Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data.

We performed a resting-state functional connectivity study to investigate directly the functional correlations within the perisylvian language networks by seeding from 3 subregions of Broca's complex (pars opercularis, pars triangularis, and pars orbitalis) and their right hemisphere homologues. A clear topographical functional connectivity pattern in the left middle frontal, parietal, and temporal areas was revealed for the 3 left seeds. This is the first demonstration that a functional connectivity topology can be observed in the perisylvian language networks. The results support the assumption of the functional division for phonology, syntax, and semantics of Broca's complex as proposed by the memory, unification, and control (MUC) model and indicated a topographical functional organization in the perisylvian language networks, which suggests a possible division of labor for phonological, syntactic, and semantic function in the left frontal, parietal, and temporal areas.

Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical step toward automating processes such as harvesting. Deep learning based methods have emerged as the state-of-the-art techniques in many problems in image segmentation and classification, and have a lot of promise in challenging domains such as agriculture, where they can deal with the large variability in data better than classical computer vision methods. This paper reports results on the detection of tomatoes in images taken in a greenhouse, using the MaskRCNN algorithm, which detects objects and also the pixels corresponding to each object. Our experimental results on the detection of tomatoes from images taken in greenhouses using a RealSense camera are comparable to or better than the metrics reported by earlier work, even though those were obtained in laboratory conditions or using higher resolution images. Our results also show that MaskRCNN can implicitly learn object depth, which is necessary for background elimination.

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Humans differ widely in their navigational abilities. Studies have shown that self-reports on navigational abilities are good predictors of performance on navigation tasks in real and virtual environments. The caudate nucleus and medial temporal lobe regions have been suggested to subserve different navigational strategies. The ability to use different strategies might underlie navigational ability differences. This study examines the anatomical correlates of self-reported navigational ability in both gray and white matter. Local gray matter volume was compared between a group (N = 134) of good and bad navigators using voxel-based morphometry (VBM), as well as regional volumes. To compare between good and bad navigators, we also measured white matter anatomy using diffusion tensor imaging (DTI) and looked at fractional anisotropy (FA) values. We observed a trend toward higher local GM volume in right anterior parahippocampal/rhinal cortex for good versus bad navigators. Good male navigators showed significantly higher local GM volume in right hippocampus than bad male navigators. Conversely, bad navigators showed increased FA values in the internal capsule, the white matter bundle closest to the caudate nucleus and a trend toward higher local GM volume in the caudate nucleus. Furthermore, caudate nucleus regional volume correlated negatively with navigational ability. These convergent findings across imaging modalities are in line with findings showing that the caudate nucleus and the medial temporal lobes are involved in different wayfinding strategies. Our study is the first to show a link between self-reported large-scale navigational abilities and different measures of brain anatomy.

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Heschl's gyrus (HG) is a core region of the auditory cortex whose morphology is highly variable across individuals. This variability has been linked to sound perception ability in both speech and music domains. Previous studies show that variations in morphological features of HG, such as cortical surface area and thickness, are heritable. To identify genetic variants that affect HG morphology, we conducted a genome-wide association scan (GWAS) meta-analysis in 3054 healthy individuals using HG surface area and thickness as quantitative traits. None of the single nucleotide polymorphisms (SNPs) showed association P values that would survive correction for multiple testing over the genome. The most significant association was found between right HG area and SNP rs72932726 close to gene DCBLD2 (3q12.1; P = 2.77 × 10 −7 ). This SNP was also associated with other regions involved in speech processing. The SNP rs333332 within gene KALRN (3q21.2; P = 2.27 × 10 −6 ) and rs143000161 near gene COBLL1 (2q24.3; P = 2.40 × 10 −6 ) were associated with the area and thickness of left HG, respectively. Both genes are involved in the development of the nervous system. The SNP rs7062395 close to the X-linked deafness gene POU3F4 was associated with right HG thickness (Xq21.1; P = 2.38 × 10 −6 ). This is the first molecular genetic analysis of variability in HG morphology.

This study aims to identify the minimum requirements for an accurate model of the diffusion MR signal in white matter of the brain. We construct a hierarchy of two-compartment models of white matter from combinations of simple models for the intra and extracellular spaces. We devise a new diffusion MRI protocol that provides measurements with a wide range of parameters for diffusion sensitization both parallel and perpendicular to white matter fibres. We use the protocol to acquire data from a fixed rat brain, which allows us to fit, study and compare the different models. The results show that models which incorporate pore size describe the measurements most accurately. The best fit comes from combining a full diffusion tensor (DT) model of the extra-cellular space with a cylindrical intra-cellular component.