REMI: Reconstructing Episodic Memory During Intrinsic Path Planning.

Grid cells in the medial entorhinal cortex (MEC) are believed to path integrate speed and direction signals to activate at triangular grids of locations in an environment, thus implementing a population code for position. In parallel, place cells in the hippocampus (HC) fire at spatially confined locations, with selectivity tuned not only to allocentric position but also to environmental contexts, such as sensory cues. Although grid and place cells both encode spatial information and support memory for multiple locations, why animals maintain two such representations remains unclear.

Deep implicit optimization enables robust learnable features for deformable image registration.

Deep Learning in Image Registration (DLIR) methods have been tremendously successful in image registration due to their speed and ability to incorporate weak label supervision at training time. However, existing DLIR methods forego many of the benefits and invariances of optimization methods. The lack of a task-specific inductive bias in DLIR methods leads to suboptimal performance, especially in the presence of domain shift.

Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience.

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols.

Pre and post-operative serum levels of titanium cobalt and aluminium after implant placement.

The preoperative serum levels and postoperative serum levels of titanium, cobalt and aluminium from dental implants in order to assess the release of these ions and to assess any risk of toxicity from these ions after dental implant placement is of interest to dentists. It was observed that there was very slight increase in serum concentration of titanium, cobalt and aluminium after 12 months of placement of implants as compared to before placement of implants. However the increase was non-significant statistically.

Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samples.

Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data are necessary for model building and optimization. To help overcome such limitations in the context of brain MRI, we present GenMIND: a collection of generative models of normative regional volumetric features derived from structural brain imaging.

Investigating Causal Genetic Effects on Overall Survival of Glioblastoma Patients using Normalizing Flow and Structural Causal Model.

Glioblastoma (GBM) is the most common and aggressive brain tumor with short overall survival (OS) of about 15 months. Understanding the causal factors affecting the patient survival is crucial for disease prognosis and treatment planning. Although previous efforts on survival prediction using multi-omics data has yielded useful predictive models, the causation of the correlated genetic risk factors has not been addressed.

The training process of many deep networks explores the same low-dimensional manifold.

We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training. By examining the underlying high-dimensional probabilistic models, we reveal that the training process explores an effectively low-dimensional manifold. Networks with a wide range of architectures, sizes, trained using different optimization methods, regularization techniques, data augmentation techniques, and weight initializations lie on the same manifold in the prediction space.

Time as a supervisor: temporal regularity and auditory object learning.

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects," that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e.

Bias in machine learning models can be significantly mitigated by careful training: Evidence from neuroimaging studies.

Despite the great promise that machine learning has offered in many fields of medicine, it has also raised concerns about potential biases and poor generalization across genders, age distributions, races and ethnicities, hospitals, and data acquisition equipment and protocols. In the current study, and in the context of three brain diseases, we provide evidence which suggests that when properly trained, machine learning models can generalize well across diverse conditions and do not necessarily suffer from bias. Specifically, by using multistudy magnetic resonance imaging consortia for diagnosing Alzheimer's disease, schizophrenia, and autism spectrum disorder, we find that well-trained models have a high area-under-the-curve (AUC) on subjects across different subgroups pertaining to attributes such as gender, age, racial groups and different clinical studies and are unbiased under multiple fairness metrics such as demographic parity difference, equalized odds difference, equal opportunity difference, etc.

How Occam's razor guides human decision-making.

Occam's razor is the principle that, all else being equal, simpler explanations should be preferred over more complex ones. This principle is thought to guide human decision-making, but the nature of this guidance is not known. Here we used preregistered behavioral experiments to show that people tend to prefer the simpler of two alternative explanations for uncertain data.

Acute immobilization stress evokes sexually dimorphic peripheral and hippocampal neuroimmune responses in adult rats.

Stress perception and response vary across sexes and may contribute to the sex differences in susceptibility to psychopathology. Stress also engages the immune system and baseline immune system markers are known to be sexually dimorphic. Here, we investigated if the neuroimmune consequences following a single episode of acute immobilization stress (AIS) are sexually dimorphic in male and female Sprague-Dawley rats.

Early Adversity and Accelerated Brain Aging: A Mini-Review.

Early adversity is an important risk factor that influences brain aging. Diverse animal models of early adversity, including gestational stress and postnatal paradigms disrupting dam-pup interactions evoke not only persistent neuroendocrine dysfunction and anxio-depressive behaviors, but also perturb the trajectory of healthy brain aging. The process of brain aging is thought to involve hallmark features such as mitochondrial dysfunction and oxidative stress, evoking impairments in neuronal bioenergetics.

