Consistent resting-state networks across healthy subjects

Data-driven MEG analysis to extract fMRI resting-state networks

The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.

Graph theoretical analysis and independent component analysis of diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study

AIMS

This study aimed to investigate the resting-state functional connectivity and topologic characteristics of brain networks in patients with diabetic optic neuropathy (DON).

METHODS

Resting-state functional magnetic resonance imaging scans were performed on 23 patients and 41 healthy control (HC) subjects. We used independent component analysis and graph theoretical analysis to determine the topologic characteristics of the brain and as well as functional network connectivity (FNC) and topologic properties of brain networks.

RESULTS

Compared with HCs, patients with DON showed altered global characteristics. At the nodal level, the DON group had fewer nodal degrees in the thalamus and insula, and a greater number in the right rolandic operculum, right postcentral gyrus, and right superior temporal gyrus. In the internetwork comparison, DON patients showed significantly increased FNC between the left frontoparietal network (FPN-L) and ventral attention network (VAN). Additionally, in the intranetwork comparison, connectivity between the left medial superior frontal gyrus (MSFG) of the default network (DMN) and left putamen of auditory network was decreased in the DON group.

CONCLUSION

DON patients altered node properties and connectivity in the DMN, auditory network, FPN-L, and VAN. These results provide evidence of the involvement of specific brain networks in the pathophysiology of DON.

Amyloid induced hyperexcitability in default mode network drives medial temporal hyperactivity and early tau accumulation

In early Alzheimer's disease (AD) β-amyloid (Aβ) deposits throughout association cortex and tau appears in the entorhinal cortex (EC). Why these initially appear in disparate locations is not understood. Using task-based fMRI and multimodal PET imaging, we assess the impact of local AD pathology on network-to-network interactions. We show that AD pathologies flip interactions between the default mode network (DMN) and the medial temporal lobe (MTL) from inhibitory to excitatory. The DMN is hyperexcited with increasing levels of Aβ, which drives hyperexcitability within the MTL and this directed hyperexcitation of the MTL by the DMN predicts the rate of tau accumulation within the EC. Our results support a model whereby Aβ induces disruptions to local excitatory-inhibitory balance in the DMN, driving hyperexcitability in the MTL, leading to tau accumulation. We propose that Aβ-induced disruptions to excitatory-inhibitory balance is a candidate causal route between Aβ and remote EC-tau accumulation.

Olfactory deficit: a potential functional marker across the Alzheimer's disease continuum

Alzheimer's disease (AD) is a prevalent form of dementia that affects an estimated 32 million individuals globally. Identifying early indicators is vital for screening at-risk populations and implementing timely interventions. At present, there is an urgent need for early and sensitive biomarkers to screen individuals at risk of AD. Among all sensory biomarkers, olfaction is currently one of the most promising indicators for AD. Olfactory dysfunction signifies a decline in the ability to detect, identify, or remember odors. Within the spectrum of AD, impairment in olfactory identification precedes detectable cognitive impairments, including mild cognitive impairment (MCI) and even the stage of subjective cognitive decline (SCD), by several years. Olfactory impairment is closely linked to the clinical symptoms and neuropathological biomarkers of AD, accompanied by significant structural and functional abnormalities in the brain. Olfactory behavior examination can subjectively evaluate the abilities of olfactory identification, threshold, and discrimination. Olfactory functional magnetic resonance imaging (fMRI) can provide a relatively objective assessment of olfactory capabilities, with the potential to become a promising tool for exploring the neural mechanisms of olfactory damage in AD. Here, we provide a timely review of recent literature on the characteristics, neuropathology, and examination of olfactory dysfunction in the AD continuum. We focus on the early changes in olfactory indicators detected by behavioral and fMRI assessments and discuss the potential of these techniques in MCI and preclinical AD. Despite the challenges and limitations of existing research, olfactory dysfunction has demonstrated its value in assessing neurodegenerative diseases and may serve as an early indicator of AD in the future.

Distinct functional subnetworks of cognitive domains in older adults with minor cognitive deficits

Although past research has established a relationship between functional connectivity and cognitive function, less is known about which cognitive domains are associated with which specific functional networks. This study investigated associations between functional connectivity and global cognitive function and performance in the domains of memory, executive function and psychomotor speed in 166 older adults aged 75-91 years (mean = 80.3 ± 3.8) with minor cognitive deficits (Mini-Mental State Examination scores between 21 and 27). Functional connectivity was assessed within 10 standard large-scale resting-state networks and on a finer spatial resolution between 300 nodes in a functional connectivity matrix. No domain-specific associations with mean functional connectivity within large-scale resting-state networks were found. Node-level analysis revealed that associations between functional connectivity and cognitive performance differed across cognitive functions in strength, location and direction. Specific subnetworks of functional connections were found for each cognitive domain in which higher connectivity between some nodes but lower connectivity between other nodes were related to better cognitive performance. Our findings add to a growing body of literature showing differential sensitivity of functional connections to specific cognitive functions and may be a valuable resource for hypothesis generation of future studies aiming to investigate specific cognitive dysfunction with resting-state functional connectivity in people with beginning cognitive deficits.

Altered spontaneous brain activity in lumbar disc herniation patients: insights from an ALE meta-analysis of neuroimaging data

Objective

To explore the characteristics of spontaneous brain activity changes in patients with lumbar disc herniation (LDH), and help reconcile the contradictory findings in the literature and enhance the understanding of LDH-related pain.

Materials and methods

PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), SinoMed, and Wanfang databases were searched for literature that studies the changes of brain basal activity in patients with LDH using regional homogeneity (ReHo) and amplitude of low-frequency fluctuation/fraction amplitude of low-frequency fluctuation (ALFF/fALFF) analysis methods. Activation likelihood estimation (ALE) was used to perform a meta-analysis of the brain regions with spontaneous brain activity changes in LDH patients compared with healthy controls (HCs).

Results

A total of 11 studies were included, including 7ALFF, 2fALFF, and 2ReHo studies, with a total of 269 LDH patients and 277 HCs. Combined with the data from the ALFF/fALFF and ReHo studies, the meta-analysis results showed that compared with HCs, LDH patients had increased spontaneous brain activity in the right middle frontal gyrus (MFG), left anterior cingulate cortex (ACC) and the right anterior lobe of the cerebellum, while they had decreased spontaneous brain activity in the left superior frontal gyrus (SFG). Meta-analysis using ALFF/fALFF data alone showed that compared with HCs, LDH patients had increased spontaneous brain activity in the right MFG and left ACC, but no decrease in spontaneous brain activity was found.

