scholarly journals Integrated PET/MR for Resting State Functional and Metabolic Imaging in Human Brain: What is Correlated and What is Impacted

2021 ◽  
Author(s):  
Yi Shan ◽  
Zhe Wang ◽  
Shuangshuang Song ◽  
Qiaoyi Xue ◽  
Hongwei Yang ◽  
...  
Keyword(s):  
Standardized Uptake Value ◽  
Correlation Coefficients ◽  
Network Connectivity ◽  
Self Control ◽  
Metabolic Imaging ◽  
Middle Temporal Gyrus ◽  
Frontal Pole ◽  
Whole Brain ◽  
Pet Quantitation ◽  
Group Ica

Abstract Purpose We aimed to, for the first time, investigate the interplay of simultaneous functional MRI (fMRI) and FDG PET using a randomized self-control protocol on an integrated PET/MR. Materials and methods 24 healthy volunteers underwent PET/MR scan 30 to 40 minutes after the injection of FDG. A 22-minute brain scan was separated into MRI-off (without fMRI pulsing) and MRI-on modes (with fMRI pulsing) with each one lasting for 11 minutes. We calculated the voxel-wise fMRI metric (ReHo, ALFF, fALFF and DC), resting networks, relative standardized uptake value ratios (SUVr), Patlak Ki and regional cerebral metabolic rate of glucose (rCMRGlu) maps. Paired two-sample t-tests were applied to assess the statistical differences between SUVr, Ki, correlation coefficients of fMRI metrics and rCMRGlu between MRI-off and MRI-on mode, respectively. Results Voxel-wise whole brain SUVr in MRI-off mode and MRI-on mode revealed no statistical difference, while Ki was significantly elevated in the whole brain (P༜0.05) during fMRI scan. Task-based group ICA revealed that the most active network components derived from combined MRI-off and MRI-on static PET images were frontal pole, superior frontal gyrus, middle temporal gyrus and occipital pole. High correlation coefficients were found among the four-fMRI metrics with rCMRGlu in MRI-off and MRI-on mode (P༜0.05). The highest correlation coefficients between rCMRGlu and all fMRI metrics were found in the visual network (R, 0.523 ± 0.057) and default network (R, 0.461 ± 0.099). Conclusions Static PET quantitation SUVr as an indicator of the accumulative effect of FDG update post-injection does not exhibit immediate change between MRI-on and MRI-off modes. Dynamic PET quantitation Ki is instantly elevated during MRI-on mode due to the additional impact of MRI sequence on imaging subjects. Network connectivity analysis also demonstrated intermediate modulation of brain function in MRI-on mode as compared to MRI-off mode.

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

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohammad S. E. Sendi ◽  
Elaheh Zendehrouh ◽  
Robyn L. Miller ◽  
Zening Fu ◽  
Yuhui Du ◽  
...  
Keyword(s):  
Default Mode Network ◽  
Early Stage ◽  
Network Connectivity ◽  
Functional Network ◽  
Normal Brain ◽  
Whole Brain ◽  
Mild Dementia ◽  
Sensorimotor Network ◽  
Functional Network Connectivity ◽  
The Brain

BackgroundAlzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.MethodWe used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.ResultsAll states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.ConclusionOur results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

scholarly journals Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia

2020 ◽  
Author(s):  
Mohammad S.E Sendi ◽  
Godfrey D Pearlson ◽  
Daniel H Mathalon ◽  
Judith M Ford ◽  
Adrian Preda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Visual Processing ◽  
Information Transfer ◽  
Pearson Correlation ◽  
Visual Learning ◽  
Network Connectivity ◽  
Resting State Fmri ◽  
Connectivity Pattern ◽  
Middle Temporal Gyrus ◽  
Middle Temporal