Chronic hM4Di-DREADD-Mediated Chemogenetic Inhibition of Forebrain Excitatory Neurons in Postnatal or Juvenile Life Does Not Alter Adult Mood-Related Behavior.

G-protein-coupled receptors (GPCRs) coupled to G signaling, in particular downstream of monoaminergic neurotransmission, are posited to play a key role during developmental epochs (postnatal and juvenile) in shaping the emergence of adult anxiodepressive behaviors and sensorimotor gating. To address the role of G signaling in these developmental windows, we used a CaMKIIα-tTA::TRE hM4Di bigenic mouse line to express the hM4Di-DREADD (designer receptor exclusively activated by designer drugs) in forebrain excitatory neurons and enhanced G signaling via chronic administration of the DREADD agonist, clozapine--oxide (CNO) in the postnatal window (postnatal days 2-14) or the juvenile window (postnatal days 28-40). We confirmed that the expression of the HA-tagged hM4Di-DREADD was restricted to CaMKIIα-positive neurons in the forebrain, and that the administration of CNO in postnatal or juvenile windows evoked inhibition in forebrain circuits of the hippocampus and cortex, as indicated by a decline in expression of the neuronal activity marker c-Fos.

Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation.

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift challenge and has limited the widespread clinical adoption of machine learning models. Harmonization methods, which aim to learn a representation of data invariant to these differences are the prevalent tools to address domain shift, but they typically result in degradation of predictive accuracy.

Harmonization with Flow-Based Causal Inference.

Heterogeneity in medical data, e.g., from data collected at different sites and with different protocols in a clinical study, is a fundamental hurdle for accurate prediction using machine learning models, as such models often fail to generalize well.

A Geometric Interpretation of Stochastic Gradient Descent Using Diffusion Metrics.

This paper is a step towards developing a geometric understanding of a popular algorithm for training deep neural networks named stochastic gradient descent (SGD). We built upon a recent result which observed that the noise in SGD while training typical networks is highly non-isotropic. That motivated a deterministic model in which the trajectories of our dynamical systems are described via geodesics of a family of metrics arising from a certain diffusion matrix; namely, the covariance of the stochastic gradients in SGD.

Chronic postnatal chemogenetic activation of forebrain excitatory neurons evokes persistent changes in mood behavior.

Early adversity is a risk factor for the development of adult psychopathology. Common across multiple rodent models of early adversity is increased signaling via forebrain Gq-coupled neurotransmitter receptors. We addressed whether enhanced Gq-mediated signaling in forebrain excitatory neurons during postnatal life can evoke persistent mood-related behavioral changes.

Evaluation of Machine Learning Models for Classifying Upper Extremity Exercises Using Inertial Measurement Unit-Based Kinematic Data.

The amount of home-based exercise prescribed by a physical therapist is difficult to monitor. However, the integration of wearable inertial measurement unit (IMU) devices can aid in monitoring home exercise by analyzing exercise biomechanics. The objective of this study is to evaluate machine learning models for classifying nine different upper extremity exercises, based upon kinematic data captured from an IMU-based device.

Effective management of focal reactive gingival overgrowths by diode laser: A review and report of two cases.

Background: Focal reactive gingival overgrowths (FRGO) are a common observation in a clinical dental practice that may occur in response to external and internal chronic stimuli in form of fibrous connective tissue lesions in the oral mucosa. Gingiva is the most commonly involved site of oral reactive lesions. For the confirmed diagnosis of FRGO not only clinical, but the histopathological presentation of the lesion plays a vital role.

Acute Chemogenetic Activation of CamKIIα-Positive Forebrain Excitatory Neurons Regulates Anxiety-Like Behaviour in Mice.

Anxiety disorders are amongst the most prevalent mental health disorders. Several lines of evidence have implicated cortical regions such as the medial prefrontal cortex, orbitofrontal cortex, and insular cortex along with the hippocampus in the top-down modulation of anxiety-like behaviour in animal models. Both rodent models of anxiety, as well as treatment with anxiolytic drugs, result in the concomitant activation of multiple forebrain regions.

Multifaceted role of keratins in epithelial cell differentiation and transformation.

Keratins, the epithelial-predominant members of the intermediate filament superfamily, are expressed in a pairwise, tissuespecific and differentiation-dependent manner. There are 28 type I and 26 type II keratins, which share a common structure comprising a central coiled coil α-helical rod domain flanked by two nonhelical head and tail domains. These domains harbor sites for major posttranslational modifications like phosphorylation and glycosylation, which govern keratin function and dynamics.