Background
The human default mode (DMN) is involved in a wide array of mental disorders. Current knowledge suggests that mental health disorders may reflect deviant trajectories of brain maturation.
...Method
We studied 654 children using functional magnetic resonance imaging (fMRI) scans under a resting‐state protocol. A machine‐learning method was used to obtain age predictions of children based on the average coefficient of fractional amplitude of low frequency fluctuations (fALFFs) of the DMN, a measure of spontaneous local activity. The chronological ages of the children and fALFF measures from regions of this network, the response and predictor variables were considered respectively in a Gaussian Process Regression. Subsequently, we computed a network maturation status index for each subject (actual age minus predicted). We then evaluated the association between this maturation index and psychopathology scores on the Child Behavior Checklist (CBCL).
Results
Our hypothesis was that the maturation status of the DMN would be negatively associated with psychopathology. Consistent with previous studies, fALFF significantly predicted the age of participants (p < .001). Furthermore, as expected, we found an association between the DMN maturation status (precocious vs. delayed) and general psychopathology scores (p = .011).
Conclusions
Our findings suggest that child psychopathology seems to be associated with delayed maturation of the DMN. This delay in the neurodevelopmental trajectory may offer interesting insights into the pathophysiology of mental health disorders.
ABSTRACT
Recent advances in technology have allowed us access to a multitude of datasets pertaining to various dimensions in neurology. Together with the enormous opportunities, we also face ...challenges related to data quality, ethics and intrinsic difficulties related to the application of data science in healthcare. In this article we will describe the main advances in the field of artificial intelligence and Big Data applied to neurology with a focus on neurosciences based on medical images. Real-World Data (RWD) and analytics related to large volumes of information will be described as well as some of the most relevant scientific initiatives at the time of this writing.
Recent evidence suggests that gains in performance observed while humans learn a novel motor sequence occur during the quiet rest periods interleaved with practice (micro-offline gains, MOGs). This ...phenomenon is reminiscent of memory replay observed in the hippocampus during spatial learning in rodents. Whether the hippocampus is also involved in the production of MOGs remains currently unknown. Using a multimodal approach in humans, here we show that activity in the hippocampus and the precuneus increases during the quiet rest periods and predicts the level of MOGs before asymptotic performance is achieved. These functional changes were followed by rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that reactivates during the quiet periods of training undergoes structural plasticity. Our work points to the involvement of the hippocampal system in the reactivation of procedural memories
Abnormal tau inclusions are hallmarks of Alzheimer's disease and predictors of clinical decline. Several tau PET tracers are available for neurodegenerative disease research, opening avenues for ...molecular diagnosis in vivo. However, few have been approved for clinical use. Understanding the neurobiological basis of PET signal validation remains problematic because it requires a large-scale, voxel-to-voxel correlation between PET and (immuno) histological signals. Large dimensionality of whole human brains, tissue deformation impacting co-registration, and computing requirements to process terabytes of information preclude proper validation. We developed a computational pipeline to identify and segment particles of interest in billion-pixel digital pathology images to generate quantitative, 3D density maps. The proposed convolutional neural network for immunohistochemistry samples, IHCNet, is at the pipeline's core. We have successfully processed and immunostained over 500 slides from two whole human brains with three phospho-tau antibodies (AT100, AT8, and MC1), spanning several terabytes of images. Our artificial neural network estimated tau inclusion from brain images, which performs with ROC AUC of 0.87, 0.85, and 0.91 for AT100, AT8, and MC1, respectively. Introspection studies further assessed the ability of our trained model to learn tau-related features. We present an end-to-end pipeline to create terabytes-large 3D tau inclusion density maps co-registered to MRI as a means to facilitate validation of PET tracers.
There is a wide range of functional magnetic resonance imaging (fMRI) study designs available for the neuroscientist who wants to investigate cognition. In this manuscript we review some aspects of ...fMRI study design, including cognitive comparison strategies (factorial, parametric designs), and stimulus presentation possibilities (block, event-related, rapid event-related, mixed, and self-driven experiment designs) along with technical aspects, such as limitations of signal to noise ratio, spatial, and temporal resolution. We also discuss methods to deal with cases where scanning parameters become the limiting factor (parallel acquisitions, variable jittered designs, scanner acoustic noise strategies).
Degeneration of basal forebrain (BF) cholinergic nuclei is associated with cognitive decline, and this effect is believed to be mediated by neuronal dysfunction in the denervated cortical areas. ...MRI-based measurements of BF atrophy are increasingly being used as in vivo surrogate markers for cholinergic degeneration, but the functional implications of reductions in BF volume are not well understood. We used high-resolution MRI, fluorodeoxyglucose-positron emission tomography (PET), and neuropsychological test data of 132 subjects with mild cognitive impairment (MCI) and 177 cognitively normal controls to determine associations between BF atrophy, cortical hypometabolism, and cognitive deficits. BF atrophy in MCI correlated with both impaired memory function and attentional control deficits, whereas hippocampus volume was more specifically associated with memory deficits. BF atrophy was also associated with widespread cortical hypometabolism, and path analytic models indicated that hypometabolism in domain-specific cortical networks mediated the association between BF volume and cognitive dysfunction. The presence of cortical amyloid pathology, as assessed using AV45-PET, did not significantly interact with the observed associations. These data underline the potential of multimodal imaging markers to study structure-function-cognition relationships in the living human brain and provide important in vivo evidence for an involvement of the human BF in cortical activity and cognitive function.
