Multimodal ChatGPT for Medical Applications: an Experimental Study of GPT-4V

In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i.e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task. Our experiments thoroughly assess GPT-4V's proficiency in answering questions paired with images using both pathology and radiology datasets from 11 modalities (e.g. Microscopy, Dermoscopy, X-ray, CT, etc.) and fifteen objects of interests (brain, liver, lung, etc.). Our datasets encompass a comprehensive range of medical inquiries, including sixteen distinct question types. Throughout our evaluations, we devised textual prompts for GPT-4V, directing it to synergize visual and textual information. The experiments with accuracy score conclude that the current version of GPT-4V is not recommended for real-world diagnostics due to its unreliable and suboptimal accuracy in responding to diagnostic medical questions. In addition, we delineate seven unique facets of GPT-4V's behavior in medical VQA, highlighting its constraints within this complex arena. The complete details of our evaluation cases are accessible at https://github.com/ZhilingYan/GPT4V-Medical-Report

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

Network anatomy in logopenic variant of primary progressive aphasia

The logopenic variant of primary progressive aphasia (lvPPA) is a neurodegenerative syndrome characterized linguistically by gradual loss of repetition and naming skills resulting from left posterior temporal and inferior parietal atrophy. Here, we sought to identify which specific cortical loci are initially targeted by the disease (epicenters) and investigate whether atrophy spreads through predetermined networks. First, we used cross-sectional structural MRI data from individuals with lvPPA to define putative disease epicenters using a surface-based approach paired with an anatomically fine-grained parcellation of the cortical surface (i.e., HCP-MMP1.0 atlas). Second, we combined cross-sectional functional MRI data from healthy controls and longitudinal structural MRI data from individuals with lvPPA to derive the epicenter-seeded resting-state networks most relevant to lvPPA symptomatology and ascertain whether functional connectivity in these networks predicts longitudinal atrophy spread in lvPPA. Our results show that two partially distinct brain networks anchored to the left anterior angular and posterior superior temporal gyri epicenters were preferentially associated with sentence repetition and naming skills in lvPPA. Critically, the strength of connectivity within these two networks in the neurologically-intact brain significantly predicted longitudinal atrophy progression in lvPPA. Taken together, our findings indicate that atrophy progression in lvPPA, starting from inferior parietal and temporoparietal junction regions, predominantly follows at least two partially nonoverlapping pathways, which may influence the heterogeneity in clinical presentation and prognosis

UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training

Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small scale, yielding the bottleneck of generalization. Towards a more effective and scalable paradigm, we propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics. Different from previous pre-training techniques for the unitary vision or textual feature, or with the brute-force alignment between vision and language information, we leverage the unique characteristic of report information in different granularity to build a hierarchical alignment mechanism, which strengthens the efficiency in feature learning. Our UniBrain is validated on three real world datasets with severe class imbalance and the public BraTS2019 dataset. It not only consistently outperforms all state-of-the-art diagnostic methods by a large margin and provides a superior grounding performance but also shows comparable performance compared to expert radiologists on certain disease types

A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets

OBJECTIVE: Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature. METHODS: The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review. RESULTS: A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging. CONCLUSIONS: Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning

Modeling and Mechanistic Investigation of α-synuclein aggregation

Our understanding of the α-synuclein aggregation process and the consequences thereof is currently limited, which in turn prevents the development of targeted therapeutic interventions. The work presented here, as a part of this thesis, is focused on expanding our understanding of the molecular events involved in α-synuclein aggregation. Towards this goal we have studied the impact of pathologically relevant forms of α-synuclein, namely A53T mutant α-synuclein and fibrillar α-synuclein, and characterized their impact on N-methyl-D-aspartate receptor (NMDAR) diffusion and function. We found both mutant and fibrillar α-synuclein, decreased the NMDAR diffusion and expression at the post-synapse. Moving further towards the mechanistic investigations we investigated the effect of two neuroprotective compounds on α-synuclein aggregation and found both compounds capable of clearing α-synuclein in cell and animal models potentially through autophagy related functions. In our efforts to scale mechanistic investigations we developed a high-throughput screening (HTS) capable FRET-based reporter for detection of α-synuclein aggregation in cells. Using this model, we performed a proof-of-concept screen of kinase inhibitors from which we identified three inhibitors with potent protective effects on α-synuclein aggregation. We further showed through mechanistic investigation that the protective effects likely involved lysosomal changes. Finally, in an effort to advance our knowledge of α-synuclein aggregation, we performed a genome-wide knockout screen to identify genes in the human genome with an impact on α-synuclein aggregation. This study also highlighted among other pathways the importance of the endolysosomal system in relation to α-synuclein aggregation. Many questions remain in regard to the molecular mechanisms involved in α-synuclein aggregation, but we hope our insights and models presented here will assist in the elucidation of the underlying mechanisms of α-synuclein aggregation

