Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis

Brain functional alternation in patients with systemic sclerosis: a resting-state functional magnetic resonance imaging study

BACKGROUND

Neuropsychiatric manifestations, such as cognitive impairment, are relatively prevalent in systemic sclerosis (SSc) patients. This study aimed to investigate the resting state (RS) functional alternations of SSc patients and the potential influenced factors.

METHODS

Forty-four SSc patients (mean age, 46.3 ± 11.4 years; 40 females) and 19 age and sex comparable healthy volunteers (mean age, 42.6 ± 11.3 years; 16 females) were recruited and underwent RS functional MR imaging (fMRI) and neuropsychological assessments. Functional segregation analysis was performed to calculate the amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo). Functional integration analysis was conducted using group independent component analysis to calculate intra-network and inter-network functional connectivity (FC). The fMRI measurements were compared between SSc patients and healthy volunteers using voxel-based pairwise two-sample t-tests. The correlations between clinical characteristics and fMRI measurements were also analyzed.

RESULTS

Compared to healthy volunteers, SSc patients exhibited significantly decreased ALFF and increased ReHo (all P < 0.01, FWE corrected). SSc patients predominantly showed decreased intra-network and inter-network FC in the auditory network, visual network, default mode network, frontoparietal network and attention network (intra-network FC: P < 0.01, uncorrected, cluster size > 30; inter-network FC: P < 0.05, FDR correction). Furthermore, clinical characteristics including disease duration (r value ranged from - 0.31 to 0.36), elevated erythrocyte sedimentation rate (r = 0.35), Montreal Cognitive Assessment score (r = 0.43), and Hamilton Depression Scale score (r = -0.40) were significantly associated with fMRI measurements (all P < 0.05).

CONCLUSIONS

Spontaneous activity and functional connectivity alternations can be seen in SSc patients, which are partially associated with neuropsychiatric manifestations and tend to aggravate with disease duration.

UCLN: Toward the Causal Understanding of Brain Disorders With Temporal Lag Dynamics

Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool for exploring interactions among brain regions. A growing body of research is actively investigating various computational approaches for estimating causal effects among brain regions. Compared to traditional methods, causal relationship reveals the causal influences among distinct brain regions, offering a deeper understanding of brain network dynamics. However, existing methods either neglect the concept of temporal lag across brain regions or set the temporal lag value to a fixed value. To address this limitation, we propose a Unified Causal and Temporal Lag Network (termed UCLN) that jointly learns the causal effects and temporal lag values among brain regions. Our method effectively captures variations in temporal lag between distant brain regions by avoiding the predefined lag value across the entire brain. The brain networks obtained are directed and weighted graphs, enabling a more comprehensive disentanglement of complex interactions. In addition, we also introduce three guiding mechanisms for efficient brain network modeling. The proposed method outperforms state-of-the-art approaches in classification accuracy on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our findings indicate that the method not only achieves superior classification but also successfully identifies crucial neuroimaging biomarkers associated with the disease.

Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis

Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.

2.5-Year changes of connectivity dynamism are relevant for physical and cognitive deterioration in multiple sclerosis

BACKGROUND

In MS, functional connectivity (FC) dynamism may influence disease evolution.

OBJECTIVES

The objective is to assess time-varying functional connectivity (TVFC) changes over time at 2.5-year follow-up in MS patients according to physical and cognitive worsening.

METHODS

We collected 3T magnetic resonance imaging (MRI) for TVFC assessment (performed using sliding-window analysis of centrality) and clinical evaluations at baseline and 2.5-year follow-up from 28 healthy controls and 129 MS patients. Of these, 79 underwent baseline and follow-up neuropsychological assessment. At 2.5 years, physical/cognitive worsening was defined according to disability/neuropsychological score changes.