Aging is associated with decreased resting-state functional connectivity (RSFC) within the default mode network (DMN), but most functional imaging studies have restricted the analysis to specific ...brain regions or networks, a strategy not appropriate to describe system-wide changes. Moreover, few investigations have employed operational psychiatric interviewing procedures to select participants; this is an important limitation since mental disorders are prevalent and underdiagnosed and can be associated with RSFC abnormalities. In this study, resting-state fMRI was acquired from 59 adults free of cognitive and psychiatric disorders according to standardized criteria and based on extensive neuropsychological and clinical assessments. We tested for associations between age and whole-brain RSFC using Partial Least Squares, a multivariate technique. We found that normal aging is not only characterized by decreased RSFC within the DMN but also by ubiquitous increases in internetwork positive correlations and focal internetwork losses of anticorrelations (involving mainly connections between the DMN and the attentional networks). Our results reinforce the notion that the aging brain undergoes a dedifferentiation processes with loss of functional diversity. These findings advance the characterization of healthy aging effects on RSFC and highlight the importance of adopting a broad, system-wide perspective to analyze brain connectivity.
The individual's socioeconomic conditions are the most relevant to predict the quality of someone's health. However, such information is not usually found in medical records, making studies in the ...area difficult. Therefore, it is common to use composite indices that characterize a region socioeconomically, such as the Human Development Index (HDI). The main advantage of the HDI is its understanding and adoption on a global scale. However, its applicability is limited for health studies since its longevity dimension presents mathematical redundancy in regression models. Here we introduce the GeoSES, a composite index that summarizes the main dimensions of the Brazilian socioeconomic context for research purposes. We created the index from the 2010 Brazilian Census, whose variables selection was guided by theoretical references for health studies. The proposed index incorporates seven socioeconomic dimensions: education, mobility, poverty, wealth, income, segregation, and deprivation of resources and services. We developed the GeoSES using Principal Component Analysis and evaluated its construct, content, and applicability. GeoSES is defined at three scales: national (GeoSES-BR), Federative Unit (GeoSES-FU), and intra-municipal (GeoSES-IM). GeoSES-BR dimensions showed a good association with HDI-M (correlation above 0.85). The model with the poverty dimension best explained the relative risk of avoidable cause mortality in Brazil. In the intra-municipal scale, the model with GeoSES-IM was the one that best explained the relative risk of mortality from circulatory system diseases. By applying spatial regressions, we demonstrated that GeoSES shows significant explanatory potential in the studied scales, being a compelling complement for future researches in public health.
Histopathological studies in Alzheimer's disease (AD) suggest severe and region-specific neurodegeneration of the basal forebrain cholinergic system (BFCS). Here, we studied the between-center ...reliability and diagnostic accuracy of MRI-based BFCS volumetry in a large multicenter data set, including participants with prodromal (n = 41) or clinically manifest AD (n = 134) and 148 cognitively healthy controls. Atrophy was determined using voxel-based and region-of-interest based analyses of high-dimensionally normalized MRI scans using a newly created map of the BFCS based on postmortem in cranio MRI and histology. The AD group showed significant volume reductions of all subregions of the BFCS, which were most pronounced in the posterior nucleus basalis Meynert (NbM). The mild cognitive impairment-AD group showed pronounced volume reductions in the posterior NbM, but preserved volumes of anterior-medial regions. Diagnostic accuracy of posterior NbM volume was superior to hippocampus volume in both groups, despite higher multicenter variability of the BFCS measurements. The data of our study suggest that BFCS morphometry may provide an emerging biomarker in AD.
Abstract We compared accuracy of hippocampus and basal forebrain cholinergic system (BFCS) atrophy to predict cortical amyloid burden in 179 cognitively normal subjects (CN), 269 subjects with early ...stages of mild cognitive impairment (MCI), 136 subjects with late stages of MCI, and 86 subjects with Alzheimer's disease (AD) dementia retrieved from the Alzheimer's Disease Neuroimaging Initiative database. Hippocampus and BFCS volumes were determined from structural magnetic resonance imaging scans at 3 Tesla, and cortical amyloid load from AV45 (florbetapir) positron emission tomography scans. In receiver operating characteristics analyses, BFCS volume provided significantly more accurate classification into amyloid-negative and -positive categories than hippocampus volume. In contrast, hippocampus volume more accurately identified the diagnostic categories of AD, late and early MCI, and CN compared with whole and anterior BFCS volume, whereas posterior BFCS and hippocampus volumes yielded similar diagnostic accuracy. In logistic regression analysis, hippocampus and posterior BFCS volumes contributed significantly to discriminate MCI and AD from CN, but only BFCS volume predicted amyloid status. Our findings suggest that BFCS atrophy is more closely associated with cortical amyloid burden than hippocampus atrophy in predementia AD.
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