Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

Abnormalities in spontaneous brain activity and functional connectivity are associated with cognitive impairments in children with type 1 diabetes mellitus

BACKGROUND

It remains unclear whether alterations in brain function occur in the early stage of pediatric type 1 diabetes mellitus(T1DM). We aimed to examine changes in spontaneous brain activity and functional connectivity (FC) in children with T1DM using resting-state functional magnetic resonance imaging (rs-fMRI), and to pinpoint potential links between neural changes and cognitive performance.

METHODS

In this study, 22 T1DM children and 21 age-, sex-matched healthy controls underwent rs-fMRI. The amplitude of low frequency fluctuations (ALFF) and seed-based FC analysis were performed to examine changes in intrinsic brain activity and functional networks in T1DM children. Partial correlation analyses were utilized to explore the correlations between ALFF values and clinical parameters.

RESULTS

The ALFF values were significantly lower in the lingual gyrus (LG) and higher in the left medial superior frontal gyrus (MSFG) in T1DM children compared to controls. Subsequent FC analysis indicated that the LG had decreased FC with bilateral inferior occipital gyrus, and the left MSFG had decreased FC with right precentral gyrus, right inferior parietal gyrus and right postcentral gyrus in children with T1DM. The ALFF values of LG were positively correlated with full-scale intelligence quotient and age at disease onset in T1DM children, while the ALFF values of left MSFG were positively correlated with working memory scores.

CONCLUSION

Our findings revealed abnormal spontaneous activity and FC in brain regions related to visual, memory, default mode network, and sensorimotor network in the early stage of T1DM children, which may aid in further understanding the mechanisms underlying T1DM-associated cognitive dysfunction.

Brain Functional Topology Alteration in Right Lateral Occipital Cortex Is Associated With Upper Extremity Motor Recovery

Stroke is a chief cause of sudden brain damage that severely disrupts the whole-brain network. However, the potential mechanisms of motor recovery after stroke are uncertain and the prognosis of poststroke upper extremity recovery is still a challenge. This study investigated the global and local topological properties of the brain functional connectome in patients with subacute ischemic stroke and their associations with the clinical measurements. A total of 57 patients, consisting of 29 left-sided and 28 right-sided stroke patients, and 32 age- and gender-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) study; patients were also clinically evaluated with the Upper Extremity Fugl-Meyer Assessment (FMA_UE). The assessment was repeated at 15 weeks to assess upper extremity functional recovery for the patient remaining in the study (12 left- 20 right-sided stroke patients). Global graph topological disruption indices of stroke patients were significantly decreased compared with HCs but these indices were not significantly associated with FMA_UE. In addition, local brain network structure of stroke patients was altered, and the altered regions were dependent on the stroke site. Significant associations between local degree and motor performance and its recovery were observed in the right lateral occipital cortex (R LOC) in the right-sided stroke patients. Our findings suggested that brain functional topologies alterations in R LOC are promising as prognostic biomarkers for right-sided subacute stroke. This cortical area might be a potential target to be further validated for non-invasive brain stimulation treatment to improve poststroke upper extremity recovery.

Depletion of acetate-producing bacteria from the gut microbiota facilitates cognitive impairment through the gut-brain neural mechanism in diabetic mice

BACKGROUND

Modification of the gut microbiota has been reported to reduce the incidence of type 1 diabetes mellitus (T1D). We hypothesized that the gut microbiota shifts might also have an effect on cognitive functions in T1D. Herein we used a non-absorbable antibiotic vancomycin to modify the gut microbiota in streptozotocin (STZ)-induced T1D mice and studied the impact of microbial changes on cognitive performances in T1D mice and its potential gut-brain neural mechanism.

RESULTS

We found that vancomycin exposure disrupted the gut microbiome, altered host metabolic phenotypes, and facilitated cognitive impairment in T1D mice. Long-term acetate deficiency due to depletion of acetate-producing bacteria resulted in the reduction of synaptophysin (SYP) in the hippocampus as well as learning and memory impairments. Exogenous acetate supplement or fecal microbiota transplant recovered hippocampal SYP level in vancomycin-treated T1D mice, and this effect was attenuated by vagal inhibition or vagotomy.

CONCLUSIONS

Our results demonstrate the protective role of microbiota metabolite acetate in cognitive functions and suggest long-term acetate deficiency as a risk factor of cognitive decline. Video Abstract.

Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes

CONTEXT

Individuals with type 1 diabetes mellitus (T1DM) have alterations in brain activity that have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain's resting state activity remains unclear.

OBJECTIVE

To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware).

DESIGN

Observational study.

SETTING

Academic medical center.

PARTICIPANTS

27 individuals with T1DM and 12 HC volunteers participated in the study.

INTERVENTION

All participants underwent blood oxygenation level dependent (BOLD) resting state functional magnetic brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dL)-hypoglycemic (60 mg/dL) clamp.

