Alzheimer disease (AD) is the most common cause of dementia, with a worldwide prevalence of about 25 million in 2010, expected to be doubled by 2030 because of increased life expectancy.¹ The earliest recognizable pathological event in AD is cerebral amyloid-β aggregation.² This pathology may be present up to 20 years before the onset of dementia.^3,4 Novel research criteria for AD in individuals without dementia emphasize the presence of amyloid pathology to define the first stage of the disease.^5,6

The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were “small world.” The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.

See Chard and Miller (doi: 10.1093/brain/awv354 ) for a scientific commentary on this article . Grey matter atrophy is common in multiple sclerosis. However, in contrast with other neurodegenerative diseases, it is unclear whether grey matter atrophy in multiple sclerosis is a diffuse ‘global’ process or develops, instead, according to distinct anatomical patterns. Using source-based morphometry we searched for anatomical patterns of co-varying cortical thickness and assessed their relationships with white matter pathology, physical disability and cognitive functioning. Magnetic resonance imaging was performed at 3 T in 208 patients with long-standing multiple sclerosis (141 females; age = 53.7 ± 9.6 years; disease duration = 20.2 ± 7.1 years) and 60 age- and sex-matched healthy controls. Spatial independent component analysis was performed on cortical thickness maps derived from 3D T ₁ -weighted images across all subjects to identify co-varying patterns. The loadings, which reflect the presence of each cortical thickness pattern in a subject, were compared between patients with multiple sclerosis and healthy controls with generalized linear models. Stepwise linear regression analyses were used to assess whether white matter pathology was associated with these loadings and to identify the cortical thickness patterns that predict measures of physical and cognitive dysfunction. Ten cortical thickness patterns were identified, of which six had significantly lower loadings in patients with multiple sclerosis than in controls: the largest loading differences corresponded to the pattern predominantly involving the bilateral temporal pole and entorhinal cortex, and the pattern involving the bilateral posterior cingulate cortex. In patients with multiple sclerosis, overall white matter lesion load was negatively associated with the loadings of these two patterns. The final model for physical dysfunction as measured with Expanded Disability Status Scale score (adjusted R ² = 0.297; P < 0.001) included the predictors age, overall white matter lesion load, the loadings of two cortical thickness patterns (bilateral sensorimotor cortex and bilateral insula), and global cortical thickness. The final model predicting average cognition (adjusted R ² = 0.469; P < 0.001) consisted of age, the loadings of two cortical thickness patterns (bilateral posterior cingulate cortex and bilateral temporal pole), overall white matter lesion load and normal-appearing white matter integrity. Although white matter pathology measures were part of the final clinical regression models, they explained limited incremental variance (to a maximum of 4%). Several cortical atrophy patterns relevant for multiple sclerosis were found. This suggests that cortical atrophy in multiple sclerosis occurs largely in a non-random manner and develops (at least partly) according to distinct anatomical patterns. In addition, these cortical atrophy patterns showed stronger associations with clinical (especially cognitive) dysfunction than global cortical atrophy.

Alzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and controls, or Alzheimer's disease with one other type of dementia. Such a bilateral comparison does not resemble clinical practice, where a clinician is faced with a number of different possible types of dementia. Here we studied which features in structural magnetic resonance imaging (MRI) scans could best distinguish four types of dementia, Alzheimer's disease, frontotemporal dementia, vascular dementia, and dementia with Lewy bodies, and control subjects. We extracted an extensive set of features quantifying volumetric and morphometric characteristics from T1 images, and vascular characteristics from FLAIR images. Classification was performed using a multi-class classifier based on Disease State Index methodology. The classifier provided continuous probability indices for each disease to support clinical decision making. A dataset of 504 individuals was used for evaluation. The cross-validated classification accuracy was 70.6% and balanced accuracy was 69.1% for the five disease groups using only automatically determined MRI features. Vascular dementia patients could be detected with high sensitivity (96%) using features from FLAIR images. Controls (sensitivity 82%) and Alzheimer's disease patients (sensitivity 74%) could be accurately classified using T1-based features, whereas the most difficult group was the dementia with Lewy bodies (sensitivity 32%). These results were notable better than the classification accuracies obtained with visual MRI ratings (accuracy 44.6%, balanced accuracy 51.6%). Different quantification methods provided complementary information, and consequently, the best results were obtained by utilizing several quantification methods. The results prove that automatic quantification methods and computerized decision support methods are feasible for clinical practice and provide comprehensive information that may help clinicians in the diagnosis making.