RESULTS

At follow-up, 25/129 (19.3%) MS patients worsened physically and 14/79 (17.7%) worsened cognitively. At baseline, MS patients showed reduced TVFC versus controls. At 2.5-year follow-up, no TVFC changes were detected in controls. Conversely, TVFC decreased over time in parieto-temporal regions in stable MS patients and in default-mode network in worsened MS. In physically worsened MS, basal ganglia TVFC reductions were also found. Reduced TVFC over time in the putamen in physically worsened and reduced TVFC in the precuneus in cognitively worsened were significant versus stable MS.

DISCUSSION

At 2.5-year follow-up, default-mode network TVFC reductions were found in worsening MS. Moreover, reduced deep gray matter TVFC characterized physically worsened patients, whereas precuneus involvement characterized cognitively worsened MS patients.

A synergetic turn in cognitive neuroscience of brain diseases

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.

White matter modifications of corpus callosum in bipolar disorder: A DTI tractography review

BACKGROUND

The recent widespread use of diffusion tensor imaging (DTI) tractography allowed researchers to investigate the diffusivity modifications and neuroanatomical changes of white matter (WM) fascicles in major psychiatric disorders, including bipolar disorder (BD). In BD, corpus callosum (CC) seems to have a crucial role in explaining the pathophysiology and cognitive impairment of this psychiatric disorder. This review aims to provide an overview on the latest results emerging from studies that investigated neuroanatomical changes of CC in BD using DTI tractography.

METHODS

Bibliographic research was conducted on PubMed, Scopus and Web of Science datasets until March 2022. Ten studies fulfilled our inclusion criteria.

RESULTS

From the reviewed DTI tractography studies a significant decrease of fractional anisotropy emerged in the genu, body and splenium of CC of BD patients compared to controls. This finding is coupled with reduction of fiber density and modification in fiber tract length. Finally, an increase of radial and mean diffusivity in forceps minor and in the entire CC was also reported.

LIMITATIONS

Small sample size, heterogeneity in terms of methodological (diffusion gradient) and clinical (lifetime comorbidity, BD status, pharmacological treatments) characteristics.

CONCLUSIONS

Overall, these findings suggest the presence of structural modifications in CC in BD patients, which may in turn explain the cognitive impairments often observed in this psychiatric disorder, especially in executive processing, motor control and visual memory. Finally, structural modifications may suggest an impairment in the amount of functional information and a morphological impact within those brain regions connected by CC.

Emerging Perspectives on MRI Application in Multiple Sclerosis: Moving from Pathophysiology to Clinical Practice

MRI plays a central role in the diagnosis of multiple sclerosis (MS) and in the monitoring of disease course and treatment response. Advanced MRI techniques have shed light on MS biology and facilitated the search for neuroimaging markers that may be applicable in clinical practice. MRI has led to improvements in the accuracy of MS diagnosis and a deeper understanding of disease progression. This has also resulted in a plethora of potential MRI markers, the importance and validity of which remain to be proven. Here, five recent emerging perspectives arising from the use of MRI in MS, from pathophysiology to clinical application, will be discussed. These are the feasibility of noninvasive MRI-based approaches to measure glymphatic function and its impairment; T1-weighted to T2-weighted intensity ratio to quantify myelin content; classification of MS phenotypes based on their MRI features rather than on their clinical features; clinical relevance of gray matter atrophy versus white matter atrophy; and time-varying versus static resting-state functional connectivity in evaluating brain functional organization. These topics are critically discussed, which may guide future applications in the field.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Dynamic functional connectivity changes of resting-state brain network in attention-deficit/hyperactivity disorder

Patients with attention-deficit/hyperactivity disorder (ADHD) have shown abnormal functional connectivity and network disruptions at the whole-brain static level. However, the changes in brain networks in ADHD patients from dynamic functional connectivity (DFC) perspective have not been fully understood. Accordingly, we executed DFC analysis on resting-state fMRI data of 25 ADHD patients and 27 typically developing (TD) children. A sliding window and Pearson correlation were used to construct the dynamic brain network of all subjects. The k-means+ + clustering method was used to recognize three recurring DFC states, and finally, the mean dwell time, the fraction of time spent for each state, and graph theory metrics were quantified for further analysis. Our results showed that ADHD patients had abnormally increased mean dwell time and the fraction of time spent in state 2, which reached a significant level (p < 0.05). In addition, a weak correlation between the default mode network was associated in three states, and the positive correlations between visual network and attention network were smaller than TD in three states. Finally, the integration of each network node of ADHD in state 2 is more potent than that of TD, and the degree of node segregation is smaller than that of TD. These findings provide new evidence for the DFC study of ADHD; dynamic changes may better explain the developmental delay of ADHD and have particular significance for studying neurological mechanisms and adjuvant therapy of ADHD.

Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study

Background

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques.

Methods

In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed.

Results

Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition.

Conclusion

The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.

Increased flexibility of brain dynamics in patients with multiple sclerosis

Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, P_FDR = 0.024), promiscuity (t = 1.94, P_FDR = 0.038), entropy (t = 2.17, P_FDR = 0.027) and cohesion (t = 2.45, P_FDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.

Microstates in multiple sclerosis: an electrophysiological signature of altered large-scale networks functioning?

Multiple sclerosis has a highly variable course and disabling symptoms even in absence of associated imaging data. This clinical-radiological paradox has motivated functional studies with particular attention to the resting-state networks by functional MRI. The EEG microstates analysis might offer advantages to study the spontaneous fluctuations of brain activity. This analysis investigates configurations of voltage maps that remain stable for 80-120 ms, termed microstates. The aim of our study was to investigate the temporal dynamic of microstates in patients with multiple sclerosis, without reported cognitive difficulties, and their possible correlations with clinical and neuropsychological parameters. We enrolled fifty relapsing-remitting multiple sclerosis patients and 24 healthy subjects, matched for age and sex. Demographic and clinical data were collected. All participants underwent to high-density EEG in resting-state and analyzed 15 min free artefact segments. Microstates analysis consisted in two processes: segmentation, to identify specific templates, and back-fitting, to quantify their temporal dynamic. A neuropsychological assessment was performed by the Brief International Cognitive Assessment for Multiple Sclerosis. Repeated measures two-way ANOVA was run to compare microstates parameters of patients versus controls. To evaluate association between clinical, neuropsychological and microstates data, we performed Pearsons' correlation and stepwise multiple linear regression to estimate possible predictions. The alpha value was set to 0.05. We found six templates computed across all subjects. Significant differences were found in most of the parameters (global explained variance, time coverage, occurrence) for the microstate Class A (P < 0.001), B (P < 0.001), D (P < 0.001), E (P < 0.001) and F (P < 0.001). In particular, an increase of temporal dynamic of Class A, B and E and a decrease of Class D and F were observed. A significant positive association of disease duration with the mean duration of Class A was found. Eight percent of patients with multiple sclerosis were found cognitive impaired, and the multiple linear regression analysis showed a strong prediction of Symbol Digit Modalities Test score by global explained variance of Class A. The EEG microstate analysis in patients with multiple sclerosis, without overt cognitive impairment, showed an increased temporal dynamic of the sensory-related microstates (Class A and B), a reduced presence of the cognitive-related microstates (Class D and F), and a higher activation of a microstate (Class E) associated to the default mode network. These findings might represent an electrophysiological signature of brain reorganization in multiple sclerosis. Moreover, the association between Symbol Digit Modalities Test and Class A may suggest a possible marker of overt cognitive dysfunctions.

Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus

PURPOSE

This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS

A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology. Degree centrality (DC) and functional connectivity (FC) were used to evaluate the changes in brain function.

RESULTS

Compared with HCs, middle-aged patients with T2DM exhibited cortical thickness reductions in the left pars opercularis, left transverse temporal, and right superior temporal gyri. Decreased DC values were observed in the cuneus and precuneus in T2DM. Hub-based FC analysis of these regions revealed lower connectivity in the bilateral hippocampus and parahippocampal gyrus, left precuneus, as well as left frontal sup.

