Precision Network Modeling of Transcranial Magnetic Stimulation Across Individuals Suggests Therapeutic Targets and Potential for Improvement.

Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuropsychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear.

Robotic right ventricle is a biohybrid platform that simulates right ventricular function in (patho)physiological conditions and intervention.

The increasing recognition of the right ventricle (RV) necessitates the development of RV-focused interventions, devices and testbeds. In this study, we developed a soft robotic model of the right heart that accurately mimics RV biomechanics and hemodynamics, including free wall, septal and valve motion. This model uses a biohybrid approach, combining a chemically treated endocardial scaffold with a soft robotic synthetic myocardium.

Physically informed deep neural networks for metabolite-corrected plasma input function estimation in dynamic PET imaging.

Introduction: We propose a novel approach for the non-invasive quantification of dynamic PET imaging data, focusing on the arterial input function (AIF) without the need for invasive arterial cannulation.

Methods: Our method utilizes a combination of three-dimensional depth-wise separable convolutional layers and a physically informed deep neural network to incorporatea priori knowledge about the AIF's functional form and shape, enabling precise predictions of the concentrations of [C]PBR28 in whole blood and the free tracer in metabolite-corrected plasma.

Results: We found a robust linear correlation between our model's predicted AIF curves and those obtained through traditional, invasive measurements.

Sensor-Head Distance and Signal Strength in Whole-Head Magnetoencephalography: Report of 996 Patients With Epilepsy.

Purpose: Although the sensor-to-head distance is theoretically known to affect the signal strength in magnetoencephalography (MEG), these values have not been reported for a whole-head MEG system in a large population. We measured the distance and signal strength in 996 patients with epilepsy.

Methods: The MEG sensor array consisted of 102 measurement sites, each of which had two gradiometers and one magnetometer.

A comprehensive overview of diffuse correlation spectroscopy: Theoretical framework, recent advances in hardware, analysis, and applications.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures.

Three-dimensional fiber orientation mapping of the human brain at micrometer resolution.

The accurate measurement of three-dimensional (3D) fiber orientation in the brain is crucial for reconstructing fiber pathways and studying their involvement in neurological diseases. Comprehensive reconstruction of axonal tracts and small fascicles requires high-resolution technology beyond the ability of current imaging (e.g.

Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks.

Quantification of tissue parameters using MRI is emerging as a powerful tool in clinical diagnosis and research studies. The need for multiple long scans with different acquisition parameters prohibits quantitative MRI from reaching widespread adoption in routine clinical and research exams. Accelerated parameter mapping techniques leverage parallel imaging, signal modelling and deep learning to offer more practical quantitative MRI acquisitions.

Covert Consciousness in the ICU.

Objectives: For critically ill patients with acute severe brain injuries, consciousness may reemerge before behavioral responsiveness. The phenomenon of covert consciousness (i.e.

The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer.

Background: Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer.

Materials And Methods: A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers.

Cognitive Motor Dissociation in Disorders of Consciousness.

Background: Patients with brain injury who are unresponsive to commands may perform cognitive tasks that are detected on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). This phenomenon, known as cognitive motor dissociation, has not been systematically studied in a large cohort of persons with disorders of consciousness.

Methods: In this prospective cohort study conducted at six international centers, we collected clinical, behavioral, and task-based fMRI and EEG data from a convenience sample of 353 adults with disorders of consciousness.

RF shimming in the cervical spinal cord at 7 T.

Purpose: Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurodegenerative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality.

Time-division multiplexing (TDM) sequence removes bias in T estimation and relaxation-diffusion measurements.

Purpose: To compare the performance of multi-echo (ME) and time-division multiplexing (TDM) sequences for accelerated relaxation-diffusion MRI (rdMRI) acquisition and to examine their reliability in estimating accurate rdMRI microstructure measures.

Method: The ME, TDM, and the reference single-echo (SE) sequences with six TEs were implemented using Pulseq with single-band (SB) and multi-band 2 (MB2) acceleration factors. On a diffusion phantom, the image intensities of the three sequences were compared, and the differences were quantified using the normalized RMS error (NRMSE).

DeepEMC-T mapping: Deep learning-enabled T mapping based on echo modulation curve modeling.

Purpose: Echo modulation curve (EMC) modeling enables accurate quantification of T relaxation times in multi-echo spin-echo (MESE) imaging. The standard EMC-T mapping framework, however, requires sufficient echoes and cumbersome pixel-wise dictionary-matching steps. This work proposes a deep learning version of EMC-T mapping, called DeepEMC-T mapping, to efficiently estimate accurate T maps from fewer echoes.

Age-related alterations in human cortical microstructure across the lifespan: Insights from high-gradient diffusion MRI.