Conclusion

In this paper, through the ALE Meta-analysis method, based on the data of reported rs-fMRI whole brain studies, we found that LDH patients had spontaneous brain activity changes in the right middle frontal gyrus, left anterior cingulate gyrus, right anterior cerebellar lobe and left superior frontal gyrus. However, it is still difficult to assess whether these results are specific and unique to patients with LDH. Further neuroimaging studies are needed to compare the effects of LDH and other chronic pain diseases on the spontaneous brain activity of patients. Furthermore, the lateralization results presented in our study also require further LDH-related pain side-specific grouping study to clarify this causation.

Systematic review registration

PROSPERO, identifier CRD42022375513.

Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment

Lifestyle interventions have positive neuroprotective effects in aging. However, there are still open questions about how changes in resting-state functional connectivity (rsFC) contribute to cognitive improvements. The Projecte Moviment is a 12-week randomized controlled trial of a multimodal data acquisition protocol that investigated the effects of aerobic exercise (AE), computerized cognitive training (CCT), and their combination (COMB). An initial list of 109 participants was recruited from which a total of 82 participants (62% female; age = 58.38 ± 5.47) finished the intervention with a level of adherence > 80%. Only in the COMB group, we revealed an extended network of 33 connections that involved an increased and decreased rsFC within and between the aDMN/pDMN and a reduced rsFC between the bilateral supplementary motor areas and the right thalamus. No global and especially local rsFC changes due to any intervention mediated the cognitive benefits detected in the AE and COMB groups. Projecte Moviment provides evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.

Brain functional connectivity of hypnosis without target suggestion. An intrinsic hypnosis rs-fMRI study

OBJECTIVE

During hypnosis, significant changes in the BOLD signal associated with the anterior default mode network (DMN) and prefrontal attentional systems have been reported as evidence of dissociation defined since Charcot. However, it remains uncertain whether these changes are mainly attributable to the hypnotic state per se or to the target suggestions used to verify subject's state during neuroimaging studies. The aim of the present study is to evidence the brain in hypnosis, contrasting the common resting state versus neutral hypnosis (hypnosis in the absence of target suggestions).

METHODS

Twenty-four healthy right-handed volunteers (age 28.3 y.o., 12 females) rated moderate hypnotic responsiveness underwent resting state fMRI at 3.0 T in two sessions, once in neutral hypnosis and the other in the common resting state. Each subject's functional data were analyzed for low-frequency BOLD signal correlations seed-to-voxel for the whole brain in the first-level analysis, and seed-to-voxel in a second-level analysis to estimate group results using seeds for five resting state networks: the default mode (DMN), the central executive (CEN), the salience (SaN), the dorso-lateral attention (DAN), and the sensorimotor (SMN) networks.

RESULTS

In general, all network maps of the hypnotic condition presented higher connectivity than those of the resting condition. However, only contrasts for the DAN, SaN, and SMN were statistically significant, including correlated out-of-the-network regions.

CONCLUSION

Parietal and occipital regions displayed increased connectivity across networks, implying dissociation from the frontal cortices. This is the first fMRI intrinsic study of hypnosis without target suggestion.

Assessing processing speed and its neural correlates in the three variants of primary progressive aphasia with a non-verbal tablet-based task

Prior research has revealed distinctive patterns of impaired language abilities across the three variants of Primary Progressive Aphasia (PPA): nonfluent/agrammatic (nfvPPA), logopenic (lvPPA) and semantic (svPPA). However, little is known about whether, and to what extent, non-verbal cognitive abilities, such as processing speed, are impacted in PPA patients. This is because neuropsychological tests typically contain linguistic stimuli and require spoken output, being therefore sensitive to verbal deficits in aphasic patients. The aim of this study is to investigate potential differences in processing speed between PPA patients and healthy controls, and among the three PPA variants, using a brief non-verbal tablet-based task (Match) modeled after the WAIS-III digit symbol coding test, and to determine its neural correlates. Here, we compared performance on the Match task between PPA patients (n = 61) and healthy controls (n = 59) and across the three PPA variants. We correlated performance on Match with voxelwise gray and white matter volumes. We found that lvPPA and nfvPPA patients performed significantly worse on Match than healthy controls and svPPA patients. Worse performance on Match across PPA patients was associated with reduced gray matter volume in specific parts of the left middle frontal gyrus, superior parietal lobule, and precuneus, and reduced white matter volume in the left parietal lobe. To conclude, our behavioral findings reveal that processing speed is differentially impacted across the three PPA variants and provide support for the potential clinical utility of a tabled-based task (Match) to assess non-verbal cognition. In addition, our neuroimaging findings confirm the importance of a set of fronto-parietal regions that previous research has associated with processing speed and executive control. Finally, our behavioral and neuroimaging findings combined indicate that differences in processing speed are largely explained by the unequal distribution of atrophy in these fronto-parietal regions across the three PPA variants.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Task-induced changes in brain entropy

Entropy indicates irregularity of a dynamic system, with higher entropy indicating higher irregularity and more transit states. In the human brain, regional brain entropy (BEN) has been increasingly assessed using resting state fMRI (rs-fMRI), while changes of regional BEN during task-based fMRI have been scarcely studied. The purpose of this study is to characterize task-induced regional BEN alterations using the large Human Connectome Project (HCP) data. To control the potential modulation by the block design, BEN of task-fMRI was calculated from the fMRI images acquired during the task conditions only (task BEN) and then compared to BEN of rs-fMRI (resting BEN). Moreover, BEN was separately calculated from the control blocks of the task-fMRI runs (control BEN) and compared to task BEN. Finally, control BEN was compared to resting BEN to test for residual task effects in the control condition. With respect to resting state, task performance unanimously induced BEN reduction in the peripheral cortical area and BEN increase in the centric part of the sensorimotor and perception networks. Control compared to resting BEN showed similar entropy alterations, suggesting large residual task effects. Task compared to control BEN was characterized by reduced entropy in occipital, orbitofrontal, and parietal regions.

