Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Sendi, Mohammad S.E.; Zendehrouh, Elaheh; Miller, Robyn L.; Fu, Zening; Du, Yuhui; Liu, Jingyu; Mormino, Elizabeth C.; Salat, David H.; Calhoun, Vince D.

doi:10.3389/fncir.2020.593263

Cited by 45 publications

(57 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After calculating the dFC of each subject separately, we vectorized each FC window and concatenated all subjects, including both the SZ and HC groups, as shown in Step 3 of Figure 1. Next, the k-means clustering algorithm was applied to the dFC windows to partition the concatenated matrix into a set of distinct clusters or states (Allen et al, 2014;Calhoun et al, 2014;Zhi et al, 2018;Sendi et al, 2021a). An FC state, which is a conceptual analogy of an EEG microstate, is a global pattern of DMN connectivity that remains quasi-stable for a short period of time before changing to another connectivity pattern that also remains quasi-stable (Calhoun et al, 2014).…”

Section: Clustering and Latent Transition Feature Estimationmentioning

confidence: 99%

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Sendi

Zendehrouh

Ellis

et al. 2021

Front. Neural Circuits

Self Cite

View full text Add to dashboard Cite

Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized.Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects.Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity.Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.

show abstract

Section: Clustering and Latent Transition Feature Estimationmentioning

confidence: 99%

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Sendi

Zendehrouh

Ellis

et al. 2021

Front. Neural Circuits

Self Cite

View full text Add to dashboard Cite

show abstract

“…Dynamic FNC refers to brain connectivity within subintervals of the time series, as opposed to static FNC, which reflects averaged brain connections over an entire scan (Calhoun et al, 2014 ). In recent years, dFNC estimated from rs_fMRI time series has been highly informative about the underlying different brain regions connectivity patterns in various brain disorders, including schizophrenia, MDD, and Alzheimer's disease (Sendi et al, 2020 , 2021a , c ). As a result, we hypothesized that looking at the effect of ECT on dFNC might reveal how and to what extent ECT affects dynamic connectivity changes in the DMN and CCN.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

Dini

Sendi

Sui

et al. 2021

Front. Hum. Neurosci.

Self Cite

View full text Add to dashboard Cite

Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states.Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 ± 6.14 and 11.48 ± 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called “occupancy rate” or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants.Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03).Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.

show abstract

“…These domains include the subcortical network (SCN), auditory network (ADN), sensorimotor network (SMN), visual network (VSN), cognitive control network (CCN), default-mode network (DMN), and cerebellar network (CBN). The 53 extracted ICNs were further described in [16].…”

Section: Methodsmentioning

confidence: 99%

“…It is made The copyright holder for this preprint this version posted February 14, 2021. ; https://doi.org/10.1101/2021.02.14.431143 doi: bioRxiv preprint mode network (DMN), and cerebellar network (CBN). The 53 extracted ICNs were further described in [16].…”

Section: Data Processingmentioning

confidence: 99%

The relationship between dynamic functional network connectivity and spatial orientation in healthy young adults

Mse

Miller

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Spatial orientation is essential to interacting with a physical environment, and better understanding it could contribute to a better understanding of a variety of diseases and disorders that are characterized by deficits in spatial orientation. Many previous studies have focused on the relationship between spatial orientation and individual brain regions, though in recent years studies have begun to examine spatial orientation from a network perspective. This study analyzes dynamic functional network connectivity (dFNC) values extracted from over 800 resting-state fMRI recordings of healthy young adults (age 22-37 years) and applies unsupervised machine learning methods to identify neural brain states that occur across all subjects. We estimated the occupancy rate (OCR) for each subject, which was proportional to the amount of time that they spent in each state, and investigated the link between the OCR and spatial orientation and the state-specific FNC values and spatial orientation controlling for age and sex. Our findings showed that the amount of time subjects spent in a state characterized by increased connectivity within and between visual, auditory, and sensorimotor networks and within the default mode network while at rest corresponded to their performance on tests of spatial orientation. We also found that increased sensorimotor network connectivity in two of the identified states negatively correlated with decreased spatial orientation, further highlighting the relationship between the sensorimotor network and spatial orientation. This study provides insight into how the temporal properties of the functional brain connectivity within and between key brain networks may influence spatial orientation.

show abstract

Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

Cited by 45 publications

References 67 publications

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

The relationship between dynamic functional network connectivity and spatial orientation in healthy young adults

Contact Info

Product

Resources

About