Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia

Shared and specific dynamics of brain segregation and integration in bipolar disorder and major depressive disorder: A resting-state functional magnetic resonance imaging study

BACKGROUND

When bipolar disorder (BD) presents as the depressive state, it is often misdiagnosed as major depressive disorder (MDD). However, few studies have focused on dynamic differences in local brain activity and connectivity between BD and MDD. Therefore, the present study explored shared and specific patterns of abnormal dynamic brain segregation and integration in BD and MDD patients.

METHODS

BD Patients (n = 106), MDD patients (n = 114), and 130 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (fMRI). We first used a sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations (dALFF) and, based on the altered dALFF, further analyzed the dynamic functional connectivity (dFC) using a seed-based approach.

RESULTS

Both the BD and MDD groups showed decreased temporal variability of the dALFF (less dynamic segregation) in the bilateral posterior cingulate cortex (PCC)/precuneus compared with the HCs. The MDD group showed increased temporal variability of the dALFF (more dynamic segregation) in the left putamen compared with the controls, but there was no significant difference between the BD and HCs. The dFC analysis also showed that both the BD and MDD groups had reduced dFC (less dynamic integration) between the bilateral PCC/ precuneus and the left inferior parietal lobule compared with the HCs.

LIMITATIONS

This study was cross-sectional and did not examine data from remitted BD and MDD patients.

CONCLUSION

Our findings indicated disrupted dynamic balance between segregation and integration within the default mode network in both BD and MDD. Moreover, we found MDD-specific abnormal brain dynamics in the putamen.

Atypical Resting-State Functional Connectivity Dynamics Correlate With Early Cognitive Dysfunction in HIV Infection

Purpose: Previous studies have shown that HIV affects striato-cortical regions, leading to persisting cognitive impairment in 30-70% of the infected individuals despite combination antiretroviral therapy. This study aimed to investigate brain functional dynamics whose deficits might link to early cognitive decline or immunologic deterioration. Methods: We applied sliding windows and K-means clustering to fMRI data (HIV patients with asymptomatic neurocognitive impairment and controls) to construct dynamic resting-state functional connectivity (RSFC) maps and identify states of their reoccurrences. The average and variability of dynamic RSFC, and the dwelling time and state transitioning of each state were evaluated. Results: HIV patients demonstrated greater variability in RSFC between the left pallidum and regions of right pre-central and post-central gyri, and between the right supramarginal gyrus and regions of the right putamen and left pallidum. Greater variability was also found in the frontal RSFC of pars orbitalis of the left inferior frontal gyrus and right superior frontal gyrus (medial). While deficits in learning and memory recall of HIV patients related to greater striato-sensorimotor variability, deficits in attention and working memory were associated with greater frontal variability. Greater striato-parietal variability presented a strong link with immunologic function (CD4+/CD8+ ratio). Furthermore, HIV-infected patients exhibited longer time and reduced transitioning in states typified by weaker connectivity in specific networks. CD4+T-cell counts of the HIV-patients were related to reduced state transitioning. Conclusion: Our findings suggest that HIV alters brain functional connectivity dynamics, which may underlie early cognitive impairment. These findings provide novel insights into our understanding of HIV pathology, complementing the existing knowledge.

Aberrant state-related dynamic amplitude of low-frequency fluctuations of the emotion network in major depressive disorder

Major depressive disorder (MDD) is a highly prevalent mental disorder that is typically characterized by pervasive and persistent low mood. This durable emotional disturbance may represent a key aspect of the neuropathology of MDD, typified by the wide-ranging distribution of brain alterations involved in emotion processing. However, little is known about whether these alterations are represented as the state properties of dynamic amplitude of low-frequency fluctuation (dALFF) variability in the emotion network. To address this question, we investigated the time-varying intrinsic brain activity derived from resting-state functional magnetic resonance imaging (R-fMRI). Data were obtained from 50 MDD patients and 37 sex- and age-matched healthy controls; a sliding-window method was used to assess dALFF in the emotion network, and two reoccurring dALFF states throughout the entire R-fMRI scan were then identified using a k-means clustering method. The results showed that MDD patients had a significant decrease in dALFF variability in the emotion network and its three modules located in the lateral paralimbic, media posterior, and visual association regions. Altered state-wise dALFF was also observed in MDD patients. Specifically, we found that these altered dALFF measurements in the emotion network were related to scores on the Hamilton Rating Scale for Depression (HAMD) among patients with MDD. The detection and estimation of these temporal dynamic alterations could advance our knowledge about the brain mechanisms underlying emotional dysfunction in MDD.

Dynamic Altered Amplitude of Low-Frequency Fluctuations in Patients With Major Depressive Disorder

Background: Major depressive disorder (MDD) has demonstrated abnormalities of static intrinsic brain activity measured by amplitude of low-frequency fluctuation (ALFF). Recent studies regarding the resting-state functional magnetic resonance imaging (rs-fMRI) have found the brain activity is inherently dynamic over time. Little is known, however, regarding the temporal dynamics of local neural activity in MDD. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by MDD. Methods: We recruited 81 first-episode, drug-naive MDD patients and 64 age-, gender-, and education-matched healthy controls who underwent rs-fMRI. A sliding-window approach was then adopted for the estimation of dynamic ALFF (dALFF), which was used to measure time-varying brain activity and then compared between the two groups. The relationship between altered dALFF variability and clinical variables in MDD patients was also analyzed. Results: MDD patients showed increased temporal variability (dALFF) mainly focused on the bilateral thalamus, the bilateral superior frontal gyrus, the right middle frontal gyrus, the bilateral cerebellum posterior lobe, and the vermis. Furthermore, increased dALFF variability values in the right thalamus and right cerebellum posterior lobe were positively correlated with MDD symptom severity. Conclusions: The overall results suggest that altered temporal variability in corticocerebellar-thalamic-cortical circuit (CCTCC), involved in emotional, executive, and cognitive, is associated with drug-naive, first-episode MDD patients. Moreover, our study highlights the vital role of abnormal dynamic brain activity in the cerebellar hemisphere associated with CCTCC in MDD patients. These findings may provide novel insights into the pathophysiological mechanisms of MDD.

Temporal dynamic changes of intrinsic brain activity in Alzheimer's disease and mild cognitive impairment patients: a resting-state functional magnetic resonance imaging study

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory impairment. Previous studies have largely focused on alterations of static brain activity occurring in patients with AD. Few studies to date have explored the characteristics of dynamic brain activity in cognitive impairment, and their predictive ability in AD patients.

METHODS

One hundred and eleven AD patients, 29 MCI patients, and 73 healthy controls (HC) were recruited. The dynamic amplitude of low-frequency fluctuation (dALFF) and the dynamic fraction amplitude of low-frequency fluctuation (dfALFF) were used to assess the temporal variability of local brain activity in patients with AD or mild cognitive impairment (MCI). Pearson's correlation coefficients were calculated between the metrics and subjects' behavioral scores.

RESULTS

The results of analysis of variance indicated that the AD, MCI, and HC groups showed significant variability of dALFF in the cerebellar posterior and middle temporal lobes. In AD patients, these brain regions had high dALFF variability. Significant dfALFF variability was found between the three groups in the left calcarine cortex and white matter. The AD group showed lower dfALFF than the MCI group in the left calcarine cortex.