I know why the caged bird sings: Distress tolerant individuals show greater resting state connectivity between ventromedial prefrontal cortex and right amygdala as a function of higher vagal tone

BACKGROUND

Intolerance to psychological distress is associated with various forms of psychopathology, ranging from addiction to mood disturbance. The capacity to withstand aversive affective states is often explained by individual differences in cardiovagal tone as well as resting state connectivity of the ventromedial prefrontal cortex (vmPFC), a region involved in the regulation of emotions and cardio-autonomic tone. However, it is unclear which brain regions involved in distress tolerance show greater resting state functional connectivity (rsFC) as a function of resting heart rate variability (HRV).

METHODS

One-hundred and twenty-six adults, aged 20 to 83.5 years, were selected from a lifespan cohort at the Nathan Kline Institute-Rockland Sample. Participants' distress tolerance levels were assessed based upon performance on the Behavioral Indicator of Resiliency to Distress (BIRD) task. Artifact-free resting-state functional brain scans collected during separate sessions were used. While inside the scanner, a pulse oximeter was used to record beat-to-beat intervals to derive high-frequency heart rate variability (HF-HRV). The relationship between HF-HRV and vmPFC to whole brain functional connectivity was compared between distress tolerant (BIRD completers) and distress intolerant (BIRD non-completers).

RESULTS

Groups did not differ in their history of psychiatric diagnosis. Higher resting HF-HRV was associated with longer total time spent on the BIRD task for the entire sample (r = 0.255, p = 0.004). After controlling for age, gender, body mass index, head motion, and gray matter volume. Distress tolerant individuals showed greater rsFC (p < 0.005 (uncorrected), k = 20) between the vmPFC and default-mode network (DMN) hubs including posterior cingulate cortex/precuneus, medial temporal lobes, and the parahippocampal cortex. As a function of higher resting HF-HRV greater vmPFC connectivity was observed with sub-threshold regions in the right amygdala and left anterior prefrontal cortex, with the former passing small volume correction, in distress tolerant versus distress intolerant individuals.

CONCLUSION

In a lifespan sample of community-dwelling adults, distress tolerant individuals showed greater vmPFC connectivity with anterior and posterior hubs of the DMN compared to distress intolerant individuals. As a function of greater HF-HRV, distress tolerant individuals evidenced greater vmPFC with salience and executive control network hubs. These findings are consistent with deficits in neural resource allocation within a triple network resting amongst persons exhibiting behavioral intolerance to psychological distress.

Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions

This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.

White matter tracts and executive functions: a review of causal and correlation evidence

Executive functions are high-level cognitive processes involving abilities such as working memory/updating, set-shifting and inhibition. These complex cognitive functions are enabled by interactions among widely distributed cognitive networks, supported by white matter tracts. Executive impairment is frequent in neurological conditions affecting white matter; however, whether specific tracts are crucial for normal executive functions is unclear. We review causal and correlation evidence from studies that used direct electrical stimulation during awake surgery for gliomas, voxel-based and tract-based lesion-symptom mapping, and diffusion tensor imaging to explore associations between the integrity of white matter tracts and executive functions in healthy and impaired adults. The corpus callosum was consistently associated with all executive processes, notably its anterior segments. Both causal and correlation evidence showed prominent support of the superior longitudinal fasciculus to executive functions, notably to working memory. More specifically, strong evidence suggested that the second branch of the superior longitudinal fasciculus is crucial for all executive functions, especially for flexibility. Global results showed left lateralization for verbal tasks and right lateralization for executive tasks with visual demands. The frontal aslant tract potentially supports executive functions, however, additional evidence is needed to clarify whether its involvement in executive tasks goes beyond the control of language. Converging evidence indicates that a right-lateralized network of tracts connecting cortical and subcortical grey matter regions supports the performance of tasks assessing response inhibition, some suggesting a role for the right anterior thalamic radiation. Finally, correlation evidence suggests a role for the cingulum bundle in executive functions, especially in tasks assessing inhibition. We discuss these findings in light of current knowledge about the functional role of these tracts, descriptions of the brain networks supporting executive functions and clinical implications for individuals with brain tumours.

Regional GABA levels modulate abnormal resting-state network functional connectivity and cognitive impairment in multiple sclerosis

More evidence shows that changes in functional connectivity with regard to brain networks and neurometabolite levels correlated to cognitive impairment in multiple sclerosis. However, the neurological basis underlying the relationship among neurometabolite levels, functional connectivity, and cognitive impairment remains unclear. For this purpose, we used a combination of magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging to study gamma-aminobutyric acid and glutamate concentrations in the posterior cingulate cortex, medial prefrontal cortex and left hippocampus, and inter-network functional connectivity in 29 relapsing-remitting multiple sclerosis patients and 34 matched healthy controls. Neuropsychological tests were used to evaluate the cognitive function. We found that relapsing-remitting multiple sclerosis patients demonstrated significantly reduced gamma-aminobutyric acid and glutamate concentrations and aberrant functional connectivity involving cognitive-related networks compared to healthy controls, and both alterations were associated with specific cognition decline. Moreover, mediation analyses indicated that decremented hippocampus gamma-aminobutyric acid levels in relapsing-remitting multiple sclerosis patients mediated the association between inter-network functional connectivity in various components of default mode network and verbal memory deficits. In summary, our findings shed new lights on the essential function of GABAergic system abnormalities in regulating network dysconnectivity and functional connectivity in relapsing-remitting multiple sclerosis patients, suggesting potential novel approach to treatment.

Recent developments and future avenues for human corticospinal neuroimaging

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Translational In Vivo Assays in Behavioral Biology

The failure of preclinical research to advance successful candidate medications in psychiatry has created a paradigmatic crisis in psychiatry. The Research Domain Criteria (RDoC) initiative was designed to remedy this situation with a neuroscience-based approach that employs multimodal and cross-species in vivo methodology to increase the probability of translational findings and, consequently, drug discovery. The present review underscores the feasibility of this methodological approach by briefly reviewing, first, the use of multidimensional and cross-species methodologies in traditional behavioral pharmacology and, subsequently, the utility of this approach in contemporary neuroimaging and electrophysiology research-with a focus on the value of functionally homologous studies in nonhuman and human subjects. The final section provides a brief review of the RDoC, with a focus on the potential strengths and weaknesses of its domain-based underpinnings. Optimistically, this mechanistic and multidimensional approach in neuropsychiatric research will lead to novel therapeutics for the management of neuropsychiatric disorders.