MEF2 impairment underlies skeletal muscle atrophy in polyglutamine disease

Polyglutamine (polyQ) tract expansion leads to proteotoxic misfolding and drives a family of nine diseases. We study spinal and bulbar muscular atrophy (SBMA), a progressive degenerative disorder of the neuromuscular system caused by the polyQ androgen receptor (AR). Using a knock-in mouse model of SBMA, AR113Q mice, we show that E3 ubiquitin ligases which are a hallmark of the canonical muscle atrophy machinery are not induced in AR113Q muscle. Similarly, we find no evidence to suggest dysfunction of signaling pathways that trigger muscle hypertrophy or impairment of the muscle stem cell niche. Instead, we find that skeletal muscle atrophy is characterized by diminished function of the transcriptional regulator Myocyte Enhancer Factor 2 (MEF2), a regulator of myofiber homeostasis. Decreased expression of MEF2 target genes is age- and glutamine tract length-dependent, occurs due to polyQ AR proteotoxicity, and is associated with sequestration of MEF2 into intranuclear inclusions in muscle. Skeletal muscle from R6/2 mice, a model of Huntington disease which develops progressive atrophy, also sequesters MEF2 into inclusions and displays age-dependent loss of MEF2 target genes. Similarly, SBMA patient muscle shows loss of MEF2 target gene expression, and restoring MEF2 activity in AR113Q muscle rescues fiber size and MEF2-regulated gene expression. This work establishes MEF2 impairment as a novel mechanism of skeletal muscle atrophy downstream of toxic polyglutamine proteins and as a therapeutic target for muscle atrophy in these disorders

Ultra-high Field MRI Methods for Precise Anatomical and Spectroscopic Measurements in the Brain and Application to Neurological and Neuropsychiatric Diseases

Neurological and neuropsychiatric diseases and disorders are a major burden on society, impairing the health and functioning of millions of people every year. There is a need to define the biological bases of these diseases and identify potential biomarkers to improve diagnosis, monitoring, and treatment efficacy across multiple diseases. Magnetic resonance imaging (MRI) is a noninvasive imaging technique which facilitates detection of brain lesions and visualization of the brain overall. However, limitations in contrast and resolution at clinical field strengths may hinder investigation of the underlying biological mechanisms of these diseases. Ultra-high field MRI scanners, such as those at 7-Tesla, can enhance biomarker detection because they provide superior contrast, resolution, and signal-to-noise-ratio (SNR) in feasible scan times. Since ultra-high field systems come with their own unique set of technical challenges, especially as applied to brain imaging, technique optimization and development is often required. To address these concerns while leveraging the advantages of 7-Tesla MRI, we have designed and conducted studies to provide high-resolution imaging of small structures and high spectral resolution of metabolite concentrations in clinically feasible scan times. As a group, these studies provide support for the usefulness of ultra-high field MRI for revealing disease pathophysiology through the detection of biomarkers, which may be unclear or below the threshold of detectability at clinical field strengths. The purpose of this work was to investigate anatomical and spectroscopic biomarkers in the brain. Specifically, we analyzed limbic structure subfield volumes, including subfields of the hippocampus, amygdala, and thalamus, for diseases including major depressive disorder (MDD) and trigeminal neuralgia (TN). We found a significant reduction in the right CA2/3 subfield volume of the hippocampus in MDD patients compared to healthy controls. In TN, we found significant differences in subfield volumes between patients and controls, specifically in the nerve cross-sectional-area, in the basal and paralaminar subnuclei of the amygdala, and in the central lateral subnucleus and the inferior and lateral pulvinar subnuclei of the thalamus. We also developed a method for detecting gamma-Aminobutyric acid (GABA) with spectroscopic editing, with potential application to MDD. In summary, we have presented significant findings in biomarker detection and a novel method for spectroscopic signal editing of GABA at ultra-high field MRI