CONCLUSIONS

Compared to HC, AD patients were found to have increased dALFF variability in the cerebellar posterior and temporal lobes. This abnormal pattern may diminish the capacity of the cerebellum and temporal lobes to participate in the cerebrocerebellar circuits and default mode network (DMN), which regulate cognition and emotion in AD. The findings above indicate that the analysis of dALFF and dfALFF based on functional magnetic resonance imaging data may give a new insight into the neurophysiological mechanisms of AD.

Altered Spatial Organization of Dynamic Functional Network Associates With Deficient Sensory and Perceptual Network in Schizophrenia

Schizophrenia is currently thought as a disorder with dysfunctional communication within and between sensory and cognitive processes. It has been hypothesized that these deficits mediate heterogeneous and comprehensive schizophrenia symptomatology. In this study, we investigated as to how the abnormal dynamic functional architecture of sensory and cognitive networks may contribute to these symptoms in schizophrenia. We calculated a sliding-window-based dynamic functional connectivity strength (FCS) and amplitude of low-frequency fluctuation (ALFF) maps. Then, using group-independent component analysis, we characterized spatial organization of dynamic functional network (sDFN) across various time windows. The spatial architectures of FCS/ALFF-sDFN were similar with traditional resting-state functional networks and cannot be accounted by length of the sliding window. Moreover, schizophrenic subjects demonstrated reduced dynamic functional connectivity (dFC) within sensory and perceptual sDFNs, as well as decreased connectivity between these sDFNs and high-order frontal sDFNs. The severity of patients' positive and total symptoms was related to these abnormal dFCs. Our findings revealed that the sDFN during rest might form the intrinsic functional architecture and functional changes associated with psychotic symptom deficit. Our results support the hypothesis that the dynamic functional network may influence the aberrant sensory and cognitive function in schizophrenia, further highlighting that targeting perceptual deficits could extend our understanding of the pathophysiology of schizophrenia.

Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as "brain states", have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the "strong" states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.

Dynamic functional network reconfiguration underlying the pathophysiology of schizophrenia and autism spectrum disorder

The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step-wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole-brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ-ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning-problem-solving performance in SZ (r = -.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders.

Temporal Dynamics in Degree Centrality of Brain Functional Connectome in First-Episode Schizophrenia with Different Short-Term Treatment Responses: A Longitudinal Study

PURPOSE

This study investigated temporal dynamics in degree centrality (DC) of the brain functional connectome in first-episode schizophrenia with different short-term treatment responses.

METHODS

A total of 127 first-episode patients (FEPs) with schizophrenia and 133 healthy controls (HCs) were recruited in this study. All subjects underwent resting-state functional magnetic resonance imaging. FEPs were scanned at baseline (pretreatment) and at follow-up (posttreatment), while HCs were scanned only at baseline. The patients were exposed to naturalistic antipsychotic treatment for 12 weeks, and classified as schizophrenia responders (SRs) or nonresponders (NRs). Voxel-wise dynamic DC analyses were conducted among the SRs (n=75), NRs (n=52), and HCs (n=133) to assess temporal variability in functional connectivity across the entire neuronal network.

RESULTS

The SRs and NRs showed dissimilar dynamic DC at baseline, with differences mainly involving the temporal lobe. Different DC alteration was observed in the left fusiform gyrus, right fusiform gyrus, left middle cingulate cortex, and left superior parietal gyrus in the SRs and NRs pre- and posttreatment. SRs group and NRs presented opposite changing patterns of dynamic DC in particular regions of the brain.

CONCLUSION

These findings indicate that dynamic DC abnormalities exist in unmedicated patients with schizophrenia. The NRs differed from the SRs in dynamic DC not only at baseline but in the characteristics of changes before and after treatment as well. Our study may contribute to understanding pathophysiology in schizophrenia with different treatment responses.

Differential Dynamical Pattern of Regional Homogeneity in Bipolar and Unipolar Depression: A Preliminary Resting-State fMRI Study

Background: Bipolar depression (BD) and unipolar depression (UD) are both characterized by depressive moods, which are difficult to distinguish in clinical practice. Human brain activity is time-varying and dynamic. Investigating dynamical pattern alterations of depressed brains can provide deep insights into the pathophysiological features of depression. This study aimed to explore similar and different abnormal dynamic patterns between BD and UD. Methods: Brain resting-state functional magnetic resonance imaging data were acquired from 36 patients with BD type I (BD-I), 38 patients with UD, and 42 healthy controls (HCs). Analysis of covariance was adopted to examine the differential pattern of the dynamical regional homogeneity (dReHo) temporal variability across 3 groups, with gender, age, and education level as covariates. Post-hoc analyses were employed to obtain the different dynamic characteristics between any 2 groups. We further applied the machine-learning methods to classify BD-I from UD by using the detected distinct dReHo pattern. Results: Compared with patients with UD, patients with BD-I demonstrated decreased dReHo variability in the right postcentral gyrus and right parahippocampal gyrus. By using the dReHo variability pattern of these two regions as features, we achieved the 91.89% accuracy and 0.92 area under curve in classifying BD-I from UD. Relative to HCs, patients with UD showed increased dReHo variability in the right postcentral gyrus, while there were no dReHo variability differences in patients with BD-I. Conclusions: The results of this study mainly report the differential dynamic pattern of the regional activity between BD-I and UD, particular in the mesolimbic system, and show its promising potential in assisting the diagnosis of these two depression groups.

More than just statics: Abnormal dynamic amplitude of low-frequency fluctuation in adolescent patients with pure conduct disorder

BACKGROUND

The human brain activity is inherently dynamic over time. Conventional neuroimaging studies have reported abnormalities of static intrinsic brain activity or connectivity in adolescent patients with conduct disorder (CD). Little is known, however, regarding the temporal dynamics alterations of brain activity in CD.

METHODS

In this study, resting-state functional magnetic resonance imaging examinations were performed on adolescent patients with pure CD and age-matched typically developing (TD) controls. The dynamic amplitude of low-frequency fluctuation (dALFF) was first measured using a sliding-window method. The temporal variability (TV) was then quantified as the variance of dALFF over time and compared between the two groups. Further, the relationships between aberrant TV of dALFF and clinical features were evaluated.

RESULTS

CD patients showed reduced brain dynamics (less temporal variability) in the default-mode network, frontal-limbic cortices, sensorimotor areas, and visual regions which are involved in cognitive, emotional and perceptional processes. Importantly, receiver operating characteristic curve analysis revealed that regions with altered TV of dALFF exhibited a better ability to distinguish CD patients than the results from static ALFF in the current data set.

CONCLUSIONS

Our findings extended previous work by providing a novel perspective on the neural mechanisms underlying adolescent patients with CD and demonstrated that the altered dynamic local brain activity may be a potential biomarker for CD diagnosis.