Aberrant resting-state co-activation network dynamics in major depressive disorder

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Grey matter structure within the visual networks in migraine with aura: multivariate and univariate analyses

BACKGROUND

The visual cortex is involved in the generation of migraine aura. Voxel-based multivariate analyses applied to this region may provide complementary information about aura mechanisms relative to the commonly used mass-univariate analyses.

METHODS

Structural images constrained within the functional resting-state visual networks were obtained in migraine patients with (n = 50) and without (n = 50) visual aura and healthy controls (n = 50). The masked images entered a multivariate analysis in which Gaussian process classification was used to generate pairwise models. Generalizability was assessed by five-fold cross-validation and non-parametric permutation tests were used to estimate significance levels. A univariate voxel-based morphometry analysis was also performed.

RESULTS

A multivariate pattern of grey matter voxels within the ventral medial visual network contained significant information related to the diagnosis of migraine with visual aura (aura vs. healthy controls: classification accuracy = 78%, p < 0.001; area under the curve = 0.84, p < 0.001; migraine with aura vs. without aura: classification accuracy = 71%, p < 0.001; area under the curve = 0.73, p < 0.003). Furthermore, patients with visual aura exhibited increased grey matter volume in the medial occipital cortex compared to the two other groups.

CONCLUSIONS

Migraine with visual aura is characterized by multivariate and univariate patterns of grey matter changes within the medial occipital cortex that have discriminative power and may reflect pathological mechanisms.

Analyzing 20 years of Resting-State fMRI Research: Trends and collaborative networks revealed

Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.

Functional connectivity of sensorimotor network is enhanced in spastic diplegic cerebral palsy: A multimodal study using fMRI and MEG

OBJECTIVE

To assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities.

METHODS

We used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests.

RESULTS

In fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population.

CONCLUSIONS

CP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals.

SIGNIFICANCE

Brain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity.

Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism

The promise of individually tailored care for autism has driven efforts to establish biomarkers. This chapter appraises the state of precision-medicine research focused on biomarkers based on the functional brain connectome. This work is grounded on abundant evidence supporting the brain dysconnection model of autism and the advantages of resting-state functional MRI (R-fMRI) for studying the brain in vivo. After considering biomarker requirements of consistency and clinical relevance, we provide a scoping review of R-fMRI studies of individual prediction in autism. In the past 10 years, responding to the availability of open data through the Autism Brain Imaging Data Exchange, machine learning studies have surged. Nearly all have focused on diagnostic label classification. These efforts have shown that autism prediction is feasible using functional connectome markers, with accuracy reported well above chance. In parallel, emerging approaches more directly addressing autism heterogeneity are paving the way for much-needed biomarkers of longitudinal outcome and treatment response. We conclude with key challenges to be addressed by the next generation of studies.

Controlling target brain regions by optimal selection of input nodes

The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy

The objective of this study is to extract pathological brain networks from interictal period of E/MEG recordings to localize epileptic foci for presurgical evaluation. We proposed here a resting state E/MEG analysis framework, to disentangle brain functional networks represented by neural oscillations. By using an Embedded Hidden Markov Model, we constructed a state space for resting state recordings consisting of brain states with different spatiotemporal patterns. Functional connectivity analysis along with graph theory was applied on the extracted brain states to quantify the network features of the extracted brain states, based on which the source location of pathological states is determined. The method is evaluated by computer simulations and our simulation results revealed the proposed framework can extract brain states with high accuracy regarding both spatial and temporal profiles. We further evaluated the framework as compared with intracranial EEG defined seizure onset zone in 10 patients with drug-resistant focal epilepsy who underwent MEG recordings and were seizure free after surgical resection. The real patient data analysis showed very good localization results using the extracted pathological brain states in 6/10 patients, with localization error of about 15 mm as compared to the seizure onset zone. We show that the pathological brain networks can be disentangled from the resting-state electromagnetic recording and could be identified based on the connectivity features. The framework can serve as a useful tool in extracting brain functional networks from noninvasive resting state electromagnetic recordings, and promises to offer an alternative to aid presurgical evaluation guiding intracranial EEG electrodes implantation.

Concordant dynamic changes of global network properties in the frontoparietal and limbic compartments: An EEG study

INTRODUCTION

Despite its complexity, deciphering nodal interaction is imperative to understanding a neural network. Network interaction is an even more complicated topic that must be addressed. This study aimed to examine the relationship between the brain waves of two canonical brain structures, i.e., the frontoparietal and limbic compartments, during a resting state.

METHODS

Electroencephalography (EEG) of 51 subjects in eye-closed condition was analyzed, and the eLORETA method was applied to convert the signals from the scalp to the brain. By way of community detection, representative neural nodes and the associated mean activities were retrieved. Total and lagged coherences were computed to indicate functional connectivity between those neural nodes. Two global network properties were elucidated based on the connectivity measures, i.e., global efficiency and mean functional connectivity strength. The temporal correlation of the global network indices between the two studied networks was explored.

RESULTS

It was found that there was a significant trend of positive correlation across the four metrics (lagged vs. total coherence x global efficiency vs. average connectivity). In other words, when the neural interaction in the FP network was stronger, so did that in the limbic network, and vice versa. Notably, the above interaction was not spectrally specific and only existed at a finer temporal scale (under hundreds of milliseconds level).

CONCLUSION

The concordant change in network properties indicates an intricate balance between FP and LM compartments. Possible mechanisms and implications for the findings are discussed.

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an "emotional" brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a "rational brain". These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the "emotional/sensory brain", as well as over-inhibition of the "rational brain" by the "emotional/sensory brain". The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the "rational brain" to the "emotional/sensory brain" in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment.

