Resting-State Brain Networks in Type 1 Diabetic Patients
Resting-State Brain Networks in Type 1 Diabetic Patients
Cognitive functioning depends on intact brain networks that can be assessed with functional magnetic resonance imaging (fMRI) techniques. We hypothesized that cognitive decrements in type 1 diabetes mellitus (T1DM) are associated with alterations in resting-state neural connectivity and that these changes vary according to the degree of microangiopathy. T1DM patients with (MA: n = 49) and without (MA: n = 52) microangiopathy were compared with 48 healthy control subjects. All completed a neuropsychological assessment and resting-state fMRI. Networks were identified using multisubject independent component analysis; specific group differences within each network were analyzed using the dual-regression method, corrected for confounding factors and multiple comparisons. Relative to control subjects, MA patients showed increased connectivity in networks involved in motor and visual processes, whereas MA patients showed decreased connectivity in networks involving attention, working memory, auditory and language processing, and motor and visual processes. Better information-processing speed and general cognitive ability were related to increased degree of connectivity. T1DM is associated with a functional reorganization of neural networks that varies, dependent on the presence or absence of microangiopathy.
Type 1 diabetes mellitus (T1DM) increases the risk of cognitive dysfunction, particularly in the domains of verbal intelligence, attention, executive function, and mental speed. These cognitive decrements have been associated with alterations in brain structure and become more prominent as diabetes progresses. The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) has shown that chronic hyperglycemia, rather than severe hypoglycemia, is the best predictor of cognitive changes. Microangiopathy in the eye and kidney has been identified by the DCCT/EDIC and other studies to be the most important biomedical correlate of cognitive decline, serving perhaps, as microangiopathy is believed to be a generalized condition, as a surrogate marker of cerebral microangiopathy. As patients without clinical microangiopathy will inevitably have had hyperglycemic exposure, they may also show (more subtle) cerebral changes.
Cognitive performance is known to be mediated by multiple interacting brain circuits and their connections. The integrity of this circuitry can be inferred from the degree of "functional connectivity," which is based on the concept that intercorrelations between clusters of neural activity, recorded from different brain regions, reflect exchange of information (i.e., functional connectivity) between these regions.
In a previous study, we used magnetoencephalography to map functional connectivity and found that in the resting state, functional connectivity was decreased in patients with microangiopathy versus control subjects, whereas it tended to increase in one frequency band in patients without complications. That study also showed that increased functional connectivity was correlated with better cognitive performance in domains—information-processing speed, attention, and executive functions—most disrupted in T1DM patients. However, since magnetoencephalography has a low spatial resolution, it is difficult to accurately determine which brain regions are specifically involved in the functional networks that are correlated with cognitive functions.
Functional magnetic resonance imaging (fMRI) is another technique to evaluate functional connectivity. Unlike magnetoencephalography, fMRI has a high spatial resolution and is well suited to identify brain regions with altered functional connectivity. fMRI can be performed during rest or during a task. In the resting-state, spontaneous low-frequency fluctuations in the blood oxygenation level–dependent (BOLD) signal occur in the brain. These fluctuations have been shown to possess strong temporal coherence and have been characterized as neuronal circuits. Resting-state fMRI enables differentiation of neuronal circuits independent of specific cognitive functions and task paradigms.
These circuits can be identified using independent-component analysis (ICA). This data-driven, reference-free method decomposes the acquired fMRI time course into different statistically independent time courses and, accordingly, creates spatial maps for these time courses. Examples of neuronal networks identified by this technique include the default mode network and sensorimotor, dorsal and ventral attention, frontal, auditory and language-processing, primary and secondary visual, and left and right frontoparietal networks. The existence of these networks has been consistently verified across conditions, including normal aging, mild cognitive impairment, and Alzheimer disease; multiple sclerosis; and depression.
At present, resting-state fMRI has not been applied to T1DM patients to assess changes in spatial localization of functional connectivity. To determine whether neuronal circuits are altered by T1DM and, if so, in which anatomical areas, we performed resting-state fMRI in T1DM patients with (MA) and without (MA) microangiopathy and in comparison with subjects without diabetes. We also assessed the relationship between the integrity of these neuronal circuits and cognition. Based on recent data suggesting that peripheral microangiopathy, as a surrogate marker of chronic hyperglycemia, is associated with cerebral changes, we hypothesized that alterations in fMRI-measured functional connectivity and cognition would be most marked in patients who developed clinically measurable microangiopathy, secondary to chronic hyperglycemia, and to a lesser extent in patients free of microangiopathy, i.e., those with lower glycemic burden.