CONCLUSION

Cortical thickness, degree centrality, as well as functional connectivity were found to have significant changes in middle-aged patients with T2DM. Our observations provide potential evidence from neuroimaging for analysis to examine diabetes-related brain damage.

Subcortical-cortical dynamical states of the human brain and their breakdown in stroke

The mechanisms controlling dynamical patterns in spontaneous brain activity are poorly understood. Here, we provide evidence that cortical dynamics in the ultra-slow frequency range (<0.01–0.1 Hz) requires intact cortical-subcortical communication. Using functional magnetic resonance imaging (fMRI) at rest, we identify Dynamic Functional States (DFSs), transient but recurrent clusters of cortical and subcortical regions synchronizing at ultra-slow frequencies. We observe that shifts in cortical clusters are temporally coincident with shifts in subcortical clusters, with cortical regions flexibly synchronizing with either limbic regions (hippocampus/amygdala), or subcortical nuclei (thalamus/basal ganglia). Focal lesions induced by stroke, especially those damaging white matter connections between basal ganglia/thalamus and cortex, provoke anomalies in the fraction times, dwell times, and transitions between DFSs, causing a bias toward abnormal network integration. Dynamical anomalies observed 2 weeks after stroke recover in time and contribute to explaining neurological impairment and long-term outcome.

Favaretto et al. show that the brain rapidly alternates between transient connectivity patterns, with cortical regions flexibly synchronizing with two groups of subcortical regions, and that this dynamic is abnormal in stroke patients.

Time-varying connectivity of the precuneus and its association with cognition and depressive symptoms in neuromyelitis optica: A pilot MRI study

Background:

The precuneus is involved in cognition and depression; static functional connectivity (SFC) abnormalities of this region have been observed in neuromyelitis optica spectrum disorders (NMOSD). Time-varying functional connectivity (TVC) underpins dynamic variations of brain connectivity.

Objective:

The aim of this study was to explore precuneus SFC and TVC in NMOSD patients and their associations with neuropsychological features.

Methods:

This retrospective study includes 27 NMOSD patients and 30 matched healthy controls undergoing resting state functional magnetic resonance imaging (MRI) and a neuropsychological evaluation of cognitive performance and depressive symptoms. A sliding-window correlation analysis using bilateral precuneus as seed region assessed TVC, which was quantified by the standard deviation of connectivity across windows. Mean connectivity indicated SFC.

Results:

Compared to controls, patients had reduced SFC between precuneus, temporal lobe, putamen and cerebellum, and reduced TVC between precuneus and prefronto-parietal-temporo-occipital cortices and caudate. Patients also had increased intra-precuneal TVC and increased TVC between the precuneus and the temporal cortex. More severe depressive symptoms correlated with increased TVC between the precuneus and the temporal lobe; worse cognitive performance mainly correlated with higher TVC between the precuneus and the parietal lobe.

Conclusion:

TVC rather than SFC of the precuneus correlates with NMOSD neuropsychological features; different TVC abnormalities underlie depressive symptoms and cognitive impairment.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

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Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages.

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Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease.

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Collapse of long-range connections and impaired hub integration characterize MS network reorganization.

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Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity.

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New perspectives might be the use of multimodal MRI and artificial intelligence.

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties

Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.

Structural and Functional Characterization of Gray Matter Alterations in Female Patients With Neuropsychiatric Systemic Lupus

Objective

To investigate morphological and functional alterations within gray matter (GM) in female patients with neuropsychiatric systemic lupus (NPSLE) and to explore their clinical significance.

Methods

54 female patients with SLE (30 NPSLE and 24 non-NPSLE) and 32 matched healthy controls were recruited. All subjects received a quantitative MRI scan (FLAIR, 3DT1, resting-state functional MRI). GM volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree of centrality (DC) were obtained. Between-group comparison, clinical correlation, and discrimination of NPSLE from non-NPSLE were achieved by voxel-based analysis, cerebellar seed-based functional connectivity analysis, regression analysis, and support vector machine (SVM), respectively.