Visual stimulation by extensive visual media consumption can be beneficial for motor learning

In this randomized controlled intervention trial, we investigated whether intense visual stimulation through television watching can enhance visual information processing and motor learning performance. 74 healthy young adults were trained in a motor skill with visual information processing demands while being accommodated in a controlled environment for five days. The experimental manipulation (n = 37) consisted of prolonged television watching (i.e., 8 h/day, + 62.5% on average) to induce intense exposure to visual stimulation. The control group (n = 37) did not consume visual media. The groups were compared by motor learning performance throughout the study as well as pre/post visual attention parameters and resting-state network connectivity in functional MRI. We found that the intervention group performed significantly better in the motor learning task (+ 8.21% (95%-CI[12.04, 4.31], t(70) = 4.23, p < 0.001) while showing an increased capacity of visual short-term memory (+ 0.254, t(58) = - 3.19, p = 0.002) and increased connectivity between visual and motor-learning associated resting-state networks. Our findings suggest that the human brain might enter a state of accentuated visuomotor integration to support the implementation of motor learning with visual information processing demands if challenged by ample input of visual stimulation. Further investigation is needed to evaluate the persistence of this effect regarding participants exposed to accustomed amounts of visual media consumption.Clinical Trials Registration: This trial was registered in the German Clinical Trials Register/Deutsches Register klinischer Studien (DRKS): DRKS00019955.

Processing of the same narrative stimuli elicits common functional connectivity dynamics between individuals

It has been suggested that conscious experience is linked to the richness of brain state repertories, which change in response to environmental and internal stimuli. High-level sensory stimulation has been shown to alter local brain activity and induce neural synchrony across participants. However, the dynamic interplay of cognitive processes underlying moment-to-moment information processing remains poorly understood. Using naturalistic movies as an ecological laboratory model of the real world, here we investigate how the processing of complex naturalistic stimuli alters the dynamics of brain network interactions and how these in turn support information processing. Participants underwent fMRI recordings during movie watching, scrambled movie watching, and resting. By measuring the phase-synchrony between different brain networks, we analyzed whole-brain connectivity patterns. Our finding revealed distinct connectivity patterns associated with each experimental condition. We found higher synchronization of brain patterns across participants during movie watching compared to rest and scrambled movie conditions. Furthermore, synchronization levels increased during the most engaging parts of the movie. The synchronization dynamics among participants were associated with suspense; scenes with higher levels of suspense induced greater synchronization. These results suggest that processing the same high-level information elicits common neural dynamics across individuals, and that whole-brain functional connectivity tracks variations in processed information and subjective experience.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.

Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network

In healthy subjects, activity in the default mode network (DMN) and the frontoparietal network (FPN) has consistently been associated with working memory (WM). In particular, the dorsolateral prefrontal cortex (DLPFC) is important for WM. The functional-anatomical basis of WM impairment in glioma patients is, however, still poorly understood. We investigated whether WM performance of glioma patients is reflected in resting-state functional connectivity (FC) between the DMN and FPN, additionally focusing on the DLPFC. Resting-state functional MRI data were acquired from 45 glioma patients prior to surgery. WM performance was derived from a pre-operative N-back task. Scans were parcellated into ROIs using both the Gordon and Yeo atlas. FC was calculated as the average Pearson correlation between functional time series. The FC between right DLPFC and DMN was inversely related to WM performance for both the Gordon and Yeo atlas (p = .010). No association was found for FC between left DLPFC and DMN, nor between the whole FPN and DMN. The results are robust and not dependent on atlas choice or tumor location, as they hold for both the Gordon and Yeo atlases, and independently of location variables. Our findings show that WM performance of glioma patients can be quantified in terms of interactions between regions and large-scale networks that can be measured with resting-state fMRI. These group-based results are a necessary step toward development of biomarkers for clinical management of glioma patients, and provide additional evidence that global disruption of the DMN relates to cognitive impairment in glioma patients.

Resting-state functional connectivity in an auditory network differs between aspiring professional and amateur musicians and correlates with performance

Auditory experience-dependent plasticity is often studied in the domain of musical expertise. Available evidence suggests that years of musical practice are associated with structural and functional changes in auditory cortex and related brain regions. Resting-state functional magnetic resonance imaging (MRI) can be used to investigate neural correlates of musical training and expertise beyond specific task influences. Here, we compared two groups of musicians with varying expertise: 24 aspiring professional musicians preparing for their entrance exam at Universities of Arts versus 17 amateur musicians without any such aspirations but who also performed music on a regular basis. We used an interval recognition task to define task-relevant brain regions and computed functional connectivity and graph-theoretical measures in this network on separately acquired resting-state data. Aspiring professionals performed significantly better on all behavioral indicators including interval recognition and also showed significantly greater network strength and global efficiency than amateur musicians. Critically, both average network strength and global efficiency were correlated with interval recognition task performance assessed in the scanner, and with an additional measure of interval identification ability. These findings demonstrate that task-informed resting-state fMRI can capture connectivity differences that correspond to expertise-related differences in behavior.

Sex matters: acute functional connectivity changes as markers of remission in late-life depression differ by sex

The efficacy of antidepressant treatment in late-life is modest, a problem magnified by an aging population and increased prevalence of depression. Understanding the neurobiological mechanisms of treatment response in late-life depression (LLD) is imperative. Despite established sex differences in depression and neural circuits, sex differences associated with fMRI markers of antidepressant treatment response are underexplored. In this analysis, we assess the role of sex on the relationship of acute functional connectivity changes with treatment response in LLD. Resting state fMRI scans were collected at baseline and day one of SSRI/SNRI treatment for 80 LLD participants. One-day changes in functional connectivity (differential connectivity) were related to remission status after 12 weeks. Sex differences in differential connectivity profiles that distinguished remitters from non-remitters were assessed. A random forest classifier was used to predict the remission status with models containing various combinations of demographic, clinical, symptomatological, and connectivity measures. Model performance was assessed with area under the curve, and variable importance was assessed with permutation importance. The differential connectivity profile associated with remission status differed significantly by sex. We observed evidence for a difference in one-day connectivity changes between remitters and non-remitters in males but not females. Additionally, prediction of remission was significantly improved in male-only and female-only models over pooled models. Predictions of treatment outcome based on early changes in functional connectivity show marked differences between sexes and should be considered in future MR-based treatment decision-making algorithms.