Although visual processing impairments have been explored in schizophrenia (SZ), their underlying neurobiology of the visual processing impairments has not been widely studied. Also, while some research has hinted at differences in information transfer and flow in SZ, there are few investigations of the dynamics of functional connectivity within visual networks. In this study, we analyzed resting-state fMRI data of the visual sensory network (VSN) in 160 healthy control (HC) subjects and 151 SZ subjects. We estimated 9 independent components within the VSN. Then, we calculated the dynamic functional network connectivity (dFNC) using the Pearson correlation. Next, using k-means clustering, we partitioned the dFNCs into five distinct states, and then we calculated the portion of time each subject spent in each state, that we termed the occupancy rate (OCR). Using OCR, we compared HC with SZ subjects and investigated the link between OCR and visual learning in SZ subjects. Besides, we compared the VSN functional connectivity of SZ and HC subjects in each state. We found that this network is indeed highly dynamic. Each state represents a unique connectivity pattern of fluctuations in VSN FNC, and all states showed significant disruption in SZ. Overall, HC showed stronger connectivity within the VSN in states. SZ subjects spent more time in a state in which the connectivity between the middle temporal gyrus and other regions of VNS is highly negative. Besides, OCR in a state with strong positive connectivity between middle temporal gyrus and other regions correlated significantly with visual learning scores in SZ.

scholarly journals IN VIVO ASSESSMENT OF AMYLOID AND GLUCOSE SIGNATURES IN SUBJECTIVE COGNITIVE DECLINE SUBJECTS

2021 ◽  
pp. 2140018
Author(s):  
XIAOFENG YU ◽  
ZHILONG ZHU ◽  
SHUZHAN ZHENG ◽  
JIAN JIANG ◽  
JUANJUAN JIANG ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Characteristic Curve ◽  
Standardized Uptake Value ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Subjective Cognitive Decline ◽  
Middle Temporal ◽  
Main Effect ◽  
Heschl Gyrus

Subjective cognitive decline (SCD), characterized by self-perceived subtle cognitive impairment ahead of the appearance of explicit and measurable cognitive deficits, is regarded as the preclinical manifestation of the pathological change continuum of Alzheimer’s disease (AD). We were committed to exploring the amyloid and glucose metabolic signatures related to imminent brain metabolic changes in SCD subjects. This study included 39 subjects (mean age = 71.9 years; 14 males and 25 females) diagnosed with SCD disease and 39 gender-matched healthy controls (HCs) (mean age = 75.2; 16 males and 23 females) with brain [18F] fluorodeoxyglucose positron emission tomography (PET) images and [18F] florbetapir PET images. The standardized uptake value ratios (SUVRs) of PET images within the regions of interest (ROIs) were calculated. Inter-group SUVR differences were assessed by two-sample [Formula: see text]-testing and receiver operating characteristic curve (ROC) analyses. A generalized linear model (GLM) was employed to evaluate the correlations between amyloid and FDG uptake. Compared with HCs, SCD subjects showed significantly increased amyloid SUVR, as well as significantly increased glucose SUVR in the olfactory, amygdala, thalamus, heschl gyrus, superior and middle temporal gyrus and temporal pole (all [Formula: see text]). The amyloid SUVR of thalamus was found to have a better ROC result (area under the curve (AUC): 0.77, 95% confidence interval (CI): 0.66–0.86) in the HC group, as was the case with the glucose SUVR of the middle temporal gyrus (AUC: 0.83, 95% CI: 0.73–0.91). There were significant positive correlations between amyloid and glucose SUVRs ([Formula: see text]). The amyloid SUVR of the thalamus showed a significantly better main effect (odd ratio [Formula: see text] 2.91, 95% CI: 1.44–6.7, [Formula: see text]), and the glucose SUVR of the heschl gyrus indicated an enhanced main effect (odd ratio [Formula: see text] 5.08, 95% CI: 1.86–18.15, [Formula: see text]). SCD subjects demonstrated significant amyloid accumulation and glucose hypermetabolism in specific brain regions, and amyloid pathology overlapped with regions of glucose abnormality. These findings may advance the understanding of imminent pathological changes in the SCD stage and help to provide clinical guidelines for interventional management.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