Results

Patients with NPSLE showed overt subcortical GM atrophy without significantly abnormal brain functions in the same region compared with controls. The dysfunction within the left superior temporal gyri (L-STG) was found precede the GM volumetric loss. The function of the nodes in default mode network (DMN) and salience network (SN) were weakened in NPSLE patients compared to controls. The function of the cerebellar posterior lobes was significantly activated in non-NPSLE patients but attenuated along with GM atrophy and presented higher connectivity with L-STG and DMN in NPSLE patients, while the variation of the functional activities in the sensorimotor network (SMN) was the opposite. These structural and functional alterations were mainly correlated with disease burden and anti-phospholipid antibodies (aPLs) (r ranges from -1.53 to 1.29). The ReHos in the bilateral cerebellar posterior lobes showed high discriminative power in identifying patients with NPSLE with accuracy of 87%.

Conclusion

Patients with NPSLE exhibit both structural and functional alterations in the GM of the brain, which especially involved the deep GM, the cognitive, and sensorimotor regions, reflecting a reorganization to compensate for the disease damage to the brain which was attenuated along with pathologic burden and cerebral vascular risk factors. The GM within the left temporal lobe may be one of the direct targets of lupus-related inflammatory attack. The function of the cerebellar posterior lobes might play an essential role in compensating for cortical functional disturbances and may contribute to identifying patients with suspected NPSLE in clinical practice.

A more unstable resting-state functional network in cognitively declining multiple sclerosis

Cognitive impairment is common in people with multiple sclerosis and strongly affects their daily functioning. Reports have linked disturbed cognitive functioning in multiple sclerosis to changes in the organization of the functional network. In a healthy brain, communication between brain regions and which network a region belongs to is continuously and dynamically adapted to enable adequate cognitive function. However, this dynamic network adaptation has not been investigated in multiple sclerosis, and longitudinal network data remain particularly rare. Therefore, the aim of this study was to longitudinally identify patterns of dynamic network reconfigurations that are related to the worsening of cognitive decline in multiple sclerosis. Resting-state functional MRI and cognitive scores (expanded Brief Repeatable Battery of Neuropsychological tests) were acquired in 230 patients with multiple sclerosis and 59 matched healthy controls, at baseline (mean disease duration: 15 years) and at 5-year follow-up. A sliding-window approach was used for functional MRI analyses, where brain regions were dynamically assigned to one of seven literature-based subnetworks. Dynamic reconfigurations of subnetworks were characterized using measures of promiscuity (number of subnetworks switched to), flexibility (number of switches), cohesion (mutual switches) and disjointedness (independent switches). Cross-sectional differences between cognitive groups and longitudinal changes were assessed, as well as relations with structural damage and performance on specific cognitive domains. At baseline, 23% of patients were cognitively impaired (≥2/7 domains Z < −2) and 18% were mildly impaired (≥2/7 domains Z < −1.5). Longitudinally, 28% of patients declined over time (0.25 yearly change on ≥2/7 domains based on reliable change index). Cognitively impaired patients displayed more dynamic network reconfigurations across the whole brain compared with cognitively preserved patients and controls, i.e. showing higher promiscuity (P = 0.047), flexibility (P = 0.008) and cohesion (P = 0.008). Over time, cognitively declining patients showed a further increase in cohesion (P = 0.004), which was not seen in stable patients (P = 0.544). More cohesion was related to more severe structural damage (average r = 0.166, P = 0.015) and worse verbal memory (r = −0.156, P = 0.022), information processing speed (r = −0.202, P = 0.003) and working memory (r = −0.163, P = 0.017). Cognitively impaired multiple sclerosis patients exhibited a more unstable network reconfiguration compared to preserved patients, i.e. brain regions switched between subnetworks more often, which was related to structural damage. This shift to more unstable network reconfigurations was also demonstrated longitudinally in patients that showed cognitive decline only. These results indicate the potential relevance of a progressive destabilization of network topology for understanding cognitive decline in multiple sclerosis.