A systematic review exploring the association between the human gut microbiota and brain connectivity in health and disease

A body of pre-clinical evidence shows how the gut microbiota influence brain functioning, including brain connectivity. Linking measures of brain connectivity to the gut microbiota can provide important mechanistic insights into the bi-directional gut-brain communication. In this systematic review, we therefore synthesized the available literature assessing this association, evaluating the degree of consistency in microbiota-connectivity associations. Following the PRISMA guidelines, a PubMed search was conducted, including studies published up to September 1, 2022. We identified 16 studies that met the inclusion criteria. Several bacterial genera, including Prevotella, Bacteroides, Ruminococcus, Blautia, and Collinsella were most frequently reported in association with brain connectivity. Additionally, connectivity of the salience (specifically the insula and anterior cingulate cortex), default mode, and frontoparietal networks were most frequently associated with the gut microbiota, both in terms of microbial diversity and composition. There was no discernible pattern in the association between microbiota and brain connectivity. Altogether, based on our synthesis, there is evidence for an association between the gut microbiota and brain connectivity. However, many findings were poorly replicated across studies, and the specificity of the association is yet unclear. The current studies show substantial inter-study heterogeneity in methodology and reporting, limiting the robustness and reproducibility of the findings and emphasizing the need to harmonize methodological approaches. To enhance comparability and replicability, future research should focus on further standardizing processing pipelines and employing data-driven multivariate analysis strategies.

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Functional magnetic resonance imaging (fMRI) studies often estimate brain intrinsic connectivity networks (ICNs) from temporal relationships between hemodynamic signals using approaches such as independent component analysis (ICA). While ICNs are thought to represent functional sources that play important roles in various psychological phenomena, current approaches have been tailored to identify ICNs that mainly reflect linear statistical relationships. However, the elements comprising neural systems often exhibit remarkably complex nonlinear interactions that may be involved in cognitive operations and altered in psychiatric conditions such as schizophrenia. Consequently, there is a need to develop methods capable of effectively capturing ICNs from measures that are sensitive to nonlinear relationships. Here, we advance a novel approach to estimate ICNs from explicitly nonlinear whole-brain functional connectivity (ENL-wFC) by transforming resting-state fMRI (rsfMRI) data into the connectivity domain, allowing us to capture unique information from distance correlation patterns that would be missed by linear whole-brain functional connectivity (LIN-wFC) analysis. Our findings provide evidence that ICNs commonly extracted from linear (LIN) relationships are also reflected in explicitly nonlinear (ENL) connectivity patterns. ENL ICN estimates exhibit higher reliability and stability, highlighting our approach's ability to effectively quantify ICNs from rsfMRI data. Additionally, we observed a consistent spatial gradient pattern between LIN and ENL ICNs with higher ENL weight in core ICN regions, suggesting that ICN function may be subserved by nonlinear processes concentrated within network centers. We also found that a uniquely identified ENL ICN distinguished individuals with schizophrenia from healthy controls while a uniquely identified LIN ICN did not, emphasizing the valuable complementary information that can be gained by incorporating measures that are sensitive to nonlinearity in future analyses. Moreover, the ENL estimates of ICNs associated with auditory, linguistic, sensorimotor, and self-referential processes exhibit heightened sensitivity towards differentiating between individuals with schizophrenia and controls compared to LIN counterparts, demonstrating the translational value of our approach and of the ENL estimates of ICNs that are frequently reported as disrupted in schizophrenia. In summary, our findings underscore the tremendous potential of connectivity domain ICA and nonlinear information in resolving complex brain phenomena and revolutionizing the landscape of clinical FC analysis.

Increased functional connectivity between the auditory cortex and the frontoparietal network compensates for impaired visuomotor transformation after early auditory deprivation

Early auditory deprivation leads to a reorganization of large-scale brain networks involving and extending beyond the auditory system. It has been documented that visuomotor transformation is impaired after early deafness, associated with a hyper-crosstalk between the task-critical frontoparietal network and the default-mode network. However, it remains unknown whether and how the reorganized large-scale brain networks involving the auditory cortex contribute to impaired visuomotor transformation after early deafness. Here, we asked deaf and early hard of hearing participants and normal hearing controls to judge the spatial location of a visual target. Compared with normal hearing controls, the superior temporal gyrus showed significantly increased functional connectivity with the frontoparietal network and the default-mode network in deaf and early hard of hearing participants, specifically during egocentric judgments. However, increased superior temporal gyrus-frontoparietal network and superior temporal gyrus-default-mode network coupling showed antagonistic effects on egocentric judgments. In deaf and early hard of hearing participants, increased superior temporal gyrus-frontoparietal network connectivity was associated with improved egocentric judgments, whereas increased superior temporal gyrus-default-mode network connectivity was associated with deteriorated performance in the egocentric task. Therefore, the data suggest that the auditory cortex exhibits compensatory neuroplasticity (i.e. increased functional connectivity with the task-critical frontoparietal network) to mitigate impaired visuomotor transformation after early auditory deprivation.

Functional neuroimaging as a catalyst for integrated neuroscience

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Living on the edge: network neuroscience beyond nodes

Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.

Association between gene expression and functional-metabolic architecture in Parkinson's disease

Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR  < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR  < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.

An automated pipeline for obtaining labeled ICA-templates corresponding to functional brain systems

The complexity of our actions and thinking is likely reflected in functional brain networks. Independent component analysis (ICA) is a popular data-driven method to compute group differences between such networks. A common way to investigate network differences is based on ICA maps which are generated from study-specific samples. However, this approach limits the generalizability and reproducibility of the results. Alternatively, network ICA templates can be used, but up to date, few such templates exist and are limited in terms of the functional systems they cover. Here, we propose a simple two-step procedure to obtain ICA-templates corresponding to functional brain systems of the researcher's choice: In step 1, the functional system of interest needs to be defined by means of a statistical parameter map (input), which one can generate with open-source software such as NeuroSynth or BrainMap. In step 2, that map is correlated to group-ICA maps provided by the Human Connectome Project (HCP), which is based on a large sample size and uses high quality and standardized acquisition procedures. The HCP-provided ICA-map with the highest correlation to the input map is then used as an ICA template representing the functional system of interest, for example, for subsequent analyses such as dual regression. We provide a toolbox to complete step 2 of the suggested procedure and demonstrate the usage of our pipeline by producing an ICA templates that corresponds to "motor function" and nine additional brain functional systems resulting in an ICA maps with excellent alignment with the gray matter/white matter boundaries of the brain. Our toolbox generates data in two different file formats: volumetric-based (NIFTI) and combined surface/volumetric files (CIFTI). Compared to 10 existing templates, our procedure output component maps with systematically stronger contribution of gray matter to the ICA z-values compared to white matter voxels in 9/10 cases by at least a factor of 2. The toolbox allows users to investigate functional networks of interest, which will enhance interpretability, reproducibility, and standardization of research investigating functional brain networks.