The human brain undergoes age-related microstructural alterations across the lifespan. Soma and Neurite Density Imaging (SANDI), a novel biophysical model of diffusion MRI, provides estimates of cell body (soma) radius and density, and neurite density in gray matter. The goal of this cross-sectional study was to assess the sensitivity of high-gradient diffusion MRI toward age-related alterations in cortical microstructure across the adult lifespan using SANDI.

A new protocol for multispecies bacterial infections in zebrafish and their monitoring through automated image analysis.

The zebrafish Danio rerio has become a popular model host to explore disease pathology caused by infectious agents. A main advantage is its transparency at an early age, which enables live imaging of infection dynamics. While multispecies infections are common in patients, the zebrafish model is rarely used to study them, although the model would be ideal for investigating pathogen-pathogen and pathogen-host interactions.

Neurovascular coupling methods in healthy individuals using transcranial doppler ultrasonography: A systematic review and consensus agreement.

Neurovascular coupling (NVC) is the perturbation of cerebral blood flow (CBF) to meet varying metabolic demands induced by various levels of neural activity. NVC may be assessed by Transcranial Doppler ultrasonography (TCD), using task activation protocols, but with significant methodological heterogeneity between studies, hindering cross-study comparisons. Therefore, this review aimed to summarise and compare available methods for TCD-based healthy NVC assessments.

Pupil contagion variation with gaze, arousal, and autistic traits.

Pupillary contagion occurs when one's pupil size unconsciously adapts to the pupil size of an observed individual and is presumed to reflect the transfer of arousal. Importantly, when estimating pupil contagion, low level stimuli properties need to be controlled for, to ensure that observations of pupillary changes are due to internal change in arousal rather than the external differences between stimuli. Here, naturalistic images of children's faces depicting either small or large pupils were presented to a group of children and adolescents with a wide range of autistic traits, a third of whom had been diagnosed with autism.

A blood-free modeling approach for the quantification of the blood-to-brain tracer exchange in TSPO PET imaging.

Introduction: Recent evidence suggests the blood-to-brain influx rate ( ) in imaging as a promising biomarker of blood-brain barrier () permeability alterations commonly associated with peripheral inflammation and heightened immune activity in the brain. However, standard compartmental modeling quantification is limited by the requirement of invasive and laborious procedures for extracting an arterial blood input function. In this study, we validate a simplified blood-free methodologic framework for estimation by fitting the early phase tracer dynamics using a single irreversible compartment model and an image-derived input function ().

Translation of monosynaptic circuits underlying amygdala fMRI neurofeedback training.

fMRI neurofeedback using autobiographical memory recall to upregulate the amygdala is associated with resting-state functional connectivity (rsFC) changes between the amygdala and the salience and default mode networks (SN and DMN, respectively). We hypothesize the existence of anatomical circuits underlying these rsFC changes. Using a cross-species brain parcellation, we identified in non-human primates locations homologous to the regions of interest (ROIs) from studies showing pre-to-post-neurofeedback changes in rsFC with the left amygdala.

The Human Connectome Project of adolescent anxiety and depression dataset.

This article describes primary data and resources available from the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, a novel arm of the Human Connectome Project (HCP). Data were collected from 215 adolescents (14-17 years old), 152 of whom had current diagnoses of anxiety and/or depressive disorders at study intake. Data include cross-sectional structural (T1- and T2-weighted), functional (resting state and three tasks), and diffusion-weighted magnetic resonance images.

Model-based navigation of transcranial focused ultrasound neuromodulation in humans: Application to targeting the amygdala and thalamus.

Background: Transcranial focused ultrasound (tFUS) neuromodulation has shown promise in animals but is challenging to translate to humans because of the thicker skull that heavily scatters ultrasound waves.

Objective: We develop and disseminate a model-based navigation (MBN) tool for acoustic dose delivery in the presence of skull aberrations that is easy to use by non-specialists.

Methods: We pre-compute acoustic beams for thousands of virtual transducer locations on the scalp of the subject under study.

Privacy preservation for federated learning in health care.

Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns.

The spleen assumes a major role in blood glucose regulation in type 1 diabetes patients treated with BCG.

The Bacillus Calmette-Guérin (BCG) vaccine, which has been used for > 100 years to prevent tuberculosis, is well-established for bladder cancer treatment, and under study for neurological and autoimmune diseases. In patients with type 1 diabetes (T1D), BCG vaccinations have been shown in randomized clinical trials to gradually lower blood sugar to near normal levels. This effect appears to be driven by a BCG-induced shift in lymphoid cells' glucose metabolism from oxidative phosphorylation to aerobic glycolysis.