OUTCOME

Changes in resting state functional connectivity.

RESULTS

Using 2 separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia between HC, T1DM-Aware, and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016).

CONCLUSION

These findings provide evidence that individuals with T1DM have changes in the brain's resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes among individuals with T1DM.

Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Background and Purpose- Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods- We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results- Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions- These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

Disturbed functional connectivity within the left prefrontal cortex and sensorimotor areas predicts impaired cognitive speed in patients with first-episode schizophrenia

This study aimed at identifying abnormal cortico-cortical functional connectivity patterns that could predict cognitive slowing in patients with schizophrenia. A group of thirty-two patients with the first-episode schizophrenia and comparable healthy controls underwent resting-state qEEG and cognitive assessment. Phase Lag Index (PLI) was applied as a connectivity index and the synchronizations were analyzed in six frequencies. Pairs of electrodes were grouped to separately cover frontal, temporal, central, parietal and occipital regions. PLI was calculated for intra-regional connectivity and between-regions connectivity. Computer version processing speed tests were applied to control for possible fluctuations in cognitive efficiency during the performance of the tasks. In the group of patients, in comparison to healthy controls, significantly higher PLI values were recorded in theta frequency, especially in the posterior areas and decreased PLI in low-alpha frequency within the frontal regions. Mean PLI in gamma frequency was also lower in the patients group. Regression analysis showed that lower intra-regional PLI for left frontal cortex and higher PLI within somatosensory cortex in theta band, together with the duration of untreated psychosis, proved to be significant predictors of impaired processing speed in first-episode patients. Our investigation confirmed that disrupted cortico-cortical synchronization contributes to cognitive slowing in schizophrenia.

Assessment and elimination of the effects of head movement on MEG resting-state measures of oscillatory brain activity

Magnetoencephalography (MEG) is increasingly being used to study brain function because of its excellent temporal resolution and its direct association with brain activity at the neuronal level. One possible cause of error in the analysis of MEG data comes from the fact that participants, even MEG-experienced ones, move their head in the MEG system. Head movement can cause source localization errors during the analysis of MEG data, which can result in the appearance of source variability that does not reflect brain activity. The MEG community places great importance in eliminating this source of possible errors as is evident, for example, by recent efforts to develop head casts that limit head movement in the MEG system. In this work we use software tools to identify, assess and eliminate from the analysis of MEG data any possible correlations between head movement in the MEG system and widely-used measures of brain activity derived from MEG resting-state recordings. The measures of brain activity we study are a) the Hilbert-transform derived amplitude envelope of the beamformer time series and b) functional networks; both measures derived by MEG resting-state recordings. Ten-minute MEG resting-state recordings were performed on healthy participants, with head position continuously recorded. The sources of the measured magnetic signals were localized via beamformer spatial filtering. Temporal independent component analysis was subsequently used to derive resting-state networks. Significant correlations were observed between the beamformer envelope time series and head movement. The correlations were substantially reduced, and in some cases eliminated, after a participant-specific temporal high-pass filter was applied to those time series. Regressing the head movement metrics out of the beamformer envelope time series had an even stronger effect in reducing these correlations. Correlation trends were also observed between head movement and the activation time series of the default-mode and frontal networks. Regressing the head movement metrics out of the beamformer envelope time series completely eliminated these correlations. Additionally, applying the head movement correction resulted in changes in the network spatial maps for the visual and sensorimotor networks. Our results a) show that the results of MEG resting-state studies that use the above-mentioned analysis methods are confounded by head movement effects, b) suggest that regressing the head movement metrics out of the beamformer envelope time series is a necessary step to be added to these analyses, in order to eliminate the effect that head movement has on the amplitude envelope of beamformer time series and the network time series and c) highlight changes in the connectivity spatial maps when head movement correction is applied.

Lower cognitive performance among long-term type 1 diabetes survivors: A case-control study

OBJECTIVE

Patients with type 1 diabetes (T1D) have an increased risk of cognitive dysfunction. The cognitive decrement is believed to depend on macro- and microvascular complications and long disease duration. Some patients do not develop these complications, but still report cognitive symptoms. We examined if long-standing T1D without complications is associated with lower cognitive performance.

METHODS

A group of patients (n=43) with long-standing T1D (>30years) without micro- or macro vascular complications was compared with a non-diabetic control group (n=86) on six cognitive tests which probed episodic memory, semantic memory, episodic short-term memory, visual attention and psychomotor speed. Each patient was matched with two controls regarding age, gender and education. A linear mixed effect model was used to analyze the data.

RESULTS

The mean age was 57years and mean duration was 41years. Patients with diabetes had lower diastolic blood pressure but BMI, waist circumference, systolic blood pressure and smoking did not differ between groups. Patients had lower results than non-diabetic controls in episodic short-term memory (p<0.001) and also lower values on a test that mirrors visual attention and psychomotor speed (p=0.019).