Divergent time-varying connectivity of thalamic sub-regions characterizes clinical phenotypes and cognitive status in multiple sclerosis

We aimed to investigate abnormal time-varying functional connectivity (FC) for thalamic sub-regions in multiple sclerosis (MS) and their clinical, cognitive and MRI correlates. Eighty-nine MS patients (49 relapsing-remitting [RR] MS; 40 progressive [P] MS) and 53 matched healthy controls underwent neurological, neuropsychological and resting state fMRI assessment. Time-varying connectivity (TVC) was quantified using sliding-window seed-voxel correlation analysis. Standard deviation of FC across windows was taken as measure of TVC, while mean connectivity across windows expressed static FC. MS patients showed reduced TVC vs controls between most of thalamic sub-regions and fronto-temporo-occipital regions. At the same time, they showed increased static FC between all thalamic sub-regions and structurally connected cortico-subcortical regions. TVC reduction was mainly driven by RRMS; while PMS exhibited a variable pattern of TVC abnormalities, characterized by reduced TVC between frontal/motor thalamic seeds and default-mode network areas and increased TVC vs controls/RRMS between posterior thalamic sub-regions and occipito-temporo-insular cortices, associated with severity of clinical disability. Compared with controls, both cognitively preserved and impaired patients showed reduced TVC between anterior thalamic sub-regions and frontal cortex. Cognitively impaired patients also showed increased TVC of the right postcentral thalamic sub-region with the cingulate cortex and postcentral gyrus vs both controls and cognitively preserved patients. Divergent patterns of TVC thalamic abnormalities were found between RRMS and PMS patients. TVC reduction in RRMS may represent the attempt of thalamic network to keep with stable connections. Conversely, increased TVC of posterior thalamic sub-regions characterized PMS and cognitively impaired MS, possibly reflecting maladaptive mechanisms.

Resting state network functional connectivity abnormalities in systemic lupus erythematosus: correlations with neuropsychiatric impairment

Neuropsychiatric manifestations are highly prevalent in systemic lupus erythematosus (SLE)-patients. We aimed to unravel the substrates of these manifestations by investigating abnormalities of resting state (RS) functional connectivity (FC) and their correlations with neuropsychiatric variables in SLE-patients. Thirty-two SLE-patients and 32 age- and sex-matched healthy controls (HC) underwent brain 3T RS fMRI. Neuropsychological assessment was performed for all SLE-patients. The main large-scale cognitive and psychiatric functional networks were derived and between-group comparisons and correlations with neuropsychological measures were performed. Compared to HC, SLE-patients exhibited increased RS FC in the right middle cingulate cortex and decreased RS FC in the left precuneus within default-mode network (DMN). They also showed increased RS FC in the left cerebellar crus I and left posterior cingulate cortex, and decreased RS FC in the left angular gyrus within working-memory networks (WMN). Compared to HC, SLE-patients exhibited increased RS FC in the left insular cortex and decreased RS FC in the right anterior cingulate cortex within salience network (SN), as well as decreased RS FC in the right middle frontal gyrus within executive-control network (ECN). Correlation analysis indicated a maladaptive role for left angular gyrus and cerebellar RS FC abnormalities in WMN, affecting memory and executive functions; and for precuneus and insular abnormalities in DMN and SN for psychiatric symptoms. Cingulate cortex modifications within DMN and SN correlated with better memory and global cognitive performance. Significant RS FC alterations in relevant cognitive and psychiatric networks occur in SLE-patients and participate in the pathophysiology of neuropsychiatric symptoms.

