic (ROC) curve analysis of abnormal indices was employed. Spearman correlation analyses were also conducted to examine the relationship between the abnormal indices, epilepsy duration and cognition scores.

RESULTS

Both TLE-HS and TLE-N presented as extensive neural network disorders, sharing similar patterns of SBA alterations. The regions with increased fALFF, dALFF, and dfALFF levels were predominantly observed in the mesial temporal lobe, thalamus, basal ganglia, pons, and cerebellum, forming a previously proposed mesial temporal epilepsy network. Conversely, decreased SBA metrics (fALFF, ReHo, dReHo, DC, GSCorr, and VMHC) consistently appeared in the lateral temporal lobe ipsilateral to the epileptic foci. Notably, SBA alterations were more obvious in patients with TLE-HS than in those with TLE-N. Additionally, patients with TLE-HS exhibited reduced VMHC in both mesial and lateral temporal lobes compared with patients with TLE-N, with the hippocampus displaying moderate discriminatory power (AUC = 0.759). Correlation analysis suggested that alterations in SBA indicators may be associated with epilepsy duration and cognitive scores.

CONCLUSIONS

The simultaneous use of static and dynamic SBA metrics provides evidence supporting the characterisation of both TLE-HS and TLE-N as complex network diseases, facilitating the exploration of mechanisms underlying epileptic activity and cognitive impairment. Overall, SBA abnormality patterns were generally similar between the TLE-HS and TLE-N groups, encompassing networks related to TLE and auditory and occipital visual functions. These changes were more pronounced in the TLE-HS group, particularly within the mesial and lateral temporal lobes.

The brain's "dark energy" puzzle: How strongly is glucose metabolism linked to resting-state brain activity?

Brain glucose metabolism, which can be investigated at the macroscale level with [18F]FDG PET, displays significant regional variability for reasons that remain unclear. Some of the functional drivers behind this heterogeneity may be captured by resting-state functional magnetic resonance imaging (rs-fMRI). However, the full extent to which an fMRI-based description of the brain's spontaneous activity can describe local metabolism is unknown. Here, using two multimodal datasets of healthy participants, we built a multivariable multilevel model of functional-metabolic associations, assessing multiple functional features, describing the 1) rs-fMRI signal, 2) hemodynamic response, 3) static and 4) time-varying functional connectivity, as predictors of the human brain's metabolic architecture. The full model was trained on one dataset and tested on the other to assess its reproducibility. We found that functional-metabolic spatial coupling is nonlinear and heterogeneous across the brain, and that local measures of rs-fMRI activity and synchrony are more tightly coupled to local metabolism. In the testing dataset, the degree of functional-metabolic spatial coupling was also related to peripheral metabolism. Overall, although a significant proportion of regional metabolic variability can be described by measures of spontaneous activity, additional efforts are needed to explain the remaining variance in the brain's 'dark energy'.