Social Skills and Emotional and Behavioral Traits of Preschool Children

2011 ◽  
Vol 39 (9) ◽  
pp. 1281-1287 ◽  
Author(s):  
Emel Arslan ◽  
Neslihan Durmuşoğlu-Saltali ◽  
Hasan Yilmaz
Keyword(s):  
Social Skills ◽  
Preschool Children ◽  
School Readiness ◽  
Family Involvement ◽  
Emotional Regulation ◽  
Positive Relationship ◽  
Rating Scale ◽  
Correlation Coefficients ◽  
Self Control ◽  
Behavioral Traits

We investigated the relationship between the emotional and behavioral traits and social skills of preschool children. The participants were 224 6-year-old children (115 female, 109 male). Data were collected using the Social Skills Evaluation Scale (Avcıoğlu, 2003) and the Preschool Behavioral and Emotional Rating Scale (Epstein, Synhorst, Cress, & Allen, 2009). Pearson product-moment correlation coefficients were determined. It was found that there was a positive relationship between interpersonal skills and emotional regulation, school readiness, social confidence, and family involvement. It was also found that there was a statistically significant positive relationship between verbal explanation, listening skills, and self-control and emotional regulation, school readiness, social confidence, and family involvement.

scholarly journals The Relationship Between IDH1 Mutation Status and Metabolic Imaging in Nonenhancing Supratentorial Diffuse Gliomas: A 11C-MET PET Study

2019 ◽  
Vol 18 ◽  
pp. 153601211989408
Author(s):  
Nijiati Kudulaiti ◽  
Huiwei Zhang ◽  
Tianming Qiu ◽  
Junfeng Lu ◽  
Abudumijiti Aibaidula ◽  
...  
Keyword(s):  
Standardized Uptake Value ◽  
Idh1 Mutation ◽  
Metabolic Imaging ◽  
Wild Type ◽  
Isocitrate Dehydrogenase 1 ◽  
Mutation Status ◽  
Positron Emission ◽  
Diffuse Gliomas ◽  
The Relationship ◽  
Met Pet

Purpose: We evaluated the relationship between isocitrate dehydrogenase 1 (IDH1) mutation status and metabolic imaging in patients with nonenhancing supratentorial diffuse gliomas using 11C-methionine positron emission tomography (11C-MET PET). Materials and Methods: Between June 2012 and November 2017, we enrolled 86 (38 women and 48 men; mean age, 41.9 ± 13.1 years [range, 8-67 years]) patients with newly diagnosed supratentorial diffuse gliomas. All patients underwent preoperative 11C-MET PET. Tumor samples were obtained and immunohistochemically analyzed for IDH1 mutation status. Results: The mutant and wild-type IDH1 diffuse gliomas had significantly different mean maximum standardized uptake value values (2.73 [95% confidence interval, CI: 2.32-3.16] vs 3.85 [95% CI: 3.22-4.51], respectively; P = .004) and mean tumor-to-background ratio (1.90 [95% CI: 1.65-2.16] vs 2.59 [95% CI: 2.17-3.04], respectively; P = .007). Conclusions: 11C-methionine PET can noninvasively evaluate the IDH1 mutation status of patients with nonenhancing supratentorial diffuse gliomas.

scholarly journals Whole-Brain Functional Network Connectivity Abnormalities in Affective and Non-Affective Early Phase Psychosis

2021 ◽  
Vol 15 ◽  
Author(s):  
Zening Fu ◽  
Armin Iraji ◽  
Jing Sui ◽  
Vince D. Calhoun
Keyword(s):  
Early Phase ◽  
Network Connectivity ◽  
Assessment Instrument ◽  
Functional Network ◽  
State Transitions ◽  
Affective Psychosis ◽  
Whole Brain ◽  
Affective Psychoses ◽  
Functional Network Connectivity ◽  
Brain Functional Network

Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called “Neuromark” was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

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