Graph theoretical analysis has become an important tool in the examination of brain dysconnectivity in neurological and psychiatric brain disorders. A common analysis step in the construction of the functional graph or network involves "thresholding" of the connectivity matrix, selecting the set of edges that together form the graph on which network organization is evaluated. To avoid systematic differences in absolute number of edges, studies have argued against the use of an "absolute threshold" in case-control studies and have proposed the use of "proportional thresholding" instead, in which a pre-defined number of strongest connections are selected as network edges, ensuring equal network density across datasets. Here, we systematically studied the effect of proportional thresholding on the construction of functional matrices and subsequent graph analysis in patient-control functional connectome studies. In a few simple experiments we show that differences in overall strength of functional connectivity (FC) - as often observed between patients and controls - can have predictable consequences for between-group differences in network organization. In individual networks with lower overall FC the proportional thresholding algorithm has to select more edges based on lower correlations, which have (on average) a higher probability of being spurious, and thus introduces a higher degree of randomness in the resulting network. We show across both empirical and artificial patient-control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences in network organization. Based on the presented case-control findings we inform about the caveats of proportional thresholding in patient-control studies in which groups show a between-group difference in overall FC. We make recommendations on how to examine, report and to take into account overall FC effects in future patient-control functional connectome studies.

Macroscale connectivity of the mammalian brain has been shown to display several characteristics of an efficient communication network architecture. In parallel, at the microscopic scale, histological studies have extensively revealed large interregional variation in cortical neural architectonics. However, how these two "scales" of cerebrum organization are linked remains an open question. Collating and combining data across multiple studies on the cortical cytoarchitecture of the macaque cortex with information on macroscale anatomical wiring derived from tract tracing studies, this study focuses on examining the interplay between macroscale organization of the macaque connectome and microscale cortical neuronal architecture. Our findings show that both macroscale degree as well as the topological role in the overall network are related to the level of neuronal complexity of cortical regions at the microscale, showing (among several effects) a positive overall association between macroscale degree and metrics of microscale pyramidal complexity. Macroscale hub regions, together forming a densely interconnected "rich club," are noted to display a high level of neuronal complexity, findings supportive of a high level of integrative neuronal processes to occur in these regions. Together, we report on cross-scale observations that jointly suggest that a region's microscale neuronal architecture is tuned to its role in the global brain network.

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Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.