Distinct Brain Functional Impairment Patterns Between Suspected Non-Alzheimer Disease Pathophysiology and Alzheimer's Disease: A Study Combining Static and Dynamic Functional Magnetic Resonance Imaging

Background: Suspected non-Alzheimer disease pathophysiology (SNAP) refers to the subjects who feature negative β-amyloid (Aβ) but positive tau or neurodegeneration biomarkers. It accounts for a quarter of the elderly population and is associated with cognitive decline. However, the underlying pathophysiology is still unclear. Methods: We included 111 non-demented subjects, then classified them into three groups using cerebrospinal fluid (CSF) Aβ 1-42 (A), phosphorylated tau 181 (T), and total tau (N). Specifically, we identified the normal control (NC; subjects with normal biomarkers, A-T-N-), SNAP (subjects with normal amyloid but abnormal tau, A-T+), and predementia Alzheimer's disease (AD; subjects with abnormal amyloid and tau, A+T+). Then, we used the static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance to reflect the intrinsic functional network strength and stability, respectively. Further, we performed a correlation analysis to explore the possible relationship between intrinsic brain activity changes and cognition. Results: SNAP showed decreased sALFF in left superior frontal gyrus (SFG) while increased sALFF in left insula as compared to NC. Regarding the dynamic metric, SNAP showed a similarly decreased dALFF in the left SFG and left paracentral lobule as compared to NC. By contrast, when compared to NC, predementia AD showed decreased sALFF in left inferior parietal gyrus (IPG) and right precuneus, while increased sALFF in the left insula, with more widely distributed decreased dALFF variance across the frontal, parietal and occipital lobe. When directly compared to SNAP, predementia AD showed decreased sALFF in left middle occipital gyrus and IPG, while showing decreased dALFF variance in the left temporal pole. Further correlation analysis showed that increased sALFF in the insula had a negative correlation with the general cognition in the SNAP group. Besides, sALFF and dALFF variance in the right precuneus negatively correlated with attention in the predementia AD group. Conclusion: SNAP and predementia AD show distinct functional impairment patterns. Specifically, SNAP has functional impairments that are confined to the frontal region, which is usually spared in early-stage AD, while predementia AD exhibits widely distributed functional damage involving the frontal, parietal and occipital cortex.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura

The purpose of the present study was to explore whether and to what extent the neuroimaging markers could predict the relief of the symptoms of patients with migraine without aura (MWoA) following a 4-week acupuncture treatment period. In study 1, the advanced multivariate pattern analysis was applied to perform a classification analysis between 40 patients with MWoA and 40 healthy subjects (HS) based on the z-transformed amplitude of low-frequency fluctuation (zALFF) maps. In study 2, the meaningful classifying features were selected as predicting features and the support vector regression models were constructed to predict the clinical efficacy of acupuncture in reducing the frequency of migraine attacks and headache intensity in 40 patients with MWoA. In study 3, a region of interest-based comparison between the pre- and post-treatment zALFF maps was conducted in 33 patients with MwoA to assess the changes in predicting features after acupuncture intervention. The zALFF value of the foci in the bilateral middle occipital gyrus, right fusiform gyrus, left insula, and left superior cerebellum could discriminate patients with MWoA from HS with higher than 70% accuracy. The zALFF value of the clusters in the right and left middle occipital gyrus could effectively predict the relief of headache intensity (R ² = 0.38 ± 0.059, mean squared error = 2.626 ± 0.325) and frequency of migraine attacks (R ² = 0.284 ± 0.072, mean squared error = 20.535 ± 2.701) after the 4-week acupuncture treatment period. Moreover, the zALFF values of these two clusters were both significantly reduced after treatment. The present study demonstrated the feasibility and validity of applying machine learning technologies and individual cerebral spontaneous activity patterns to predict acupuncture treatment outcomes in patients with MWoA. The data provided a quantitative benchmark for selecting acupuncture for MWoA.

Characteristics of temporal dynamics of intrinsic brain activity in unmedicated bipolar disorder with suicidality

BACKGROUND

Bipolar disorder is associated with a high risk of suicide. Routine neuroimaging examination exhibited that bipolar disorder with suicidality was associated with brain structural and functional changes. However, the alterations of brain dynamics have still remained elusive.

PURPOSE

To investigate the alterations of brain dynamics in unmedicated bipolar disorder II depression with suicidality and predict the severity of suicidality.

MATERIALS AND METHODS

This prospective study included 106 bipolar disorder II participants (20 with suicidal attempt, 35 with suicidal ideation, 51 without suicidal ideation) and 50 healthy controls who underwent resting-state functional magnetic resonance imaging between February 2016 and December 2017. We first used sliding window analysis to evaluate the dynamic amplitude of low-frequency fluctuations. Then, we predicted the severity of suicidality using a multivariate regression model.

RESULTS

One-way analysis of covariance revealed that the dynamic amplitude of low-frequency fluctuations in the right temporal pole, inferior temporal gyrus, superior temporal gyrus and the bilateral precuneus/posterior cingulate cortex was significantly different among the four groups. Post hoc pairwise comparisons revealed that dynamic amplitude of low-frequency fluctuations was remarkably decreased in the bilateral precuneus/posterior cingulate cortex in the three bipolar disorder II groups compared with that in healthy controls group. Increased dynamic amplitude of low-frequency fluctuations was found in the right superior temporal gyrus and inferior temporal gyrus in the suicidal attempt group compared with that in the other groups, and in the right temporal pole in the suicidal attempt group compared with that in the suicidal ideation and healthy controls groups. Importantly, these temporal variabilities could be used to predict the severity of suicidality (r = 0.330, p = 0.036), whereas static amplitude of low-frequency fluctuations couldn't (r = -0.050, p = 0.532).

CONCLUSION

Our findings indicated that alterations of temporal variability in the precuneus/posterior cingulate cortex are such a common feature of bipolar disorder patients. Besides, the severity of suicidality could be predicted by the dynamic amplitude of low-frequency fluctuations abnormalities rather than static amplitude of low-frequency fluctuations abnormalities, which is the first evidence of dynamic brain alterations in bipolar disorder patients with suicidality. The proposed predictive model may be advantageous for clinical applications.

Disturbances of Dynamic Function in Patients With Bipolar Disorder I and Its Relationship With Executive-Function Deficit

Abnormity in brain regional function and inter-regional cooperation have been linked with the dysfunction during cognitive and emotional processing in bipolar disorder (BD) patients. Recent evidences have suggested that brain function is not static but temporal dynamic. In present study, we aimed to characterize the temporal dynamics of regional function and inter-regional cooperation in BD and its relationship with executive dysfunction, an important deficit in BD. Resting-state functional MRI was performed in patients with bipolar I disorder (BDI) (n = 18) and healthy controls (HCs, n = 19). We first assessed local-function temporal variety with dynamic amplitude of low-frequency fluctuation (dALFF). Region with significant inter-groups difference in dALFF was chosen as a seed to calculate inter-regions connective temporal variety with dynamic functional connectivity (dFC). The executive function was measured by Verbal Fluency Test (VFT). The relationship between executive function and brain dynamics were examined. Compared with HC, the BDI group showed decreased dALFF (less temporal variability) in the posterior cingulate cortex (PCC) and decreased dFC between PCC and medial prefrontal cortex (mPFC). The PCC-mPFC dFC was positively associated with VFT in BDI patients, but not in HC. These findings implicated the reduced temporal variability in local region and inter-regions cooperation in BDI, which may be a neural substrate of executive-function deficit in BDI.