CONCLUSIONS

Long-standing T1D was associated with lower cognitive performance, regardless of other diabetes-related complications.

Altered eigenvector centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus

INTRODUCTION

Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector centrality mapping (ECM) and degree centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree centrality) of regions within the whole-brain network hierarchy.

METHODS

Therefore, ECM and degree centrality of resting-state functional MRI-scans were compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition.

RESULTS

In all patients versus controls, ECM and degree centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (P_FWE  < 0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right cuneus and occipital fusiform gyrus versus controls (P_FWE  < 0.05). In all patients, ECM and degree centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (P_FWE  < 0.05), but not to subcortical gray matter volume or cognition (P_FDR  > 0.05).

CONCLUSION

The findings suggested reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression. Hum Brain Mapp 38:3623-3636, 2017. © 2017 Wiley Periodicals, Inc.

Integrating cross-frequency and within band functional networks in resting-state MEG: A multi-layer network approach

Neuronal oscillations exist across a broad frequency spectrum, and are thought to provide a mechanism of interaction between spatially separated brain regions. Since ongoing mental activity necessitates the simultaneous formation of multiple networks, it seems likely that the brain employs interactions within multiple frequency bands, as well as cross-frequency coupling, to support such networks. Here, we propose a multi-layer network framework that elucidates this pan-spectral picture of network interactions. Our network consists of multiple layers (frequency-band specific networks) that influence each other via inter-layer (cross-frequency) coupling. Applying this model to MEG resting-state data and using envelope correlations as connectivity metric, we demonstrate strong dependency between within layer structure and inter-layer coupling, indicating that networks obtained in different frequency bands do not act as independent entities. More specifically, our results suggest that frequency band specific networks are characterised by a common structure seen across all layers, superimposed by layer specific connectivity, and inter-layer coupling is most strongly associated with this common mode. Finally, using a biophysical model, we demonstrate that there are two regimes of multi-layer network behaviour; one in which different layers are independent and a second in which they operate highly dependent. Results suggest that the healthy human brain operates at the transition point between these regimes, allowing for integration and segregation between layers. Overall, our observations show that a complete picture of global brain network connectivity requires integration of connectivity patterns across the full frequency spectrum.

Resting-state functional MR imaging shed insights into the brain of diabetes

Diabetes mellitus is a common metabolic disease which is associated with increasing risk for multiple cognitive declines. Alterations in brain functional connectivity are believed to be the mechanisms underlying the cognitive function impairments. During the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) has been developed as a major tool to study brain functional connectivity in vivo. This paper briefly reviews the diabetes-associated cognitive impairment, analysis algorithms and clinical applications of rs-fMRI. We also provide future perspectives of rs-fMRI in diabetes.

Disrupted subject-specific gray matter network properties and cognitive dysfunction in type 1 diabetes patients with and without proliferative retinopathy

INTRODUCTION

Type 1 diabetes mellitus (T1DM) patients, especially with concomitant microvascular disease, such as proliferative retinopathy, have an increased risk of cognitive deficits. Local cortical gray matter volume reductions only partially explain these cognitive dysfunctions, possibly because volume reductions do not take into account the complex connectivity structure of the brain. This study aimed to identify gray matter network alterations in relation to cognition in T1DM.

METHODS

We investigated if subject-specific structural gray matter network properties, constructed from T1-weighted MRI scans, were different between T1DM patients with (n = 51) and without (n = 53) proliferative retinopathy versus controls (n = 49), and were associated to cognitive decrements and fractional anisotropy, as measured by voxel-based TBSS. Global normalized and local (45 bilateral anatomical regions) clustering coefficient and path length were assessed. These network properties measure how the organization of connections in a network differs from that of randomly connected networks.

RESULTS

Global gray matter network topology was more randomly organized in both T1DM patient groups versus controls, with the largest effects seen in patients with proliferative retinopathy. Lower local path length values were widely distributed throughout the brain. Lower local clustering was observed in the middle frontal, postcentral, and occipital areas. Complex network topology explained up to 20% of the variance of cognitive decrements, beyond other predictors. Exploratory analyses showed that lower fractional anisotropy was associated with a more random gray matter network organization.

CONCLUSION

T1DM and proliferative retinopathy affect cortical network organization that may consequently contribute to clinically relevant changes in cognitive functioning in these patients.

Age of Childhood Onset in Type 1 Diabetes and Functional Brain Connectivity in Midlife

OBJECTIVES

The development of Type 1 diabetes mellitus (T1DM) within the first 7 years of life has been linked to poorer cognitive performance. Adults with T1DM have altered functional brain connectivity, but no studies have examined whether earlier age of T1DM onset is associated with functional connectivity later in life. Accordingly, we tested the relationship between age of onset and resting state functional connectivity in a cohort of middle-aged adults with childhood-onset T1DM.

