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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It is one of the leading sources of morbidity and mortality in the aging population AD cardinal symptoms include memory and executive function impairment that profoundly alters a patient’s ability to perform activities of daily living. People with mild cognitive impairment (MCI) exhibit many of the early clinical symptoms of patients with AD and have a high chance of converting to AD in their lifetime. Diagnostic criteria rely on clinical assessment and brain magnetic resonance imaging (MRI). Many groups are working to help automate this process to improve the clinical workflow. Current computational approaches are focused on predicting whether or not a subject with MCI will convert to AD in the future. To our knowledge, limited attention has been given to the development of automated computer-assisted diagnosis (CAD) systems able to provide an AD conversion diagnosis in MCI patient cohorts followed longitudinally. This is important as these CAD systems could be used by primary care providers to monitor patients with MCI. The method outlined in this paper addresses this gap and presents a computationally efficient pre-processing and prediction pipeline, and is designed for recognizing patterns associated with AD conversion. We propose a new approach that leverages longitudinal data that can be easily acquired in a clinical setting (e.g., T1-weighted magnetic resonance images, cognitive tests, and demographic information) to identify the AD conversion point in MCI subjects with AUC = 84.7. In contrast, cognitive tests and demographics alone achieved AUC = 80.6, a statistically significant difference (n = 669, p < 0.05). We designed a convolutional neural network that is computationally efficient and requires only linear registration between imaging time points. The model architecture combines Attention and Inception architectures while utilizing both cross-sectional and longitudinal imaging and clinical information. Additionally, the top brain regions and clinical features that drove the model’s decision were investigated. These included the thalamus, caudate, planum temporale, and the Rey Auditory Verbal Learning Test. We believe our method could be easily translated into the healthcare setting as an objective AD diagnostic tool for patients with MCI.

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Background: The effectiveness of hypnosis in reducing pain is well supported by the scientific literature. Hypnosis typically involves verbal suggestions but the mechanisms by which verbal contents are transformed into predictive signals to modulate perceptual processes remain unclear. We hypothesized that brain activity during verbal suggestions would predict the modulation of responses to acute nociceptive stimuli.Methods: Brain activity was measured using BOLD-fMRI in healthy participants while they listened to verbal suggestions of HYPERALGESIA, HYPOALGESIA, or NORMAL sensation (control) following a standardized hypnosis induction. Immediately after the suggestions, series of noxious electrical stimuli were administered to assess pain-related responses. Brain responses measured during the suggestions were then used to predict changes in pain-related responses using delayed regression analyses.Results: Listening to suggestions of HYPERALGESIA and HYPOALGESIA produced BOLD decreases (vs. control) in the parietal operculum (PO) and in the anterior midcingulate cortex (aMCC), and increases in the left parahippocampal gyrus (lPHG). Changes in activity in PO, aMCC and PHG during the suggestions predicted larger pain-evoked responses following the HYPERALGESIA suggestions in the anterior cingulate cortex (ACC) and the anterior insula (aINS), and smaller pain-evoked responses following the HYPOALGESIA suggestions in the ACC, aMCC, posterior insula (pINS) and thalamus. These changes in pain-evoked brain responses are consistent with the changes in pain perception reported by the participants in HYPERALGESIA and HYPOALGESIA, respectively.Conclusions: The fronto-parietal network (supracallosal ACC and PO) has been associated with self-regulation and perceived self-agency. Deactivation of these regions during suggestions is predictive of the modulation of brain responses to noxious stimuli in areas previously associated with pain perception and pain modulation. The response of the hippocampal complex may reflect its role in contextual learning, memory and pain anticipation/expectations induced by verbal suggestions of pain modulation. This study provides a basis to further explore the transformation of verbal suggestions into perceptual modulatory processes fundamental to hypnosis neurophenomenology. These findings are discussed in relation to predictive coding models.

