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Small subclinical hyperintense lesions are frequently encountered on brain diffusion-weighted imaging (DWI) scans of patients with cerebral amyloid angiopathy (CAA). Interpretation of these DWI+ lesions, however, has been limited by absence of histopathological examination. We aimed to determine whether DWI+ lesions represent acute microinfarcts on histopathology in brains with advanced CAA, using a combined in vivo MRI—ex vivo MRI—histopathology approach. We first investigated the histopathology of a punctate cortical DWI+ lesion observed on clinical in vivo MRI 7 days prior to death in a CAA case. Subsequently, we assessed the use of ex vivo DWI to identify similar punctate cortical lesions post-mortem. Intact formalin-fixed hemispheres of 12 consecutive cases with CAA and three non-CAA controls were subjected to high-resolution 3 T ex vivo DWI and T2 imaging. Small cortical lesions were classified as either DWI+/T2+ or DWI−/T2+. A representative subset of lesions from three CAA cases was selected for detailed histopathological examination. The DWI+ lesion observed on in vivo MRI could be matched to an area with evidence of recent ischemia on histopathology. Ex vivo MRI of the intact hemispheres revealed a total of 130 DWI+/T2+ lesions in 10/12 CAA cases, but none in controls ( p  = 0.022). DWI+/T2+ lesions examined histopathologically proved to be acute microinfarcts (classification accuracy 100%), characterized by presence of eosinophilic neurons on hematoxylin and eosin and absence of reactive astrocytes on glial fibrillary acidic protein-stained sections. In conclusion, we suggest that small DWI+ lesions in CAA represent acute microinfarcts. Furthermore, our findings support the use of ex vivo DWI as a method to detect acute microinfarcts post-mortem, which may benefit future histopathological investigations on the etiology of microinfarcts.

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.

Introduction Thresholding of low‐weight connections of diffusion MRI‐based brain networks has been proposed to remove false‐positive connections. It has been previously established that this yields more reproducible scan–rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter‐individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications. Methods We applied fixed‐density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow‐up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan–rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge‐weights and hub‐scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA. Results Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70–.78; thresholded: dice = .77; ICC: .73–.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed‐density thresholds that were optimal in terms of consistency (densities: .10–.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation. Conclusions Our results indicate that thresholding of low‐weight connections, particularly when using fixed‐density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects. In this study, we investigated whether thresholding methods that have been shown to improve scan–rescan reproducibility of diffusion‐based brain networks in healthy young subjects are also applicable to datasets of elderly patients with brain pathology, scanned over much longer time frames. We examined whether thresholding achieves improved consistency in network architecture, while maintaining sensitivity to biological effects in patients with cerebral small vessel disease.

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.

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. 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. 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. Individuals with type 2 diabetes showed microstructural abnormalities in various white matter pathways. These abnormalities were related to worse cognitive functioning.

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like “How do dynamic processes alter the underlying structural network?” and “Can we use network neuroscience for disease classification?” This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

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.

Impaired recognition of emotion after stroke can have important implications for social competency, social participation, and consequently quality of life. We describe a case of left hemispheric ischemic stroke with impaired recognition of specifically faces expressing fear. Three months later, the patient’s spouse reports that the patient was irritable and slow in communication, which may be caused by the impaired emotion recognition. The case is discussed in relation to the literature concerning emotion recognition and its neural correlates. Our case supports the notion that emotion recognition, including fear recognition, is regulated by a network of interconnected brain regions located in both hemispheres. We conclude that impaired emotion recognition is not uncommon after stroke and can be caused by dysfunction of this emotion-network.

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and brain abnormalities on MRI. The underlying mechanisms are unclear. We examined the relationship between cerebral haemodynamics (cerebral blood flow (CBF) and cerebrovascular reactivity (CVR)) and cognitive performance and brain volumes in patients with T2DM, at baseline and after four years. 114 patients with T2DM, aged 56–80 years, underwent a detailed cognitive assessment and MRI scan. In 68 patients the evaluation was repeated after four years. CBF (two-dimensional flow-encoded phase-contrast MRI) and CVR (carbogen breathing response middle cerebral artery; transcranial Doppler) were measured at baseline. Cognitive performance was expressed as composite z-score and regression based index score. Brain volumes were measured on MRI by automated segmentation. The relationship of haemodynamics with cognition and brain volumes was examined with linear regression analyses adjusted for age, sex and IQ. Mean CVR was 51.8%±18.0% and mean rCBF 53.3±11.3 ml/min/100 ml brain tissue. CBF was associated with baseline cognitive performance (standardized regression coefficient β (95% CI): 0.17 (0.00; 0.32) and total brain volume (0.23 (0.05; 0.41)). No correlation was found between CVR and baseline cognitive performance. Neither CBF nor CVR predicted change in cognition (CBF 0.11 (−0.21; 0.44); CVR 0.07 (−0.21; 0.36)) or total brain volume (CBF 0.09 (−0.22; 0.39); CVR 0.13 (−0.13; 0.40)) over four years. CBF was associated with impaired cognition and total brain volume in cross-sectional analyses, but did not predict changes in cognition or brain volumes over time. Apparently, alterations in cerebral haemodynamics play no major etiological role in cognitive decline or change in brain volumes in non-demented individuals with T2DM.

Background/Aims: The present study examined the relationship between microvascular complications and cognitive decline and the development of structural brain abnormalities over a period of 4 years in patients with type 2 diabetes mellitus (T2DM). Methods: Sixty-eight elderly patients with T2DM had 2 cognitive assessments with a 4-year interval. Two MRI scans, performed at the same time as the cognitive assessments, were available from 55 patients. Changes in cognitive performance over time were expressed as a regression-based index (RBI). Automated volumetric measurements of total brain, lateral ventricles and white matter hyperintensities were performed. The relationship between baseline microvascular complications [diabetic retinopathy, peripheral neuropathy or albuminuria (micro- or macroalbuminuria)] and cognition and brain volumes was examined with linear regression analyses adjusted for age and sex (for cognition also for IQ). Results: At baseline, diabetic retinopathy was present in 18% of patients, peripheral neuropathy in 36%, albuminuria in 15%. Retinopathy or neuropathy were not significantly associated with baseline cognition or brain volumes, or changes in these measures over time. Albuminuria was associated with a lower composite RBI score, indicating accelerated cognitive decline (adjusted mean difference between patients with or without albuminuria: –0.58, 95% CI –0.85 to –0.31, p < 0.001). Conclusion: Albuminuria predicted accelerated cognitive decline in patients with T2DM, but other microvascular complications were unrelated to accelerated cognitive decline or brain MRI abnormalities.