Individual Brain Research Articles

Application of individual brain connectome in chronic ischemia: mapping symptoms before and after reperfusion.

How brain functions in the distorted ischemic state before and after reperfusion is unclear. It is also uncertain whether there are any indicators within ischemic brain that could predict surgical outcomes. To alleviate these issues, we applied individual brain connectome in chronic steno-occlusive vasculopathy (CSOV) to map both ischemic symptoms and their postbypass changes. A total of 499 bypasses in 455 CSOV patients were collected and followed up for 47.8±20.5 months. Using multimodal parcellation with connectivity-based and pathological distortion-independent approach, areal MR features of brain connectome were generated with three measurements of functional connectivity (FC), structural connectivity, and PageRank centrality at the single-subject level. Thirty-three machine-learning models were then trained with clinical and areal MR features to obtain acceptable classifiers for both ischemic symptoms and their postbypass changes, among which, 11 were deemed acceptable (AUC>0.7). Notably, the FC feature-based model for long-term neurological outcomes performed very well (AUC>0.8). Finally, a Shapley additive explanations plot was adopted to extract important individual features in acceptable models to generate "fingerprints" of brain connectome. This study not only establishes brain connectomic fingerprint databases for brain ischemia with distortion, but also provides informative insights for how brain functions before and after reperfusion.

Long-Term Bridge Training Induces Functional Plasticity Changes in the Brain of Early-Adult Individuals.

The aim of this study was to investigate the impact of extended bridge expertise on rapid perceptual processing and brain functional plasticity in early adulthood, utilizing functional magnetic resonance imaging (fMRI). In this investigation, we compared 6 high-level college bridge players with 25 college students lacking bridge experience, assessing their intelligence and working memory. Additionally, we scrutinized behavioral performance and whole-brain activation patterns during an image perceptual judgment task. Findings indicated significant group and interaction effects at the behavioral level. Bridge players exhibited prolonged reaction times and enhanced accuracy on card tasks. At the neural level, the activation level of bridge players in the occipital lobe exceeded that of ordinary college students, with more pronounced group effects in the motor area and inferior parietal lobule during card tasks. This implies that bridge expertise in early adulthood induces functional plasticity changes in regions associated with visual processing and automated mathematical computation.

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry.

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm.

In the field of neuroimaging, more studies of abnormalities in brain regions of the autism spectrum disorder (ASD) usually focused on two brain regions connected, and less on abnormalities of higher-order interactions of brain regions. To explore the complex relationships of brain regions, we used the partial entropy decomposition (PED) algorithm to capture higher-order interactions by computing the higher-order dependencies of all three brain regions (triads). We proposed a method for examining the effect of individual brain regions on triads based on the PED and surrogate tests. The key triads were discovered by analyzing the effects. Further, the hypergraph modularity maximization algorithm revealed the higher-order brain structures, of which the link between right thalamus and left thalamus in ASD was more loose compared with the typical control (TC). Redundant key triad (left cerebellum crus 1 and left precuneus and right inferior occipital gyrus) exhibited a discernible attenuation in interaction in ASD, while the synergistic key triad (right cerebellum crus 1 and left postcentral gyrus and left lingual gyrus) indicated a notable decline. The results of classification model further confirmed the potential of the key triads as diagnostic biomarkers.

Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN) for cross-modal individual analysis of the whole brain

Cross-modal analysis of the same whole brain is an ideal strategy to uncover brain function and dysfunction. However, it remains challenging due to the slow speed and destructiveness of traditional whole-brain optical imaging techniques. Here we develop a new platform, termed Photoacoustic Tomography with Temporal Encoding Reconstruction (PATTERN), for non-destructive, high-speed, 3D imaging of ex vivo rodent, ferret, and non-human primate brains. Using an optimally designed image acquisition scheme and an accompanying machine-learning algorithm, PATTERN extracts signals of genetically-encoded probes from photobleaching-based temporal modulation and enables reliable visualization of neural projection in the whole central nervous system with 3D isotropic resolution. Without structural and biological perturbation to the sample, PATTERN can be combined with other whole-brain imaging modalities to acquire the whole-brain image with both high resolution and morphological fidelity. Furthermore, cross-modal transcriptome analysis of an individual brain is achieved by PATTERN imaging. Together, PATTERN provides a compatible and versatile strategy for brain-wide cross-modal analysis at the individual level.