Progranulin deficiency due to heterozygous null mutations in the GRN gene is a common cause of familial frontotemporal lobar degeneration (FTLD), while homozygous loss-of-function GRN mutations cause neuronal ceroid lipofuscinosis (NCL). Aged progranulin-knockout mice display highly exaggerated lipofuscinosis, microgliosis, and astrogliosis, as well as mild cell loss in specific brain regions. Progranulin is a secreted glycoprotein expressed in both neurons and microglia, but not astrocytes, in the brain. We generated conditional progranulin-knockout mice that lack progranulin in nestin-expressing cells (Nes-cKO mice), which include most neurons as well as astrocytes. We confirmed near complete knockout of progranulin in neurons in Nes-cKO mice, while microglial progranulin levels remained similar to that of wild-type animals. Overall brain progranulin levels were reduced by about 50% in Nes-cKO, and no Grn was detected in primary Nes-cKO neurons. Nes-cKO mice aged to 12 months did not display any increase in lipofuscin deposition, microgliosis, or astrogliosis in the four brain regions examined, though increases were observed for most of these measures in Grn-null animals. We conclude that neuron-specific loss of progranulin is not sufficient to cause similar neuropathological changes to those seen in constitutive Grn-null animals. Our results suggest that increased lipofuscinosis and gliosis in Grn-null animals are not caused by intrinsic progranulin deficiency in neurons, and that microglia-derived progranulin may be sufficient to maintain neuronal health and homeostasis in the brain.

Myotonic dystrophy type 1 (DM1) is a multisystemic and heterogeneous disorder caused by the expansion of CTG repeats in the 3’ UTR of the myotonic dystrophy protein kinase (DMPK) gene. There is a congenital form (CDM1) of the disease characterized by severe hypotonia, respiratory insufficiency as well as developmental delays and intellectual disabilities. CDM1 infants manifest important brain structure abnormalities present from birth while, in contrast, older patients with adult-onset DM1 often present neurodegenerative features and milder progressive cognitive deficits. Promising therapies targeting central molecular mechanisms contributing to the symptoms of adult-onset DM1 are currently in development, but their relevance for treating cognitive impairment in CDM1, which seems to be a partially distinct neurodevelopmental disorder, remain to be elucidated. Here, we provide an update on the clinical presentation of CDM1 and review recent in vitro and in vivo models that have provided meaningful insights on its consequences in development, with a particular focus on the brain. We discuss how enhanced toxic gain-of-function of the mutated DMPK transcripts with larger CUG repeats and the resulting dysregulation of RNA-binding proteins may affect the developing cortex in utero. Because the methylation of CpG islets flanking the trinucleotide repeats has emerged as a strong biomarker of CDM1, we highlight the need to investigate the tissue-specific impacts of these chromatin modifications in the brain. Finally, we outline promising potential therapeutic treatments for CDM1 and propose future in vitro and in vivo models with great potential to shed light on this disease.

Domestication is the process by which wild organisms become adapted for human use. Many phenotypic changes are associated with animal domestication, including decreases in brain and brain region sizes. In contrast with this general pattern, the chicken has a larger cerebellum compared with the wild red junglefowl, but what neuroanatomical changes are responsible for this difference have yet to be investigated. Here, we quantified cell layer volumes, neuron numbers and neuron sizes in the cerebella of chickens and junglefowl. Chickens have larger, more folded cerebella with more and larger granule cells than junglefowl, but neuron numbers and cerebellar folding were proportional to cerebellum size. However, chickens do have relatively larger granule cell layer volumes and relatively larger granule cells than junglefowl. Thus, the chicken cerebellum can be considered a scaled-up version of the junglefowl cerebellum, but with enlarged granule cells. The combination of scaling neuron number and disproportionate enlargement of cell bodies partially supports a recent theory that domestication does not affect neuronal density within brain regions. Whether the neuroanatomical changes we observed are typical of domestication or not requires similar quantitative analyses in other domesticated species and across multiple brain regions. Funding Agencies|European Research CouncilEuropean Research Council (ERC)European Commission [FERALGEN 772874]; Canada Research Chairs ProgramCanada Research Chairs; Canada Foundation for InnovationCanada Foundation for InnovationCGIAR; Natural Sciences and Engineering Research Council of Canada (NSERC)Natural Sciences and Engineering Research Council of Canada (NSERC); NSERC Undergraduate Student Research AwardNatural Sciences and Engineering Research Council of Canada (NSERC)

AbstractWe previously identified and modelled a principle of visual maps alignment in the midbrain involving the mapping of the retinal projections and concurrent transposition of retinal guidance cues into the superior colliculus providing positional information for the organization of cortical V1 projections onto the retinal map (Savier et al., 2017). This principle relies on mechanisms involving Epha/Efna signaling, correlated neuronal activity and axon competition. Here, using the 3-step map alignment computational model, we predict and validate in vivo the visual mapping defects in a well-characterized mouse model. Our results challenge previous hypotheses and provide an alternative, although complementary, explanation for the phenotype observed. In addition, we propose a new quantification method to assess the degree of alignment and organization between maps, allowing inter-model comparisons. This work generalizes the validity and robustness of the 3-step map alignment algorithm as a predictive tool and confirms the basic mechanisms of visual maps organization.