Altered Static and Dynamic Spontaneous Neural Activity in Drug-Naïve and Drug-Receiving Benign Childhood Epilepsy With Centrotemporal Spikes

The present study aims to investigate intrinsic abnormalities of brain and the effect of antiepileptic treatment on brain activity in Benign childhood epilepsy with centrotemporal spikes (BECTS). Twenty-six drug-naïve patients (DNP) and 22 drug-receiving patients (DRP) with BECTS were collected in this study. Static amplitude of low frequency fluctuation (sALFF) and dynamic ALFF (dALFF) were applied to resting-state fMRI data. Functional connectivity (FC) analysis was further performed for affected regions identified by static and dynamic analysis. One-way analysis of variance and post hoc statistical analyses were performed for between-group differences. Abnormal sALFF and dALFF values were correlated with clinical features of patients. Compared with healthy controls (HC), DNP group demonstrated alterations of sALFF and/or dALFF in medial prefrontal cortex (MPFC), supplementary motor areas (SMA), cerebellum, hippocampus, pallidum and cingulate cortex, in which the values were close to normal in DRP. Notably, sALFF and dALFF showed specific sensitivity in detecting abnormalities in basal ganglia and cerebellum. Additionally, DRP showed additional changes in precuneus, inferior temporal gyrus, superior frontal gyrus and occipital visual cortex. Compared with HC, the DNP showed increased FC in default network and motion-related networks, and the DRP showed decreased FC in default network. The MPFC, hippocampus, SMA, basal ganglia and cerebellum are indicated to be intrinsically affected regions and effective therapeutic targets. And the FC profiles of default and motion-related networks might be potential core indicators for clinical treatment. This study revealed potential neuromodulatory targets and helped understand pathomechanism of BECTS. Static and dynamic analyses should be combined to investigate neuropsychiatric disorders.

Tools of the trade: estimating time-varying connectivity patterns from fMRI data

Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders

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Propose a new pipeline to link brain changes among different datasets, studies, and disorders.

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Identify reproducible biomarkers in schizophrenia using independent data.

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Find both common and unique brain impairments in schizophrenia and autism.

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Reveal gradual changes from healthy controls to mild cognitive impairment to Alzheimer’s disease.

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Obtain high classification accuracy (~90%) between bipolar disorder and major depressive disorder.

Many mental illnesses share overlapping or similar clinical symptoms, confounding the diagnosis. It is important to systematically characterize the degree to which unique and similar changing patterns are reflective of brain disorders. Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. However, it is still an open question on replicating and translating findings across studies. Standardized approaches for capturing reproducible and comparable imaging markers are greatly needed. Here, we propose a pipeline based on the priori-driven independent component analysis, NeuroMark, which is capable of estimating brain functional network measures from functional magnetic resonance imaging (fMRI) data that can be used to link brain network abnormalities among different datasets, studies, and disorders. NeuroMark automatically estimates features adaptable to each individual subject and comparable across datasets/studies/disorders by taking advantage of the reliable brain network templates extracted from 1828 healthy controls as guidance. Four studies including 2442 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, mild cognitive impairment, Alzheimer’s disease, bipolar disorder, and major depressive disorder) to evaluate validity of the proposed pipeline from different perspectives (replication of brain abnormalities, cross-study comparison, identification of subtle brain changes, and multi-disorder classification using identified biomarkers). Our results highlight that NeuroMark effectively identified replicated brain network abnormalities of schizophrenia across different datasets; revealed interesting neural clues on the overlap and specificity between autism and schizophrenia; demonstrated brain functional impairments present to varying degrees in mild cognitive impairments and Alzheimer's disease; and captured biomarkers that achieved good performance in classifying bipolar disorder and major depressive disorder.

Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its non-specific etiology and complexity. Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dynamics in cLBP patients and validate the findings in two independent cohorts. We first identify two reoccurring dynamic connectivity states and their associations with chronic and temporary pain. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state. These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Thalamocortical dysrhythmia is a key pathology of chronic pain. Here, the authors propose an analytical pipeline to study dynamic fMRI brain networks and demonstrate that chronic low back pain pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Intersubject consistent dynamic connectivity during natural vision revealed by functional MRI

The functional communications between brain regions are thought to be dynamic. However, it is usually difficult to elucidate whether the observed dynamic connectivity is functionally meaningful or simply due to noise during unconstrained task conditions such as resting-state. During naturalistic conditions, such as watching a movie, it has been shown that local brain activities, e.g. in the visual cortex, are consistent across subjects. Following similar logic, we propose to study intersubject correlations of the time courses of dynamic connectivity during naturalistic conditions to extract functionally meaningful dynamic connectivity patterns. We analyzed a functional MRI (fMRI) dataset when the subjects watched a short animated movie. We calculated dynamic connectivity by using sliding window technique, and quantified the intersubject correlations of the time courses of dynamic connectivity. Although the time courses of dynamic connectivity are thought to be noisier than the original signals, we found similar level of intersubject correlations of dynamic connectivity to those of regional activity. Most importantly, highly consistent dynamic connectivity could occur between regions that did not show high intersubject correlations of regional activity, and between regions with little stable functional connectivity. The analysis highlighted higher order brain regions such as the default mode network that dynamically interacted with posterior visual regions during the movie watching, which may be associated with the understanding of the movie.

Reconfiguration of Dynamic Functional Connectivity in Sensory and Perceptual System in Schizophrenia

Schizophrenia is thought as a self-disorder with dysfunctional brain connectivity. This self-disorder is often attributed to high-order cognitive impairment. Yet due to the frequent report of sensorial and perceptual deficits, it has been hypothesized that self-disorder in schizophrenia is dysfunctional communication between sensory and cognitive processes. To further verify this assumption, the present study comprehensively examined dynamic reconfigurations of resting-state functional connectivity (rsFC) in schizophrenia at voxel level, region level, and network levels (102 patients vs. 124 controls). We found patients who show consistently increased rsFC variability in sensory and perceptual system, including visual network, sensorimotor network, attention network, and thalamus at all the three levels. However, decreased variability in high-order networks, such as default mode network and frontal-parietal network were only consistently observed at region and network levels. Taken together, these findings highlighted the rudimentary role of elevated instability of information communication in sensory and perceptual system and attenuated whole-brain integration of high-order network in schizophrenia, which provided novel neural evidence to support the hypothesis of disrupted perceptual and cognitive function in schizophrenia. The foci of effects also highlighted that targeting perceptual deficits can be regarded as the key to enhance our understanding of pathophysiology in schizophrenia and promote new treatment intervention.

Static and Dynamic Changes of Amplitude of Low-Frequency Fluctuations in Cervical Discogenic Pain

Cervical discogenic pain (CDP) is a clinically common pain syndrome caused by cervical disk degeneration. A large number of studies have reported that CDP results in brain functional impairments. However, the detailed dynamic brain functional abnormalities in CDP are still unclear. In this study, using resting-state functional magnetic resonance imaging, we explored the neural basis of CDP with 40 CDP patients and 40 age-, gender-matched healthy controls to delineate the changes of the voxel-level static and dynamic amplitude of low frequency fluctuations (ALFF). We found increased static ALFF in left insula (INS) and posterior precuneus (PCu), and decreased static ALFF in left precentral/postcentral gyrus (PreCG/PoCG), thalamus (THA), and subgenual anterior cingulate cortex in CPD patients compared to healthy controls. We also found decreased dynamic ALFF in left PreCG/PoCG, right posterior middle temporal gyrus, and bilateral THA. Moreover, we found that static ALFF in left PreCG/PoCG and dynamic ALFF in THA were significantly negatively correlated with visual analog scale and disease duration, respectively. Our findings provide the neurophysiological basis for CDP and facilitate understanding the neuropathology of CDP.