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age—referred to as brain-predicted age difference (brain-PAD)—is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

Further characterisation of late somatosensory evoked potentials using electroencephalogram and magnetoencephalogram source imaging.

Beside the well-documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensory evoked potentials, using sensory inputs of different strengths and by testing the correlation between cortical sources. Simultaneous high-density electroencephalograms and magnetoencephalograms were performed in 19 participants, and electrical stimulation was applied to the median nerve (wrist level) at intensity between 1.5 and 9 times the perceptual threshold. Source imaging was undertaken to map the stimulus-induced brain cortical activity according to each individual brain magnetic resonance imaging, during three windows of analysis covering early and late somatosensory evoked potentials. Results for P60/N60 and P100/N100 were compared with those for P20/N20 (early response). According to literature, maximal activity during P20/N20 was found in central sulcus contralateral to stimulation site. During P60/N60 and P100/N100, activity was observed in contralateral primary sensorimotor area, secondary somatosensory area (on both hemispheres) and premotor and multisensory associative cortices. Late responses exhibited similar characteristics but different from P20/N20, and no significant correlation was found between early and late generated activities. Specific clusters of cortical activities were activated with specific input/output relationships underlying early and late somatosensory evoked potentials. Cortical networks, partly common to and distinct from early somatosensory responses, contribute to late responses, all participating in the complex somatosensory brain processing.

Human endogenous retrovirus W in multiple sclerosis: transcriptional activity is associated with decline in oligodendrocyte proportions in the white matter of the brain.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease. One of the basic mechanisms in this disease is the autoimmune response against the myelin sheet leading to axonal damage. There is strong evidence showing that this response is regulated by both genetic and environmental factors. In addition, the role of viruses has been extensively studied, especially in the case of human endogenous retroviruses (HERVs). However, although several associations with MS susceptibility, especially in the case of HERV-W family have been observed, the pathogenic mechanisms have remained enigmatic. To clarify these HERV-mediated mechanisms as well as the responsible HERV-W loci, we utilized RNA sequencing data obtained from the white matter of the brain of individuals with and without MS. CIBERSORTx tool was applied to estimate the proportions of neuronal, glial, and endothelial cells in the brain. In addition, the transcriptional activity of 215 HERV-W loci were analyzed. The results indicated that 65 HERV-W loci had detectable expression, of which 14 were differentially expressed between MS and control samples. Of these, 12 HERV-W loci were upregulated in MS. Expression levels of the 8 upregulated HERV-W loci had significant negative correlation with estimated oligodendrocyte proportions, suggesting that they are associated with the dynamics of oligodendrocyte generation and/or maintenance. Furthermore, Gene Set Enrichment Analysis (GSEA) results indicated that expression levels of three upregulated HERV-W loci: 2p16.2, 2q13, and Xq13.3, are associated with suppression of oligodendrocyte development and myelination. Taken together, these data suggest new HERV-W loci candidates that might take part in MS pathogenesis.

Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence

Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.

EfficientNet Transfer Learning Approach for Multi-Class Brain Tumor Classification

- It can be difficult for radiologists to accurately diagnose brain tumors in the medical field. When radiologists manually identify tumors, it can result in incorrect treatment planning and medical errors. The Miami Neuroscience Center states that there are 120 different kinds of brain tumors that can impact an individual's brain. Brain tumors that are most common and dangerous include glioma, meningioma, and pituitary tumors. In this work, we developed an online diagnostic tool to accurately classify brain tumors, including Astrocytoma, Glioma, Meningioma, Neurocytoma and Pituitary. Additionally, our program is capable of classifying a normal brain. We added CT and MRI scan images to improve cross-modality and model capability. We employed the CNN based neural network architecture known as EfficientNet. With this model, we achieved over 93.4% accuracy. Our application will help the radiologist identify tumors quickly and promote better treatment planning. Key words: Deep Learning, Transfer Learning, CNN, MRI, CT, Multi-Class Classification.

Neural processing of speech comprehension in noise predicts individual age using fNIRS-based brain-behavior models.