Comparative analysis of group information-guided independent component analysis and independent vector analysis for assessing brain functional network characteristics in autism spectrum disorder

Introduction

Group information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA) are two methods that improve estimation of subject-specific independent components in neuroimaging studies. These methods have shown better performance than traditional group independent component analysis (GICA) with respect to intersubject variability (ISV).

Methods

In this study, we compared the patterns of community structure, spatial variance, and prediction performance of GIG-ICA and IVA-GL, respectively. The dataset was obtained from the publicly available Autism Brain Imaging Data Exchange (ABIDE) database, comprising 75 healthy controls (HC) and 102 Autism Spectrum Disorder (ASD) participants. The greedy rule was used to match components from IVA-GL and GIG-ICA in order to compare the similarities between the two methods.

Results

Robust correspondence was observed between the two methods the following networks: cerebellum network (CRN; |r| = 0.7813), default mode network (DMN; |r| = 0.7263), self-reference network (SRN; |r| = 0.7818), ventral attention network (VAN; |r| = 0.7574), and visual network (VSN; |r| = 0.7503). Additionally, the Sensorimotor Network demonstrated the highest similarity between IVA-GL and GIG-ICA (SOM: |r| = 0.8125). Our findings revealed a significant difference in the number of modules identified by the two methods (HC: p < 0.001; ASD: p < 0.001). GIG-ICA identified significant differences in FNC between HC and ASD compared to IVA-GL. However, in correlation analysis, IVA-GL identified a statistically negative correlation between FNC of ASD and the social total subscore of the classic Autism Diagnostic Observation Schedule (ADOS: pi = -0.26, p = 0.0489). Moreover, both methods demonstrated similar prediction performances on age within specific networks, as indicated by GIG-ICA-CRN (R2 = 0.91, RMSE = 3.05) and IVA-VAN (R2 = 0.87, RMSE = 3.21).

Conclusion

In summary, IVA-GL demonstrated lower modularity, suggesting greater sensitivity in estimating networks with higher intersubject variability. The improved age prediction of cerebellar-attention networks underscores their importance in the developmental progression of ASD. Overall, IVA-GL may be appropriate for investigating disorders with greater variability, while GIG-ICA identifies functional networks with distinct modularity patterns.

Changes of cerebral functional connectivity induced by foot reflexology in a RCT

Non-Pharmacological Interventions (NPIs) are increasingly being introduced into healthcare, but their mechanisms are unclear. In this study, 30 healthy participants received foot reflexology (FR) and sham massage, and went through a resting-state functional magnetic resonance imaging (rs-fMRI) to evaluate NPIs effect on brain. Rs-fMRI revealed an effect of both NPIs on functional connectivity with changes occurring in the default-mode network, the sensorimotor network and a Neural Network Correlates of Pain (NNCP-a newly discovered network showing great robustness). Even if no differences were found between FR and SM, this study allowed to report brain biomarkers of well-being as well as the safety of NPIs. In further research, it could be relevant to study it in patients to look for a true reflexology induced-effect dependent of patient reported outcomes. Overall, these findings enrich the understanding of the neural correlates of well-being experienced with NPIs and provided insight into the basis of the mechanisms of NPIs.

Substructure of the brain's Cingulo-Opercular network

The Cingulo-Opercular network (CON) is an executive network of the human brain that regulates actions. CON is composed of many widely distributed cortical regions that are involved in top-down control over both lower-level (i.e., motor) and higher-level (i.e., cognitive) functions, as well as in processing of painful stimuli. Given the topographical and functional heterogeneity of the CON, we investigated whether subnetworks within the CON support separable aspects of action control. Using precision functional mapping (PFM) in 15 participants with > 5 hours of resting state functional connectivity (RSFC) and task data, we identified three anatomically and functionally distinct CON subnetworks within each individual. These three distinct subnetworks were linked to Decisions, Actions, and Feedback (including pain processing), respectively, in convergence with a meta-analytic task database. These Decision, Action and Feedback subnetworks represent pathways by which the brain establishes top-down goals, transforms those goals into actions, implemented as movements, and processes critical action feedback such as pain.

Large-scale brain functional network abnormalities in social anxiety disorder

BACKGROUND

Although aberrant brain regional responses are reported in social anxiety disorder (SAD), little is known about resting-state functional connectivity at the macroscale network level. This study aims to identify functional network abnormalities using a multivariate data-driven method in a relatively large and homogenous sample of SAD patients, and assess their potential diagnostic value.

METHODS

Forty-six SAD patients and 52 demographically-matched healthy controls (HC) were recruited to undergo clinical evaluation and resting-state functional MRI scanning. We used group independent component analysis to characterize the functional architecture of brain resting-state networks (RSNs) and investigate between-group differences in intra-/inter-network functional network connectivity (FNC). Furtherly, we explored the associations of FNC abnormalities with clinical characteristics, and assessed their ability to discriminate SAD from HC using support vector machine analyses.

RESULTS

SAD patients showed widespread intra-network FNC abnormalities in the default mode network, the subcortical network and the perceptual system (i.e. sensorimotor, auditory and visual networks), and large-scale inter-network FNC abnormalities among those high-order and primary RSNs. Some aberrant FNC signatures were correlated to disease severity and duration, suggesting pathophysiological relevance. Furthermore, intrinsic FNC anomalies allowed individual classification of SAD v. HC with significant accuracy, indicating potential diagnostic efficacy.

CONCLUSIONS

SAD patients show distinct patterns of functional synchronization abnormalities both within and across large-scale RSNs, reflecting or causing a network imbalance of bottom-up response and top-down regulation in cognitive, emotional and sensory domains. Therefore, this could offer insights into the neurofunctional substrates of SAD.