Dynamic Functional Connectivity in the Main Clinical Phenotypes of Multiple Sclerosis

Introduction: Dynamic functional connectivity (dFC) allows capturing recurring patterns (states) of interaction among functional networks. In this study, we investigated resting state (RS) dFC abnormalities across the different clinical phenotypes of multiple sclerosis (MS) and assessed their correlation with motor and cognitive performances. Methods: RS functional magnetic resonance imaging (fMRI) and 3D T1-weighted MRI data were acquired from 128 MS patients (69 relapsing-remitting [RR] MS, 34 secondary progressive [SP] MS, and 25 primary progressive [PP] MS) and 40 healthy controls (HC). RS fMRI data underwent independent component analysis and sliding-window correlations, to identify recurring dFC states and between-group dFC differences in the main networks. Results: dFC identified three recurring connectivity states: State 1 (frequency of appearance during fMRI acquisition = 57%, low dFC strength), State 2 (frequency = 19%, middle-high dFC strength), and State 3 (frequency = 24%, high sensorimotor and visual dFC strength). Compared to HC, MS (as a whole), RRMS, and PPMS patients exhibited lower State1/State 3 dFC (p = 0.0001, corrected) between sensorimotor, cerebellar, and cognitive networks, and some dFC increments (p = 0.001-0.05, uncorrected) in sensorimotor, visual, default-mode, and frontal/attention networks in States 2 and 3. Similar results were observed in SPMS versus RRMS patients. In MS, dFC decrease in sensorimotor, default-mode, and frontal/attention networks was correlated with worse motor and cognitive performances. Conclusions: MS patients exhibited overall lower dFC, and marginally higher dFC in sensorimotor/cognitive networks in the less-frequent middle/high-connected States. dFC abnormalities became more severe in progressive MS and correlated with motor and cognitive impairment, suggesting the presence of maladaptive mechanisms concomitant with accumulation of damage.

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

BACKGROUND

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely applied to explore abnormal functional connectivity (FC) in patients with post-facial paralysis synkinesis (PFPS). However, most studies considered steady spatial-temporal signal interactions between distinct brain regions during the period of scanning.

OBJECTIVE

In this study, we aim to investigate abnormal dynamic functional connectivity (dFC) in PFPS patients.

METHODS

We enrolled 31 PFPS patients and 19 healthy controls. All participants underwent rs-fMRI. Sliding windows approach was applied to construct dFC matrices. Next, these matrices were clustered into distinct states using the k-means clustering algorithm.

RESULTS

We found that it was not the dFC patterns, but rather the temporal properties including the mean dwell time (MDT) and occurrence frequencies, that showed a significant difference between PFPS patients and healthy controls. Two randomly clustered dFC states were recognized for both groups. Among them, State 1 showed significantly lower connectivity compared to State 2 in patients group. Compared to healthy controls, the duration spent by the PFPS patients in the state with lower connectivity significantly increased and is positively correlated with the better facial function.

CONCLUSIONS

In conclusion, aberrant dFC is a fundamental feature of brain dysfunction in PFPS patients, which is associated with the facial nerve function. These findings may contribute to a better understanding of the abnormal brain reorganization mechanisms in PFPS patients.

Thalamocortical Connectivity in Experimentally-Induced Migraine Attacks: A Pilot Study

In this study we used nitroglycerin (NTG)-induced migraine attacks as a translational human disease model. Static and dynamic functional connectivity (FC) analyses were applied to study the associated functional brain changes. A spontaneous migraine-like attack was induced in five episodic migraine (EM) patients using a NTG challenge. Four task-free functional magnetic resonance imaging (fMRI) scans were acquired over the study: baseline, prodromal, full-blown, and recovery. Seed-based correlation analysis (SCA) was applied to fMRI data to assess static FC changes between the thalamus and the rest of the brain. Wavelet coherence analysis (WCA) was applied to test time-varying phase-coherence changes between the thalamus and salience networks (SNs). SCA results showed significantly FC changes between the right thalamus and areas involved in the pain circuits (insula, pons, cerebellum) during the prodromal phase, reaching its maximal alteration during the full-blown phase. WCA showed instead a loss of synchronisation between thalami and SN, mainly occurring during the prodrome and full-blown phases. These findings further support the idea that a temporal change in thalamic function occurs over the experimentally induced phases of NTG-induced headache in migraine patients. Correlation of FC changes with true clinical phases in spontaneous migraine would validate the utility of this model.