Speech comprehension in noise depends on complex interactions between peripheral sensory and central cognitive systems. Despite having normal peripheral hearing, older adults show difficulties in speech comprehension. It remains unclear whether the brain's neural responses could indicate aging. The current study examined whether individual brain activation during speech perception in different listening environments could predict age. We applied functional near-infrared spectroscopy to 93 normal-hearing human adults (20 to 70years old) during a sentence listening task, which contained a quiet condition and 4 different signal-to-noise ratios (SNR = 10, 5, 0, -5dB) noisy conditions. A data-driven approach, the region-based brain-age predictive modeling was adopted. We observed a significant behavioral decrease with age under the 4 noisy conditions, but not under the quiet condition. Brain activations in SNR = 10dB listening condition could successfully predict individual's age. Moreover, we found that the bilateral visual sensory cortex, left dorsal speech pathway, left cerebellum, right temporal-parietal junction area, right homolog Wernicke's area, and right middle temporal gyrus contributed most to prediction performance. These results demonstrate that the activations of regions about sensory-motor mapping of sound, especially in noisy conditions, could be sensitive measures for age prediction than external behavior measures.

DNA Methylation Biosignature of Suicide Death in the Brains of Individuals With Bipolar Disorder

DNA Methylation Biosignature of Suicide Death in the Brains of Individuals With Bipolar Disorder

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease.

Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for AD, such as neuroimaging approaches. Neuroimaging techniques, including positron emission tomography and magnetic resonance imaging, have revolutionized the field by providing valuable insights into the structural and functional alterations in the brains of individuals with AD. These imaging modalities enable the detection of early biomarkers such as amyloid-β plaques and tau protein tangles, facilitating early and precise diagnosis. Furthermore, the emerging technologies encompassing blood-based biomarkers and neurochemical profiling exhibit promising results in the identification of specific molecular signatures for AD. The integration of machine learning algorithms and artificial intelligence has enhanced the predictive capacity of these diagnostic tools when analyzing complex datasets. In this review article, we will highlight not only some of the most used diagnostic imaging approaches in neurodegeneration research but focus much more on new tools like artificial intelligence, emphasizing their application in the realm of AD. These advancements hold immense potential for early detection and intervention, thereby paving the way for personalized therapeutic strategies and ultimately augmenting the quality of life for individuals affected by AD.

Attention-Deficit/ Hyperactivity Disorder Brain Functional Reference: Introducing an Inter-subject Consistency Measure and Dual-layer Parcellation

The latest advancements in neuroimaging techniques have contributedto studying the disordered human brain, but still, there is a lack ofdisease-specific brain reference to localise brain seeds and standardisethe comparison across studies. The current study first evaluates theinter-subject consistency across Attention-Deficit/ HyperactivityDisorder (ADHD) in a publicly accessible resting-state functionalmagnetic resonance imaging (RS-fMRI) ADHD-200 dataset by proposing a new voxels similarity index (VSI) that integrates bothinter-atlases flexibility (F`) from previous studies with proposed intersubjectsstability (S`) measure to improve outcomes. Secondly, thestudy employs a dual-layer clustering-based parcellation strategyinspired by the resultant improved Master Atlas networks to examinethe ADHD cerebral cortex. The suggested approach to enhancingthe ADHD cortex parcellation uses spectral clustering for globalstructure identification, followed by hierarchical clustering for localrefinements and granularity specification. The final connectivitydrivenbrain reference achieved an average homogeneity of 0.63,and the enhanced Master Atlas with (S’) achieved 0.35 homogeneity,surpassing the original Master Atlas with 0.27. This discovery impliesthat the final brain reference offers a more accurate and reliableframework to examine the connections and functions of the brain inindividuals with ADHD.

Total intracranial hemorrhage volume measurement summating all compartments best in traumatic and nontraumatic intracranial bleeding.