Dynamic Neuroimaging Biomarkers of Smoking in Young Smokers

OBJECTIVE

To examine potential changes in the dynamic characteristics of regional neural activity in young smokers and to detect whether the changes were associated with smoking behavior.

METHODS

The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) in 40 young smokers and 42 nonsmokers were compared. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and smoking behavior [e.g., the Fagerström Test for Nicotine dependence (FTND) scores and pack-years].

RESULTS

Significantly differences in dReHo variability were observed in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), medial frontal gyrus (MFG), insula, cuneus, postcentral gyrus, inferior semi-lunar lobule, orbitofrontal gyrus, and inferior temporal gyrus (ITG). Young smokers also showed significantly increased dALFF variability in the anterior cingulate cortex (ACC) and ITG. Furthermore, a significant positive correlation was found between dALFF variability in the ACC and the pack-years; whereas a significant negative correlation between dReHo variability in the IFG and the FTND scores was found in young smokers.

CONCLUSION

The pattern of resting state regional neural activity variability was different between young smokers and nonsmokers. Dynamic regional indexes might be a novel neuroimaging biomarker of smoking behavior in young smokers.

Aberrant static and dynamic functional connectivity of the executive control network in lung cancer patients after chemotherapy: a longitudinal fMRI study

OBJECTIVE

The purpose of the current study was to investigate chemotherapy-related variations in the intrinsic static and dynamic functional connectivity (sFC and dFC, respectively) of the executive control network (ECN) in lung cancer patients.

MATERIALS AND METHODS

In this study, we evaluated 18 lung cancer patients scanned before and after adjuvant chemotherapy treatment and compared the patients with 21 healthy controls (HCs). All subjects underwent resting-state functional MRI (rs-fMRI). We constructed the sFC and dFC of the bilateral dorsolateral prefrontal cortex (DLPFC) using a sliding-window approach, and the correlations between the changed sFC or dFC and cognitive performance were analyzed.

RESULTS

Whole-brain sFC analysis showed that the lung cancer patients showed significant FC pattern changes in the bilateral DLPFC, mainly in the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus, left superior temporal gyrus, left inferior parietal lobe and the right insula. Furthermore, after chemotherapy, the lung cancer patients showed significantly reduced dFC variability between the right DLPFC and right precuneus compared with HCs. In addition, the decreased dFC between the right DLPFC and left SFG in the lung cancer patients after chemotherapy in state 1 and between the right DLPFC and left insula in the lung cancer patients before chemotherapy in state 2 were negatively correlated with MoCA scores ((r = -0.520, p = 0.039; r = -0.548, p = 0.028, respectively).

CONCLUSIONS

Our results reveal that dynamic connectivity analysis is more effective and sensitive than methods that assume static brain states for linking brain FC patterns and chemotherapy.

Cerebral regional and network characteristics in asthma patients: a resting-state fMRI study

Asthma is a serious health problem that involves not only the respiratory system but also the central nervous system. Previous studies identified either regional or network alterations in patients with asthma, but inconsistent results were obtained. A key question remains unclear: are the regional and neural network deficits related or are they two independent characteristics in asthma? Answering this question is the aim of this study. By collecting resting-state functional magnetic resonance imaging from 39 patients with asthma and 40 matched health controls, brain functional measures including regional activity (amplitude of low-frequency fluctuations) and neural network function (degree centrality (DC) and functional connectivity) were calculated to systematically characterize the functional alterations. Patients exhibited regional abnormities in the left angular gyrus, right precuneus, and inferior temporal gyrus within the default mode network. Network abnormalities involved both the sensorimotor network and visual network with key regions including the superior frontal gyrus and occipital lobes. Altered DC in the lingual gyrus was correlated with the degree of airway obstruction. This study elucidated different patterns of regional and network changes, thereby suggesting that the two parameters reflect different brain characteristics of asthma. These findings provide evidence for further understanding the potential cerebral alterations in the pathophysiology of asthma.

Altered Dynamic Neural Activity in the Default Mode Network in Lung Cancer Patients After Chemotherapy

Background

Few studies have examined functional brain changes specifically associated with chemotherapy (CTx) in patients with lung cancer. This prospective longitudinal research aimed to explore the change in intrinsic brain activity by investigating patients with lung cancer after CTx.

Material/Methods

Sixteen patients and 20 healthy individuals were enrolled in this study. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dynamic amplitude of low-frequency fluctuation (dALFF), and dynamic regional homogeneity (dReHo) were computed. The group differences in resting state functional magnetic resonance imaging (rs-fMRI) parameters were compared. Alterations in the rs-fMRI parameters from before CTx to after CTx were assessed using the paired t-test. We performed correlation analyses between rs-fMRI parameters and Montreal Cognitive Assessment (MoCA) scores.

Results

We found statistically significant differences in MoCA scores before CTx and after CTx. Compared to the healthy group, rs-fMRI values decreased in the frontal regions as well as parietal regions compared to values before CTx. In addition, we found significantly decreased rs-fMRI values in the default-mode network (DMN) region of the brain before CTx compared to after CTx. We found no significant correlations between altered intrinsic activity values and MoCA scores.

Conclusions

The current study indicated that patients with lung cancer after CTx had decreased dynamic brain activity in the DMN region, and the DMN is vulnerable when patients undergoing CTx.

Abnormalities of intrinsic regional brain activity in first-episode and chronic schizophrenia: a meta-analysis of resting-state functional MRI

BACKGROUND

Resting-state functional MRI (fMRI) studies have provided much evidence for abnormal intrinsic brain activity in schizophrenia, but results have been inconsistent.

METHODS

We conducted a meta-analysis of whole-brain, resting-state fMRI studies that explored differences in amplitude of low-frequency fluctuation (ALFF) between people with schizophrenia (including first episode and chronic) and healthy controls.

RESULTS

A systematic literature search identified 24 studies comparing a total of 1249 people with schizophrenia and 1179 healthy controls. Overall, patients with schizophrenia displayed decreased ALFF in the bilateral postcentral gyrus, bilateral precuneus, left inferior parietal gyri and right occipital lobe, and increased ALFF in the right putamen, right inferior frontal gyrus, left inferior temporal gyrus and right anterior cingulate cortex. In the subgroup analysis, patients with first-episode schizophrenia demonstrated decreased ALFF in the bilateral inferior parietal gyri, right precuneus and left medial prefrontal cortex, and increased ALFF in the bilateral putamen and bilateral occipital gyrus. Patients with chronic schizophrenia showed decreased ALFF in the bilateral postcentral gyrus, left precuneus and right occipital gyrus, and increased ALFF in the bilateral inferior frontal gyri, bilateral superior frontal gyrus, left amygdala, left inferior temporal gyrus, right anterior cingulate cortex and left insula.

LIMITATIONS

The small sample size of our subgroup analysis, predominantly Asian samples, processing steps and publication bias could have limited the accuracy of the results.

