Yael D. Reijmer

Cognitive dysfunction in patients with type 2 diabetes.

People with diabetes mellitus are at increased risk of cognitive dysfunction and dementia. This review explores the nature and severity of cognitive changes in patients with type 2 diabetes. Possible risk factors such as hypo‐ and hyperglycemia, vascular risk factors, micro‐ and macrovascular complications, depression and genetic factors will be examined, as well as findings from brain imaging and autopsy studies. We will show that type 2 diabetes is associated with modest cognitive decrements in non‐demented patients that evolve only slowly over time, but also with an increased risk of more severe cognitive deficits and dementia. There is a dissociation between these two ‘types’ of cognitive dysfunction with regard to affected age groups and course of development. Therefore, we hypothesize that the mild and severe cognitive deficits observed in patients with type 2 diabetes reflect separate processes, possibly with different risk factors and aetiologies. Copyright

Microstructural White Matter Abnormalities and Cognitive Functioning in Type 2 Diabetes: A diffusion tensor imaging study

OBJECTIVE To examine whether type 2 diabetes is associated with microstructural abnormalities in specific cerebral white matter tracts and to relate these microstructural abnormalities to cognitive functioning. RESEARCH DESIGN AND METHODS Thirty-five nondemented older individuals with type 2 diabetes (mean age 71 ± 5 years) and 35 age-, sex-, and education-matched control subjects underwent a 3 Tesla diffusion-weighted MRI scan and a detailed cognitive assessment. Tractography was performed to reconstruct several white matter tracts. Diffusion tensor imaging measures, including fractional anisotropy (FA) and mean diffusivity (MD), were compared between groups and related to cognitive performance. RESULTS MD was significantly increased in all tracts in both hemispheres in patients compared with control subjects (P < 0.05), reflecting microstructural white matter abnormalities in the diabetes group. Increased MD was associated with slowing of information-processing speed and worse memory performance in the diabetes but not in the control group after adjustment for age, sex, and estimated IQ (group × MD interaction, all P < 0.05). These associations were independent of total white matter hyperintensity load and presence of cerebral infarcts. CONCLUSIONS Individuals with type 2 diabetes showed microstructural abnormalities in various white matter pathways. These abnormalities were related to worse cognitive functioning.

Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes

OBJECTIVE Type 2 diabetes is associated with a moderate degree of cerebral atrophy and a higher white matter hyperintensity (WMH) volume. How these brain-imaging abnormalities evolve over time is unknown. The present study aims to quantify cerebral atrophy and WMH progression over 4 years in type 2 diabetes. RESEARCH DESIGN AND METHODS A total of 55 patients with type 2 diabetes and 28 age-, sex-, and IQ-matched control participants had two 1.5T magnetic resonance imaging scans with a 4-year interval. Volumetric measurements of total brain, peripheral cerebrospinal fluid (CSF), lateral ventricles, and WMH were performed with k-nearest neighbor–based probabilistic segmentation. All volumes were expressed as percentage of intracranial volume. Linear regression analyses, adjusted for age and sex, were performed to compare brain volumes between the groups and to identify determinants of volumetric change within the type 2 diabetic group. RESULTS At baseline, patients with type 2 diabetes had a significantly smaller total brain volume and larger peripheral CSF volume than control participants. In both groups, all volumes showed a significant change over time. Patients with type 2 diabetes had a greater increase in lateral ventricular volume than control participants (mean adjusted between-group difference in change over time [95% CI]: 0.11% in 4 years [0.00 to 0.22], P = 0.047). CONCLUSIONS The greater increase in lateral ventricular volume over time in patients with type 2 diabetes compared with control participants shows that type 2 diabetes is associated with a slow increase of cerebral atrophy over the course of years.

Brain Changes Underlying Cognitive Dysfunction in Diabetes: What Can We Learn From MRI?

Diabetes is associated with cognitive dysfunction and an increased risk of dementia. This article addresses findings with brain MRI that may underlie cognitive dysfunction in diabetes. Studies in adults with type 1 diabetes show regional reductions in brain volume. In those with a diabetes onset in childhood, these volume reductions are likely to reflect the sum of changes that occur during brain development and changes that occur later in life due to exposure to diabetes-related factors. Type 2 diabetes is associated with global brain atrophy and an increased burden of small-vessel disease. These brain changes occur in the context of aging and often also in relation to an adverse vascular risk factor profile. Advanced imaging techniques detect microstructural lesions in the cerebral gray and white matter of patients with diabetes that affect structural and functional connectivity. Challenges are to further unravel the etiology of these cerebral complications by integrating findings from different imaging modalities and detailed clinical phenotyping and by linking structural MRI abnormalities to histology. A better understanding of the underlying mechanisms is necessary to establish interventions that will improve long-term cognitive outcomes for patients with type 1 and type 2 diabetes.

Disruption of the cerebral white matter network is related to slowing of information processing speed in patients with type 2 diabetes

Patients with type 2 diabetes often show slowing of information processing. Disruptions in the brain white matter network, possibly secondary to vascular damage, may underlie these cognitive disturbances. The current study reconstructed the white matter network of 55 nondemented individuals with type 2 diabetes (mean age, 71 ± 4 years) and 50 age-, sex-, and education-matched controls using diffusion magnetic resonance imaging–based fiber tractography. Graph theoretical analysis was then applied to quantify the efficiency of these networks. Patients with type 2 diabetes showed alterations in local and global network properties compared with controls (P < 0.05). These structural network abnormalities were related to slowing of information processing speed in patients. This relation was partly independent of cerebrovascular lesion load. This study shows that the approach of characterizing the brain as a network using diffusion magnetic resonance imaging and graph theory can provide new insights into how abnormalities in the white matter affect cognitive function in patients with diabetes.