METHODS

Participants were from a subsample of the Pittsburgh Epidemiology of Diabetes Complications cohort and included 66 adults (mean age = 47.54 years, 32 men). Resting state blood oxygen level-dependent activity was used to calculate mean connectivity for eight functional brain networks. A multivariate analysis of variance examined associations between age of onset and network connectivity. Diffusion tensor and fluid-attenuated inversion recovery images were analyzed to identify microstructural alterations and white-matter hyperintensity volumes.

RESULTS

Later childhood onset of T1DM was associated with lower connectivity (F(8,57) = 2.40, p = .026). A significant interaction was present for current age such that an inverse association with age of onset for functional connectivity was present in older individuals (F(8,55) = 2.88, p = .035). Lower connectivity was associated with older age, increased white-matter hyperintensity volume, and lower microstructural integrity.

CONCLUSIONS

Diagnosis of T1DM later in childhood may be associated with lower brain functional connectivity, particularly in those surviving into older ages. These alterations may be an early marker for subsequent cognitive decrements. Future studies are warranted to understand the pathways underlying these associations.

Short and long term neuro-behavioral alterations in type 1 diabetes mellitus pediatric population

Type 1 diabetes mellitus (T1DM) is one of the most prevalent chronic conditions affecting individuals under the age of 18 years, with increasing incidence worldwide, especially among very young age groups, younger than 5. There is still no cure for the disease, and therapeutic goals and guidelines are a challenge. Currently, despite T1DM intensive management and technological interventions in therapy, the majority of pediatric patients do not achieve glycemic control goals. This leads to a potential prognosis of long term diabetic complications, nephrological, cardiac, ophthalmological and neurological. Unfortunately, the neurological manifestations, including neurocognitive and behavioral complications, may present soon after disease onset, during childhood and adolescence. These manifestations may be prominent, but at times subtle, thus they are often not reported by patients or physicians as related to the diabetes. Furthermore, the metabolic mechanism for such manifestations has been inconsistent and difficult to interpret in practical clinical care, as reported in several reviews on the topic of brain and T1DM. However, new technological methods for brain assessment, as well as the introduction of continuous glucose monitoring, provide new insights and information regarding brain related manifestations and glycemic variability and control parameters, which may impact the clinical care of children and youth with T1DM. This paper provides a comprehensive review of the most recently reported behavioral, cognitive domains, sleep related, electrophysiological, and structural alterations in children and adolescences from a novel point of view. The review focuses on reported impairments based on duration of T1DM, its timeline, and modifiable disease related risk parameters. These findings are not without controversy, and limitations of data are presented in addition to recommendations for future research direction.

Impairment of functional integration of the default mode network correlates with cognitive outcome at three months after stroke

Resting‐state studies conducted with stroke patients are scarce. The study of brain activity and connectivity at rest provides a unique opportunity for the investigation of brain rewiring after stroke and plasticity changes. This study sought to identify dynamic changes in the functional organization of the default mode network (DMN) of stroke patients at three months after stroke. Eleven patients (eight male and three female; age range: 48–72) with right cortical and subcortical ischemic infarctions and 17 controls (eleven males and six females; age range: 57–69) were assessed by neurological and neuropsychological examinations and scanned with resting‐state functional magnetic ressonance imaging. First, we explored group differences in functional activity within the DMN by means of probabilistic independent component analysis followed by a dual regression approach. Second, we estimated functional connectivity between 11 DMN nodes both locally by means of seed‐based connectivity analysis, as well as globally by means of graph‐computation analysis. We found that patients had greater DMN activity in the left precuneus and the left anterior cingulate gyrus when compared with healthy controls (P < 0.05 family‐wise error corrected). Seed‐based connectivity analysis showed that stroke patients had significant impairment (P = 0.014; threshold = 2.00) in the connectivity between the following five DMN nodes: left superior frontal gyrus (lSFG) and posterior cingulate cortex (t = 2.01); left parahippocampal gyrus and right superior frontal gyrus (t = 2.11); left parahippocampal gyrus and lSFG (t = 2.39); right parietal and lSFG (t = 2.29). Finally, mean path length obtained from graph‐computation analysis showed positive correlations with semantic fluency test (r_s = 0.454; P = 0.023), phonetic fluency test (r_s = 0.523; P = 0.007) and the mini mental state examination (r_s = 0.528; P = 0.007). In conclusion, the ability to regulate activity of the DMN appears to be a central part of normal brain function in stroke patients. Our study expands the understanding of the changes occurring in the brain after stroke providing a new avenue for investigating lesion‐induced network plasticity. Hum Brain Mapp 36:577–590, 2015. © 2014 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.