CONCLUSION

Our comprehensive meta-analysis suggests that findings of aberrant regional intrinsic brain activity during the initial stages of schizophrenia, and much more widespread damage with the progression of disease, may contribute to our understanding of the progressive pathophysiology of schizophrenia.

Dynamic changes of amplitude of low-frequency fluctuations in patients with generalized anxiety disorder

Previous neuroimaging studies have mainly focused on alterations of static and dynamic functional connectivity in patients with generalized anxiety disorder (GAD). However, the characteristics of local brain activity over time in GAD are poorly understood. This study aimed to investigate the abnormal time‐varying local brain activity of GAD by using the amplitude of low‐frequency fluctuation (ALFF) method combined with sliding‐window approach. Group comparison results showed that compared with healthy controls (HCs), patients with GAD exhibited increased dynamic ALFF (dALFF) variability in widespread regions, including the bilateral dorsomedial prefrontal cortex, hippocampus, thalamus, striatum; and left orbital frontal gyrus, inferior parietal lobule, temporal pole, inferior temporal gyrus, and fusiform gyrus. The abnormal dALFF could be used to distinguish between patients with GAD and HCs. Increased dALFF variability values in the striatum were positively correlated with GAD symptom severity. These findings suggest that GAD patients are associated with abnormal temporal variability of local brain activity in regions implicated in executive, emotional, and social function. This study provides insight into the brain dysfunction of GAD from the perspective of dynamic local brain activity, highlighting the important role of dALFF variability in understanding neurophysiological mechanisms and potentially informing the diagnosis of GAD.

Abnormal dynamic properties of functional connectivity in disorders of consciousness

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to research abnormal functional connectivity (FC) in patients with disorders of consciousness (DOC). However, most studies assumed steady spatial-temporal signal interactions between distinct brain regions during the scan period. The aim of this study was to explore abnormal dynamic functional connectivity (dFC) in DOC patients. After excluding 26 patients' data that failed to meet the requirements of imaging quality, we retained 19 DOC patients (12 with unresponsive wakefulness syndrome and 7 in a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised [CRS-R]) for the dFC analysis. We used the sliding windows approach to construct dFC matrices. Then these matrices were clustered into distinct states using the k-means clustering algorithm. We found that the DOC patients showed decreased dFC in the sensory and somatomotor networks compared with the healthy controls. There were also significant differences in temporal properties, the mean dwell time (MDT) and the number of transitions (NT), between the DOC patients and the healthy controls. In addition, we also used a hidden Markov model (HMM) to test the robustness of the results. With the connectome-based predictive modeling (CPM) approach, we found that the properties of abnormal dynamic network can be used to predict the CRS-R scores of the patients after severe brain injury. These findings may contribute to a better understanding of the abnormal brain networks in DOC patients.

Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations

Intrinsic neural activity ubiquitously persists in all physiological states. However, how intrinsic brain activity (iBA) changes over a short time remains unknown. To uncover the brain dynamics' theoretic underpinning, electrophysiological relevance, and neuromodulation, we identified iBA dynamics on simulated data, electroencephalogram-functional magnetic resonance imaging (EEG-fMRI) data, and repetitive transcranial magnetic stimulation (rTMS) fMRI data using sliding-window analysis. The temporal variability (dynamics) of iBA were quantified using the variance of the amplitude of low-frequency fluctuations (ALFF) over time. We first used simulated fMRI data to examine the effects of various parameters including window length, and step size on dynamic ALFF. Second, using EEG-fMRI data, we found that the heteromodal association cortex had the most variable dynamics while the limbic regions had the least, consistent with previous findings. In addition, the temporal variability of dynamic ALFF depended on EEG power fluctuations. Moreover, using rTMS fMRI data, we found that the temporal variability of dynamic ALFF could be modulated by rTMS. Taken together, these results provide evidence about the theory, relevance, and adjustability of iBA dynamics.

The preference for future outcomes correlates with the temporal variability of functional connectivity among brain regions

People inevitably make decisions between short-term and long-term consequences across domains like education, health and economics. In this kind of decision, the tendency to discount the value of later-larger rewards with increasing delays is defined as delay discounting (DD). A recent review has suggested that three neural systems which respectively responsible for valuation, prospection and cognitive control (e.g., ventromedial prefrontal cortex [vmPFC], hippocampus, precuneus and dorsolateral prefrontal cortex [dlPFC]) could interact with each other flexibly to have impacts on DD. However, to date, there is little attention paid on the connection between the DD and the dynamic interaction of brain regions.To tackle this issue, we investigate the relationship between the DD and the time-varying connectivity among brain regions in two samples of young adults. Results in sample 1 found that the DD was negatively correlated with the temporal variability of functional connectivity [FC] between the vmPFC and precuneus, and between the vmPFC and the left superior frontal gyrus. And the temporal variabilities of FC between the ventral striatum and right dlPFC, and between the ventral striatum and dorsomedial prefrontal cortex were also negatively related to DD. Furthermore, the main results were well replicated and validated in another sample using different analysis parameters. Overall, our findings reveal that temporal fluctuation of FC within default mode and fronto-striatal circuits can favor for prospecting future, cognitive control and valuation of delayed incentives, and this flexible connectivity patterns generally have association with preference for future outcomes.

Interregional causal influences of brain metabolic activity reveal the spread of aging effects during normal aging

During healthy brain aging, different brain regions show anatomical or functional declines at different rates, and some regions may show compensatory increases in functional activity. However, few studies have explored interregional influences of brain activity during the aging process. We proposed a causality analysis framework combining high dimensionality independent component analysis (ICA), Granger causality, and least absolute shrinkage and selection operator regression on longitudinal brain metabolic activity data measured by Fludeoxyglucose positron emission tomography (FDG-PET). We analyzed FDG-PET images from healthy old subjects, who were scanned for at least five sessions with an averaged intersession interval of 1 year. The longitudinal data were concatenated across subjects to form a time series, and the first-order autoregressive model was used to measure interregional causality among the independent sources of metabolic activity identified using ICA. Several independent sources with reduced metabolic activity in aging, including the anterior temporal lobe and orbital frontal cortex, demonstrated causal influences over many widespread brain regions. On the other hand, the influenced regions were more distributed, and had smaller age-related declines or even relatively increased metabolic activity. The current data demonstrated interregional spreads of aging on metabolic activity at the scale of a year, and have identified key brain regions in the aging process that have strong influences over other regions.

Temporal dynamic changes of intrinsic brain activity in schizophrenia with cigarette smoking

Mounting evidence from multimodal neuroimaging studies has supported a neurobiological basis for schizophrenia-nicotine dependence comorbidity. However, this evidence comes exclusively from studies measuring static intrinsic activity/connectivity of the brain, while the dynamic effects of this comorbidity remain poorly understood. The current study therefore sought to examine whether temporal dynamic intrinsic brain activity interacted with diagnosis (schizophrenics vs. healthy controls) and smoking status (smokers vs. non-smokers). We used a mixed sample design and included the following four groups: i) schizophrenic smokers (n = 22), ii) schizophrenic non-smokers (n = 27), iii) healthy control smokers (n = 22), and iv) healthy control non-smokers (n = 21). All subjects underwent functional magnetic resonance imaging during the resting state. The temporal variability in intrinsic brain activity among the four groups was compared using a novel dynamic amplitude of low-frequency fluctuation (dALFF) method. A significant main effect of diagnosis was found in the left superior parietal gyrus (SPG; F_{(1, 88)} = 142.1, P < 0.0001). Moreover, the dALFF strength in the SPG was positively correlated with disease duration in patients with schizophrenia (Rho₍₄₆₎ = 0.43, P = 0.002). In addition, a significant interaction between diagnosis and smoking status was observed in the left dorsolateral prefrontal cortex (DLPFC; F_{(1, 88)} = 7.39, P = 0.008), which was consistent with the self-medication hypothesis. Together, this study has demonstrated for the first time that nicotine restores dynamic intrinsic brain activity in the left DLPFC in patients with schizophrenia. This interaction may be a clinical neuromarker for increased comorbid smoking in schizophrenia.