Abstract and Introduction
Abstract
Cognitive functioning depends on intact brain networks that can be assessed with functional magnetic resonance imaging (fMRI) techniques. We hypothesized that cognitive decrements in type 1 diabetes mellitus (T1DM) are associated with alterations in resting-state neural connectivity and that these changes vary according to the degree of microangiopathy. T1DM patients with (MA: n = 49) and without (MA: n = 52) microangiopathy were compared with 48 healthy control subjects. All completed a neuropsychological assessment and resting-state fMRI. Networks were identified using multisubject independent component analysis; specific group differences within each network were analyzed using the dual-regression method, corrected for confounding factors and multiple comparisons. Relative to control subjects, MA patients showed increased connectivity in networks involved in motor and visual processes, whereas MA patients showed decreased connectivity in networks involving attention, working memory, auditory and language processing, and motor and visual processes. Better information-processing speed and general cognitive ability were related to increased degree of connectivity. T1DM is associated with a functional reorganization of neural networks that varies, dependent on the presence or absence of microangiopathy.
Introduction
Type 1 diabetes mellitus (T1DM) increases the risk of cognitive dysfunction, particularly in the domains of verbal intelligence, attention, executive function, and mental speed. These cognitive decrements have been associated with alterations in brain structure and become more prominent as diabetes progresses. The Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) has shown that chronic hyperglycemia, rather than severe hypoglycemia, is the best predictor of cognitive changes. Microangiopathy in the eye and kidney has been identified by the DCCT/EDIC and other studies to be the most important biomedical correlate of cognitive decline, serving perhaps, as microangiopathy is believed to be a generalized condition, as a surrogate marker of cerebral microangiopathy. As patients without clinical microangiopathy will inevitably have had hyperglycemic exposure, they may also show (more subtle) cerebral changes.
Cognitive performance is known to be mediated by multiple interacting brain circuits and their connections. The integrity of this circuitry can be inferred from the degree of "functional connectivity," which is based on the concept that intercorrelations between clusters of neural activity, recorded from different brain regions, reflect exchange of information (i.e., functional connectivity) between these regions.
In a previous study, we used magnetoencephalography to map functional connectivity and found that in the resting state, functional connectivity was decreased in patients with microangiopathy versus control subjects, whereas it tended to increase in one frequency band in patients without complications. That study also showed that increased functional connectivity was correlated with better cognitive performance in domains—information-processing speed, attention, and executive functions—most disrupted in T1DM patients. However, since magnetoencephalography has a low spatial resolution, it is difficult to accurately determine which brain regions are specifically involved in the functional networks that are correlated with cognitive functions.
Functional magnetic resonance imaging (fMRI) is another technique to evaluate functional connectivity. Unlike magnetoencephalography, fMRI has a high spatial resolution and is well suited to identify brain regions with altered functional connectivity. fMRI can be performed during rest or during a task. In the resting-state, spontaneous low-frequency fluctuations in the blood oxygenation level–dependent (BOLD) signal occur in the brain. These fluctuations have been shown to possess strong temporal coherence and have been characterized as neuronal circuits. Resting-state fMRI enables differentiation of neuronal circuits independent of specific cognitive functions and task paradigms.
These circuits can be identified using independent-component analysis (ICA). This data-driven, reference-free method decomposes the acquired fMRI time course into different statistically independent time courses and, accordingly, creates spatial maps for these time courses. Examples of neuronal networks identified by this technique include the default mode network and sensorimotor, dorsal and ventral attention, frontal, auditory and language-processing, primary and secondary visual, and left and right frontoparietal networks. The existence of these networks has been consistently verified across conditions, including normal aging, mild cognitive impairment, and Alzheimer disease; multiple sclerosis; and depression.
At present, resting-state fMRI has not been applied to T1DM patients to assess changes in spatial localization of functional connectivity. To determine whether neuronal circuits are altered by T1DM and, if so, in which anatomical areas, we performed resting-state fMRI in T1DM patients with (MA) and without (MA) microangiopathy and in comparison with subjects without diabetes. We also assessed the relationship between the integrity of these neuronal circuits and cognition. Based on recent data suggesting that peripheral microangiopathy, as a surrogate marker of chronic hyperglycemia, is associated with cerebral changes, we hypothesized that alterations in fMRI-measured functional connectivity and cognition would be most marked in patients who developed clinically measurable microangiopathy, secondary to chronic hyperglycemia, and to a lesser extent in patients free of microangiopathy, i.e., those with lower glycemic burden.