Disruption of cerebral networks and cognitive impairment in Alzheimer disease.

Objective: To examine the relation between measures of whole-brain white matter connectivity and cognitive performance in patients with early Alzheimer disease (AD) using a network-based approach and to assess whether network parameters provide information that is complementary to conventional MRI markers of AD. Methods: Fifty patients (mean age 78.8 ± 7.1 years) with early AD were recruited via a memory clinic. In addition, 15 age-, sex-, and education-matched control participants were used as a reference group. All participants underwent a 3-T MRI scan and cognitive assessment. Diffusion tensor imaging–based tractography was used to reconstruct the brain network of each individual, followed by graph theoretical analyses. Overall network efficiency was assessed by measures of local (clustering coefficient, local efficiency) and global (path length, global efficiency) connectivity. Age-, sex-, and education-adjusted cognitive scores were related to network measures and to conventional MRI parameters (i.e., degree of cerebral atrophy and small-vessel disease). Results: The structural brain network of patients showed reduced local efficiency compared to controls. Within the patient group, worse performance in memory and executive functioning was related to decreased local efficiency (r = 0.434; p = 0.002), increased path length (r = −0.538; p < 0.001), and decreased global efficiency (r = 0.431; p = 0.005). Measures of network efficiency explained up to 27% of the variance in cognitive functioning on top of conventional MRI markers (p < 0.01). Conclusion: This study shows that network-based analysis of brain white matter connections provides a novel way to reveal the structural basis of cognitive dysfunction in AD.

Accelerated cognitive decline in patients with type 2 diabetes: MRI correlates and risk factors

Type 2 diabetes mellitus is associated with an increased risk of cognitive decline and dementia. We examined brain imaging correlates and vascular and metabolic risk factors of accelerated cognitive decline in patients with type 2 diabetes.

Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy

Cerebral amyloid angiopathy is a common form of small-vessel disease and an important risk factor for cognitive impairment. The mechanisms linking small-vessel disease to cognitive impairment are not well understood. We hypothesized that in patients with cerebral amyloid angiopathy, multiple small spatially distributed lesions affect cognition through disruption of brain connectivity. We therefore compared the structural brain network in patients with cerebral amyloid angiopathy to healthy control subjects and examined the relationship between markers of cerebral amyloid angiopathy-related brain injury, network efficiency, and potential clinical consequences. Structural brain networks were reconstructed from diffusion-weighted magnetic resonance imaging in 38 non-demented patients with probable cerebral amyloid angiopathy (69 ± 10 years) and 29 similar aged control participants. The efficiency of the brain network was characterized using graph theory and brain amyloid deposition was quantified by Pittsburgh compound B retention on positron emission tomography imaging. Global efficiency of the brain network was reduced in patients compared to controls (0.187 ± 0.018 and 0.201 ± 0.015, respectively, P < 0.001). Network disturbances were most pronounced in the occipital, parietal, and posterior temporal lobes. Among patients, lower global network efficiency was related to higher cortical amyloid load (r = -0.52; P = 0.004), and to magnetic resonance imaging markers of small-vessel disease including increased white matter hyperintensity volume (P < 0.001), lower total brain volume (P = 0.02), and number of microbleeds (trend P = 0.06). Lower global network efficiency was also related to worse performance on tests of processing speed (r = 0.58, P < 0.001), executive functioning (r = 0.54, P = 0.001), gait velocity (r = 0.41, P = 0.02), but not memory. Correlations with cognition were independent of age, sex, education level, and other magnetic resonance imaging markers of small-vessel disease. These findings suggest that reduced structural brain network efficiency might mediate the relationship between advanced cerebral amyloid angiopathy and neurologic dysfunction and that such large-scale brain network measures may represent useful outcome markers for tracking disease progression.

Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.

Diffusion tensor imaging (DTI) based fiber tractography (FT) is the most popular approach for investigating white matter tracts in vivo, despite its inability to reconstruct fiber pathways in regions with “crossing fibers.” Recently, constrained spherical deconvolution (CSD) has been developed to mitigate the adverse effects of “crossing fibers” on DTI based FT. Notwithstanding the methodological benefit, the clinical relevance of CSD based FT for the assessment of white matter abnormalities remains unclear. In this work, we evaluated the applicability of a hybrid framework, in which CSD based FT is combined with conventional DTI metrics to assess white matter abnormalities in 25 patients with early Alzheimer’s disease. Both CSD and DTI based FT were used to reconstruct two white matter tracts: one with regions of “crossing fibers,” i.e., the superior longitudinal fasciculus (SLF) and one which contains only one fiber orientation, i.e. the midsagittal section of the corpus callosum (CC). The DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD), obtained from these tracts were related to memory function. Our results show that in the tract with “crossing fibers” the relation between FA/MD and memory was stronger with CSD than with DTI based FT. By contrast, in the fiber bundle where one fiber population predominates, the relation between FA/MD and memory was comparable between both tractography methods. Importantly, these associations were most pronounced after adjustment for the planar diffusion coefficient, a measure reflecting the degree of fiber organization complexity. These findings indicate that compared to conventionally applied DTI based FT, CSD based FT combined with DTI metrics can increase the sensitivity to detect functionally significant white matter abnormalities in tracts with complex white matter architecture.

Heart failure and cognitive function in the general population: the Hoorn Study

To examine whether reduced cognitive functioning can be observed in early stages of left ventricular (LV) dysfunction and heart failure.