The ANNEXA-4 trial measured hemostatic efficacy of andexanet alfa in patients with major bleeding taking factor Xa inhibitors. A proportion of this was traumatic and nontraumatic intracranial bleeding. Different measurements were applied in the trial including volumetrics to assess for intracranial bleeding depending on the compartment involved. We aimed to determine the most reliable way to measure intracranial hemorrhage (ICrH) volume by comparing individual brain compartment and total ICrH volume. Thirty patients were randomly selected from the ANNEXA-4 database to assess measurement of ICrH volume by compartment and in total. Total and compartmental hemorrhage volumes were measured by five readers using Quantomo software. Each reader measured baseline hemorrhage volumes twice separated by 1 week. Twenty-eight different ANNEXA-4 subjects were also randomly selected to assess intra-rater reliability of total ICrH volume measurement change at baseline and 12-h follow up, performed by three readers twice to assess hemostatic efficacy categories used in ANNEXA-4. Compartmental minimal detectable change percentages (MDC%) ranged between 9.72 and 224.13, with the greatest measurement error occurring in patients with a subdural hemorrhage. Total ICrH volume measurements had the lowest MDC%, which ranged between 6.57 and 33.52 depending on the reader. Measurement of total ICrH volumes is more accurate than volume by compartment with less measurement error. Determination of hemostatic efficacy was consistent across readers, and within the same reader, as well as when compared to consensus read. Volumetric analysis of intracranial hemostatic efficacy is feasible and reliable when using total ICrH volumes.

Clinical and Brain Morphometry Predictors of Deep Brain Stimulation Outcome in Parkinson’s Disease

Subthalamic deep brain stimulation (STN-DBS) is known to improve motor function in advanced Parkinson’s disease (PD) and to enable a reduction of anti-parkinsonian medication. While the levodopa challenge test and disease duration are considered good predictors of STN-DBS outcome, other clinical and neuroanatomical predictors are less established. This study aimed to evaluate, in addition to clinical predictors, the effect of patients’ individual brain topography on DBS outcome. The medical records of 35 PD patients were used to analyze DBS outcomes measured with the following scales: Part III of the Unified Parkinson’s Disease Rating Scale (UPDRS-III) off medication at baseline, and at 6-months during medication off and stimulation on, use of anti-parkinsonian medication (LED), Abnormal Involuntary Movement Scale (AIMS) and Non-Motor Symptoms Questionnaire (NMS-Quest). Furthermore, preoperative brain MRI images were utilized to analyze the brain morphology in relation to STN-DBS outcome. With STN-DBS, a 44% reduction in the UPDRS-III score and a 43% decrease in the LED were observed (p<0.001). Dyskinesia and non-motor symptoms decreased significantly [median reductions of 78,6% (IQR 45,5%) and 18,4% (IQR 32,2%) respectively, p=0.001 – 0.047]. Along with the levodopa challenge test, patients’ age correlated with the observed DBS outcome measured as UPDRS-III improvement (ρ= -0.466 – -0.521, p<0.005). Patients with greater LED decline had lower grey matter volumes in left superior medial frontal gyrus, in supplementary motor area and cingulum bilaterally. Additionally, patients with greater UPDRS-III score improvement had lower grey matter volume in similar grey matter areas. These findings remained significant when adjusted for sex, age, baseline LED and UPDRS scores respectively and for total intracranial volume (p=0.0041- 0.001). However, only the LED decrease finding remained significant when the analyses were further controlled for stimulation amplitude. It appears that along with the clinical predictors of STN-DBS outcome, individual patient topographic differences may influence DBS outcome. Clinical Trial Registration Number: NCT06095245, registration date October 23, 2023, retrospectively registered

Ex vivo method for rapid quantification of post traumatic brain injury lesion volumes using ultrasound

BackgroundStudies of traumatic brain injury often involve the quantification of the lesion volume as a major outcome measure. The determination of lesion volume typically employs the cutting and mounting of brain tissue, and the calculation of the cross-sectional area of the lesion within each section of brain after histological staining. This is a time consuming and laborious task often requiring many weeks to determine the lesion volume for an individual brain. MethodsIn this report we present a method for determining the lesion volume within the brain following traumatic brain injury that involves the use of ultrasound imaging. With this process the lesion volume can be determined within a time period of 90 min per brain rather than weeks and months. Moreover, we have developed a pipeline that will combine the cross-sectional ultrasound images of the brain with the Allen Mouse Brain Atlas to provide the precise anatomical structures that are affected by traumatic injury to the brain. The anatomical detail was lastly paired with behavioral data showing neurological deficits correlated with specific areas of brain injury. ResultsThe accuracy and precision of this method was shown to be highly consistent with the traditional histological approach. Additionally, the mapping process and behavioral data show that neurological recovery from 1 to 3 weeks post injury is not correlated with gross anatomical recovery of the TBI lesion in our TBI model. ConclusionTogether these approaches will enhance the pipeline for processing brain tissue in experimental conditions where the lesion volume is an important outcome parameter and provide more high resolution information about the identity of the damaged regions of the brain.