Altered functional connectivity and low-frequency signal fluctuations in early psychosis and genetic high risk

Studying individuals at increased genetic risk for schizophrenia may generate important theories regarding the emergence of the illness. In this investigation, genetic high-risk individuals (GHR, n = 37) were assessed with functional magnetic resonance imaging and compared to individuals in the first episode of schizophrenia (FESZ, n = 42) and healthy comparison subjects (HCS, n = 59). Measures of functional connectivity and the amplitude of low-frequency fluctuation (ALFF) were obtained in a global, data-driven analysis. The functional connectivity measure, termed degree centrality, assessed each voxel's connectivity with all the other voxels in the brain. GHR and FESZ displayed increased degree centrality globally and locally. On ALFF measures, GHR were indistinguishable from HCS in the majority of areas but resembled FESZ in insula, basal ganglia and hippocampus. FESZ evidenced reduced amplitude of the global neural signal as compared to HCS and GHR. Results support the hypothesis that schizophrenia diathesis involves functional connectivity and ALFF abnormalities. In addition, they further an emerging theory suggesting that increased connectivity and metabolism may be involved in schizophrenia vulnerability and early stages of the illness.

Altered temporal dynamics of brain activity in patients with generalized tonic-clonic seizures

Generalized seizures engage bilateral networks from their onset at a low temporal scale. Previous studies findings have demonstrated focal/local brain activity abnormalities in the patients with generalized tonic-clonic seizures (GTCS). Resting state functional magnetic resonance imaging (fMRI) allows the detection of aberrant spontaneous brain activity in GTCS. Little is known, however, about alterations of dynamics (temporal variability) of spontaneous brain activity. It also remains unclear whether temporal variability of spontaneous brain activity is associated with disease severity. To address these questions, the current study assessed patients with GTCS (n = 35), and age- and sex-matched healthy controls (HCs, n = 33) who underwent resting state fMRI. We first assessed the dynamics of spontaneous brain activity using dynamic amplitude of low-frequency fluctuation (dALFF). Furthermore, the temporal variability of brain activity was quantified as the variance of dALFF across sliding window. Compared to HCs, patients with GTCS showed hyper-temporal variability of dALFF in parts of the default mode network, whereas they showed hypo-temporal variability in the somatomotor cortex. Furthermore, dynamic ALFF in the subgenual anterior cingulate cortex was positively correlated with duration of disease, indicating that disease severity is associated with excessive variability. These results suggest both an excessive variability and excessive stability in patients with GTCS. Overall, the current findings from brain activity dynamics contribute to our understanding of the pathophysiological mechanisms of generalized seizure.

In search of multimodal brain alterations in Alzheimer's and Binswanger's disease

Structural and functional brain abnormalities have been widely identified in dementia, but with variable replicability and significant overlap. Alzheimer's disease (AD) and Binswanger's disease (BD) share similar symptoms and common brain changes that can confound diagnosis. In this study, we aimed to investigate correlated structural and functional brain changes in AD and BD by combining resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A group independent component analysis was first performed on the fMRI data to extract 49 intrinsic connectivity networks (ICNs). Then we conducted a multi-set canonical correlation analysis on three features, functional network connectivity (FNC) between ICNs, fractional anisotropy (FA) and mean diffusivity (MD). Two inter-correlated components show significant group differences. The first component demonstrates distinct brain changes between AD and BD. AD shows increased cerebellar FNC but decreased thalamic and hippocampal FNC. Such FNC alterations are linked to the decreased corpus callosum FA. AD also has increased MD in the frontal and temporal cortex, but BD shows opposite alterations. The second component demonstrates specific brain changes in BD. Increased FNC is mainly between default mode and sensory regions, while decreased FNC is mainly within the default mode domain and related to auditory regions. The FNC changes are associated with FA changes in posterior/middle cingulum cortex and visual cortex and increased MD in thalamus and hippocampus. Our findings provide evidence of linked functional and structural deficits in dementia and suggest that AD and BD have both common and distinct changes in white matter integrity and functional connectivity.

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This is the first study to explore multi-modalities changes in different dementia.

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A multimodal fusion method is applied to identify joint components.

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Brain abnormalities in different modalities are highly correlated.

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Alzheimer's and Binswanger's disease share similar brain changes.

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Alzheimer's and Binswanger's disease also have distinct brain changes.

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

Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.

Abnormal thalamocortical network dynamics in migraine

OBJECTIVE

To investigate the dynamic functional connectivity of thalamocortical networks in interictal migraine patients and whether clinical features are associated with abnormal connectivity.

METHODS

We investigated dynamic functional network connectivity (dFNC) of the migraine brain in 89 interictal migraine patients and 70 healthy controls. We focused on the temporal properties of thalamocortical connectivity using sliding window cross-correlation, clustering state analysis, and graph-theory methods. Relationships between clinical symptoms and abnormal dFNC were evaluated using a multivariate linear regression model.

RESULTS

Five dFNC brain states were identified to characterize and compare dynamic functional connectivity patterns. We demonstrated that migraineurs spent more time in a strongly interconnected between-network state, but they spent less time in a sparsely connected state. Interestingly, we found that abnormal posterior thalamus (pulvinar nucleus) dFNC with the visual cortex and the precuneus were significantly correlated with headache frequency of migraine. Further topologic measures revealed that migraineurs had significantly lower efficiency of information transfer in both global and local dFNC.

CONCLUSION

Our results demonstrated a transient pathologic state with atypical thalamocortical connectivity in migraineurs and extended current findings regarding abnormal thalamocortical networks and dysrhythmia in migraine.

Altered dynamics of brain segregation and integration in poststroke aphasia

Poststroke aphasia (PSA) results from direct effect of focal lesions and dysfunction of distributed language networks. However, how flexible the activity at specific nodes control global dynamics is currently unknown. In this study, we demonstrate that alterations in the regional activity may cause imbalances between segregation and integration in temporo-spatial pattern, and the transient dynamics are disrupted in PSA patients. Specifically, we applied dynamic framework to eyes-closed resting-state functional MRI data from PSA patients (n = 17), and age-, gender-, and education-matched healthy controls (HCs, n = 20). Subsequently, we calculated two basis brain organizational principles: "dynamic segregation," obtained from dynamic amplitude of low-frequency fluctuations (dALFF), which represent the specialized processing within interconnected brain regions; and "dynamic integration," obtained from dynamic functional connectivity, which measures the efficient communication between interconnected brain regions. We found that both measures were decreased in the PSA patients within the left frontal and temporal subregions compared to the HCs. PSA patients displayed increased flexibility of interaction between left temporo-frontal subregions and right temporo-parieto-frontal subnetworks. Furthermore, we found that dALFF in the pars triangularis of left inferior frontal gyrus was associated with aphasia quotient. These findings suggest that the reduced temporal flexibility of regional activity in language-relevant cortical regions in PSA is related to the disrupted organization of intrahemispheric networks, leading to a loss of the corresponding functions. By using dynamic framework, our results offer valuable information about the alterations in segregation and integration of spatiotemporal information across networks and illuminate how dysfunction in flexible activity may underlie language deficits in PSA.

Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism

Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with social communication deficits and restricted/repetitive behaviors and is characterized by large-scale atypical subcortical-cortical connectivity, including impaired resting-state functional connectivity between thalamic and sensory regions. Previous studies have typically focused on the abnormal static connectivity in ASD and overlooked potential valuable dynamic patterns in brain connectivity. However, resting-state brain connectivity is indeed highly dynamic, and abnormalities in dynamic brain connectivity have been widely identified in psychiatric disorders. In this study, we investigated the dynamic functional network connectivity (dFNC) between 51 intrinsic connectivity networks in 170 individuals with ASD and 195 age-matched typically developing (TD) controls using independent component analysis and a sliding window approach. A hard clustering state analysis and a fuzzy meta-state analysis were conducted respectively, for the exploration of local and global aberrant dynamic connectivity patterns in ASD. We examined the group difference in dFNC between thalamic and sensory networks in each functional state and group differences in four high-dimensional dynamic measures. The results showed that compared with TD controls, individuals with ASD show an increase in transient connectivity between hypothalamus/subthalamus and some sensory networks (right postcentral gyrus, bi paracentral lobule, and lingual gyrus) in certain functional states, and diminished global meta-state dynamics of the whole-brain functional network. In addition, these atypical dynamic patterns are significantly associated with autistic symptoms indexed by the Autism Diagnostic Observation Schedule. These converging results support and extend previous observations regarding hyperconnectivity between thalamic and sensory regions and stable whole-brain functional configuration in ASD. Dynamic brain connectivity may serve as a potential biomarker of ASD and further investigation of these dynamic patterns might help to advance our understanding of behavioral differences in this complex neurodevelopmental disorder.

Altered Local and Large-Scale Dynamic Functional Connectivity Variability in Posttraumatic Stress Disorder: A Resting-State fMRI Study

Posttraumatic stress disorder (PTSD) is a psychiatric condition that can emerge after exposure to an exceedingly traumatic event. Previous neuroimaging studies have indicated that PTSD is characterized by aberrant resting-state functional connectivity (FC). However, few existing studies on PTSD have examined dynamic changes in resting-state FC related to network formation, interaction, and dissolution over time. In this study, we compared the dynamic resting-state local and large-scale FC between PTSD patients (n = 22) and healthy controls (HC; n = 22; conducted as standard deviation in resting-state local and large-scale FC over a series of sliding windows). Local dynamic FC was examined by calculating the dynamic regional homogeneity (dReHo), and large-scale dynamic FC (dFC) was investigated between regions with significant dReHo group differences. For the PTSD patients, we also investigated the relationship between symptom severity and dFC/dReHo. Our results showed that PTSD patients were characterized by I) increased dynamic (more variable) dReHo in left precuneus (PCu); II) increased dynamic (more variable) dFC between the left PCu and left insula; and III) decreased dFC between left PCu and left inferior parietal lobe (IPL), and decreased dFC between left PCu and right PCu. However, there is no significant correlation between the clinical indicators and dReHo/dFC after the family-wise-error (FWE) correction. These findings provided the initial evidence that PTSD is characterized by aberrant patterns of fluctuating communication within brain system such as the default mode network (DMN) and among different brain systems such as the salience network and the DMN.

Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities

Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.

Prediction of early response to overall treatment for schizophrenia: A functional magnetic resonance imaging study

INTRODUCTION

Treatment response at an early stage of schizophrenia is of considerable value with regard to future management of the disorder; however, there are currently no biomarkers that can inform physicians about the likelihood of response.

OBJECTS

We aim to develop and validate regional brain activity derived from functional magnetic resonance imaging (fMRI) as a potential signature to predict early treatment response in schizophrenia.

METHODS

Amplitude of low-frequency fluctuation (ALFF) was measured at the start of the first/single episode resulting in hospitalization. Inpatients were included in a principal dataset (n = 79) and a replication dataset (n = 44). Two groups of healthy controls (n = 87; n = 106) were also recruited for each dataset. The clinical response was assessed at discharge from the hospital. The predictive capacity of normalized ALFF in patients by healthy controls, ALFF_ratio , was evaluated based on diagnostic tests and clinical correlates.

RESULTS

In the principal dataset, responders exhibited increased baseline ALFF in the left postcentral gyrus/inferior parietal lobule relative to non-responders. ALFF_ratio of responders before treatment was significantly higher than that of non-responders (p < 0.001). The area under the receiver operating characteristic curve was 0.746 for baseline ALFF_ratio to distinguish responders from non-responders, and the sensitivity, specificity, and accuracy were 72.7%, 68.6%, and 70.9%, respectively. Similar results were found in the independent replication dataset.

CONCLUSIONS

Baseline regional activity of the brain seems to be predictive of early response to treatment for schizophrenia. This study shows that psycho-neuroimaging holds promise for influencing the clinical treatment and management of schizophrenia.

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network

Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.

Dynamic Alterations in Spontaneous Neural Activity in Multiple Brain Networks in Subacute Stroke Patients: A Resting-State fMRI Study

Objective: To examine whether subacute stroke patients would exhibit abnormal dynamic characteristics of brain activity relative to healthy controls (HC) and to investigate whether the altered dynamic regional indexes were associated with clinical behavior in stroke patients. Methods: The dynamic amplitude of low-frequency fluctuations (dALFF) and dynamic regional homogeneity (dReHo) in 42 subacute stroke patients and 55 healthy controls were compared. Correlation analyses between dALFF and dReHo in regions showing significant intergroup differences and clinical scores (i.e., the National Institutes of Health Stroke Scale, Fugl-Meyer assessment and lesion volume size) were conducted in stroke patients. Receiver operating characteristic (ROC) curve analysis was used to determine the potential value of altered dynamic regional indexes to identify stroke patients. Results: Significantly dALFF in the bilateral cerebellum posterior lobe (CPL), ipsilesional superior parietal lobe, ipsilesional inferior temporal gyrus (ITG), the midline supplementary motor area (SMA), ipsilesional putamen and lentiform nucleus were detected in stroke patients compared to HC. Relative to the HC group, the stroke patients showed significant differences in dReHo in the contralesional rectal gyrus, contralesional ITG, contralesional pons, ipsilesional middle frontal gyrus (MFG). Significant correlations between dALFF variability in midline SMA and Fugl-Meyer assessment (FMA) scores or between dReHo variability in the ipsilesional MFG and FMA scores were detected in stroke patients. Furthermore, the ROC curve revealed that dynamic ALFF at SMA and ReHo at ipsilesional MFG might have the potential to distinguish stroke patients. Conclusion: The pattern of intrinsic brain activity variability is altered in stroke patients compared with HC, and dynamic ALFF/ReHo might be potential tools to assess stroke patients' motor function.