Introduction to Functional MR Imaging

During the past decade, functional MR imaging has rapidly moved from the research environment into clinical practice. Preoperative functional MR imaging is now standard clinical practice not only in major academic institutions, but also in community neurosurgical and neuroradiologic practices. The clinical use of functional MR imaging will only increase in the years to come. Application of functional MR imaging (including resting-state functional MR imaging) to the context of neuropsychiatric diseases is likely to continue to advance.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Pain- and Fatigue-Related Functional and Structural Changes in Ankylosing Spondylitis: An fRMI Study

Background: Chronic pain and fatigue are two cardinal features of ankylosing spondylitis (AS) and how to effectively treat these conditions continues to be a challenge. The underlying mechanisms and the relationship between AS-related pain and fatigue remain poorly understood. The present study was conducted, therefore, to explore the brain functional and structural changes associated with pain and fatigue in AS.

Methods: A total of 65 AS patients (48 men and 17 women; 32.33 ± 8.6 years) and 53 age- and sex-matched controls were enrolled in the study. The patients underwent clinical assessment based on Total Back Pain scores, Fatigue Severity Scale, Bath Ankylosing Spondylitis Disease Activity Index, (BASDAI), high-sensitivity C-reactive Protein (hsCRP), erythrocyte sedimentation rate (ESR), and Beck Depression Inventory (BDI). Using 3T magnetic resonance imaging (3T-MRI), we analyzed the brain functional (connectivity and nodal properties) and structural (covariance and gray matter volumes) differences between AS patients and controls. Furthermore, we extracted the values of the significantly changed regions in the AS cohort and explored their association with pain and fatigue.

Results: In AS patients, there were functional and structural abnormalities distributed in the default mode network (DMN), salience network (SN), sensory/somatomotor network (SMN), dorsal attention network (DAN), task control network (TCN), and visual network, and some regions showed both types of changes. Among these, the functional connectivity (FC) between the left insula and medial prefrontal cortex, the betweenness centrality of the left medial prefrontal cortex and the gray matter volume of the right putamen tracked both pain and fatigue. In addition, pain was related to within-DMN FC disruption and nodal function / gray matter volumes changes in DMN, SN, and the visual network, while fatigue mainly involved the SMN, DAN, and TCN. Moreover, certain changes were also related to BASDAI and inflammation level.

Conclusion: This study offers new insights into understanding the neural mechanism of AS-related pain and fatigue, and could help to stratify patients based on the correlation features and ultimately move towards a personalized therapy.

Current methods and new directions in resting state fMRI

Resting state functional connectivity magnetic resonance imaging (rsfcMRI) has become a key component of investigations of neurocognitive and psychiatric behaviors. Over the past two decades, several methods and paradigms have been adopted to utilize and interpret data from resting-state fluctuations in the brain. These findings have increased our understanding of changes in many disease states. As the amount of resting state data available for research increases with big datasets and data-sharing projects, it is important to review the established traditional analysis methods and recognize areas where research methodology can be adapted to better accommodate the scale and complexity of rsfcMRI analysis. In this paper, we review established methods of analysis as well as areas that have been receiving increasing attention such as dynamic rsfcMRI, independent vector analysis, multiband rsfcMRI and network of networks.

Action observation training promotes motor improvement and modulates functional network dynamic connectivity in multiple sclerosis

We investigated modifications of resting state dynamic functional network connectivity (dFNC) following a 2-week action observation training (AOT) in 46 right-handed healthy controls (HC) and 41 patients with multiple sclerosis (pwMS) and dominant-hand motor impairment, who were randomized to AOT or control (C) training. PwMS had decreased baseline dFNC versus HC. After training, MS groups improved in right upper limb functions, mainly in AOT, and showed dFNC increase (larger in MS-AOT vs MS-C) in sensorimotor and cognitive networks (p range, ⩽0.001-0.01). Both HC groups showed decreased dFNC over time (p range, ⩽0.001-0.01). Clinical improvements following interventions correlated with sensorimotor and cognitive dFNC changes, suggesting their possible role in motor recovery.