Longitudinal multiparametric MRI of traumatic spinal cord injury in animal models

Multi-parametric MRI (mpMRI) technology enables non-invasive and quantitative assessments of the structural, molecular, and functional characteristics of various neurological diseases. Despite the recognized importance of studying spinal cord pathology, mpMRI applications in spinal cord research have been somewhat limited, partly due to technical challenges associated with spine imaging. However, advances in imaging techniques and improved image quality now allow longitudinal investigations of a comprehensive range of spinal cord pathological features by exploiting different endogenous MRI contrasts. This review summarizes the use of mpMRI techniques including blood oxygenation level-dependent (BOLD) functional MRI (fMRI), diffusion tensor imaging (DTI), quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST) MRI in monitoring different aspects of spinal cord pathology. These aspects include cyst formation and axonal disruption, demyelination and remyelination, changes in the excitability of spinal grey matter and the integrity of intrinsic functional circuits, and non-specific molecular changes associated with secondary injury and neuroinflammation. These approaches are illustrated with reference to a nonhuman primate (NHP) model of traumatic cervical spinal cord injuries (SCI). We highlight the benefits of using NHP SCI models to guide future studies of human spinal cord pathology, and demonstrate how mpMRI can capture distinctive features of spinal cord pathology that were previously inaccessible. Furthermore, the development of mechanism-based MRI biomarkers from mpMRI studies can provide clinically useful imaging indices for understanding the mechanisms by which injured spinal cords progress and repair. These biomarkers can assist in the diagnosis, prognosis, and evaluation of therapies for SCI patients, potentially leading to improved outcomes.

Resolving inter-regional communication capacity in the human connectome

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

Lateralization of the cerebral network of inhibition in children before and after cognitive training

Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. IC relies on a lateralized cortico-subcortical brain network including the inferior frontal cortex, anterior parts of insula, anterior cingulate cortex, caudate nucleus and putamen. Brain asymmetries play a critical role for IC efficiency. In parallel to age-related changes, IC can be improved following training. The aim of this study was to (1) assess the lateralization of IC network in children (N = 60, 9-10 y.o.) and (2) examine possible changes in neural asymmetry of this network from anatomical (structural MRI) and functional (resting-state fMRI) levels after 5-week computerized IC vs. active control (AC) training. We observed that IC training, but not AC training, led to a leftward lateralization of the putamen anatomy, similarly to what is observed in adults, supporting that training could accelerate the maturation of this structure.

Brain-wide mapping of resting-state networks in mice using high-frame rate functional ultrasound

Functional ultrasound (fUS) imaging is a method for visualizing deep brain activity based on cerebral blood volume changes coupled with neural activity, while functional MRI (fMRI) relies on the blood-oxygenation-level-dependent signal coupled with neural activity. Low-frequency fluctuations (LFF) of fMRI signals during resting-state can be measured by resting-state fMRI (rsfMRI), which allows functional imaging of the whole brain, and the distributions of resting-state network (RSN) can then be estimated from these fluctuations using independent component analysis (ICA). This procedure provides an important method for studying cognitive and psychophysiological diseases affecting specific brain networks. The distributions of RSNs in the brain-wide area has been reported primarily by rsfMRI. RSNs using rsfMRI are generally computed from the time-course of fMRI signals for more than 5 min. However, a recent dynamic functional connectivity study revealed that RSNs are still not perfectly stable even after 10 min. Importantly, fUS has a higher temporal resolution and stronger correlation with neural activity compared with fMRI. Therefore, we hypothesized that fUS applied during the resting-state for a shorter than 5 min would provide similar RSNs compared to fMRI. High temporal resolution rsfUS data were acquired at 10 Hz in awake mice. The quality of the default mode network (DMN), a well-known RSN, was evaluated using signal-noise separation (SNS) applied to different measurement durations of rsfUS. The results showed that the SNS did not change when the measurement duration was increased to more than 210 s. Next, we measured short-duration rsfUS multi-slice measurements in the brain-wide area. The results showed that rsfUS with the short duration succeeded in detecting RSNs distributed in the brain-wide area consistent with RSNs detected by 11.7-T MRI under awake conditions (medial prefrontal cortex and cingulate cortex in the anterior DMN, retrosplenial cortex and visual cortex in the posterior DMN, somatosensory and motor cortexes in the lateral cortical network, thalamus, dorsal hippocampus, and medial cerebellum), confirming the reliability of the RSNs detected by rsfUS. However, bilateral RSNs located in the secondary somatosensory cortex, ventral hippocampus, auditory cortex, and lateral cerebellum extracted from rsfUS were different from the unilateral RSNs extracted from rsfMRI. These findings indicate the potential of rsfUS as a method for analyzing functional brain networks and should encourage future research to elucidate functional brain networks and their relationships with disease model mice.

Echoes from Intrinsic Connectivity Networks in the Subcortex

Decades of research have greatly improved our understanding of intrinsic human brain organization in terms of functional networks and the transmodal hubs within the cortex at which they converge. However, substrates of multinetwork integration in the human subcortex are relatively uncharted. Here, we leveraged recent advances in subcortical atlasing and ultra-high field (7 T) imaging optimized for the subcortex to investigate the functional architecture of 14 individual structures in healthy adult males and females with a fully data-driven approach. We revealed that spontaneous neural activity in subcortical regions can be decomposed into multiple independent subsignals that correlate with, or "echo," the activity in functional networks across the cortex. Distinct subregions of the thalamus, striatum, claustrum, and hippocampus showed a varied pattern of echoes from attention, control, visual, somatomotor, and default mode networks, demonstrating evidence for a heterogeneous organization supportive of functional integration. Multiple network activity furthermore converged within the globus pallidus externa, substantia nigra, and ventral tegmental area but was specific to one subregion, while the amygdala and pedunculopontine nucleus preferentially affiliated with a single network, showing a more homogeneous topography. Subregional connectivity of the globus pallidus interna, subthalamic nucleus, red nucleus, periaqueductal gray, and locus coeruleus did not resemble patterns of cortical network activity. Together, these finding describe potential mechanisms through which the subcortex participates in integrated and segregated information processing and shapes the spontaneous cognitive dynamics during rest.SIGNIFICANCE STATEMENT Despite the impact of subcortical dysfunction on brain health and cognition, large-scale functional mapping of subcortical structures severely lags behind that of the cortex. Recent developments in subcortical atlasing and imaging at ultra-high field provide new avenues for studying the intricate functional architecture of the human subcortex. With a fully data-driven analysis, we reveal subregional connectivity profiles of a large set of noncortical structures, including those rarely studied in fMRI research. The results have implications for understanding how the functional organization of the subcortex facilitates integrative processing through cross-network information convergence, paving the way for future work aimed at improving our knowledge of subcortical contributions to intrinsic brain dynamics and spontaneous cognition.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Brain optimization with additional study time: potential brain differences between high- and low-performance college students

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8-9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students' diverse academic needs.