Why behaviour matters: Studying inter-brain coordination during child-caregiver interaction

Modern technology allows for simultaneous neuroimaging from interacting caregiver-child dyads. Whereas most analyses that examine the coordination between brain regions within an individual brain do so by measuring changes relative to observed events, studies that examine coordination between two interacting brains generally do this by measuring average intra-brain coordination across entire blocks or experimental conditions. In other words, they do not examine changes in inter-brain coordination relative to individual behavioural events. Here, we discuss the limitations of this approach. First, we present data suggesting that fine-grained temporal interdependencies in behaviour can leave residual artifact in neuroimaging data. We show how artifact can manifest as both power and (through that) phase synchrony effects in EEG and affect wavelet transform coherence in fNIRS analyses. Second, we discuss different possible mechanistic explanations of how inter-brain coordination is established and maintained. We argue that non-event-locked approaches struggle to differentiate between them. Instead, we contend that approaches which examine how interpersonal dynamics change around behavioural events have better potential for addressing possible artifactual confounds and for teasing apart the overlapping mechanisms that drive changes in inter-brain coordination.

Neuroimaging and Brain-Based Markers Identifying Neurobiological Markers Associated With Criminal Behaviour, Personality Disorders, and Mental Health: A Narrative Review.

We begin the review by pointing to the common stigma associated with mental health issues, which often derives from a lack of understanding or incomplete knowledge.Neurobiological research provides us with a new lens to help challenge and dispel common assumptions and misunderstandings and gives an understanding of sexual behaviours that influence society.As such, it generates substantial evidence for the structural and functional asymmetry of the brains of individuals with mental disorders.However, this type of representation poses many challenges to traditional thinking and constantly provokes change in perspective and empathy towards those individuals. In the review, we go deeper into the effects of neurobiological findings on understanding criminal behaviours and personality disorders, looking further beyond behavioural health.These problems, which were once mainly discussed as moral ones or viewed from the perspective of character flaws, are analysed today through neurological considerations pointing to their complexity.When the root of bipolar disorder is revealed to be neurological, society will react with more information and understanding, hence reducing the stigmatisation and discrimination meted out to people with these problems. At a macro level, findings from neurobiology affect society in ways that go beyond individuals; social attitudes, laws, and policies about the services rendered are influenced.Operating as a catalyst within the community, neurobiological research helps to initiate social change through the creation of an informed, understanding public forum.Thus, it creates broader value for those dealing with behavioural and mental health challenges. The first and most important question of this narrative review is focused on identifying identifiable neurobiological markers that are closely related to criminal conduct, personality disorders, and mental health disorders.Through this review, we aim to present detailed insights into the neurological foundations that anchor these phenomena via a narrative analysis of contemporary literature.The potential implications are finding problems early to apply specific treatment and learning an advanced strategy for social attitudes.This will promote a more humanistic approach based on adequate information on the behavioural and mental health issues involved.

An increase in ER stress and unfolded protein response in iPSCs-derived neuronal cells from neuronopathic Gaucher disease patients

Gaucher disease (GD) is a lysosomal storage disorder caused by a mutation in the GBA1 gene, responsible for encoding the enzyme Glucocerebrosidase (GCase). Although neuronal death and neuroinflammation have been observed in the brains of individuals with neuronopathic Gaucher disease (nGD), the exact mechanism underlying neurodegeneration in nGD remains unclear. In this study, we used two induced pluripotent stem cells (iPSCs)-derived neuronal cell lines acquired from two type-3 GD patients (GD3-1 and GD3-2) to investigate the mechanisms underlying nGD by biochemical analyses. These iPSCs-derived neuronal cells from GD3-1 and GD3-2 exhibit an impairment in endoplasmic reticulum (ER) calcium homeostasis and an increase in unfolded protein response markers (BiP and CHOP), indicating the presence of ER stress in nGD. A significant increase in the BAX/BCL-2 ratio and an increase in Annexin V-positive cells demonstrate a notable increase in apoptotic cell death in GD iPSCs-derived neurons, suggesting downstream signaling after an increase in the unfolded protein response. Our study involves the establishment of iPSCs-derived neuronal models for GD and proposes a possible mechanism underlying nGD. This mechanism involves the activation of ER stress and the unfolded protein response, ultimately leading to apoptotic cell death in neurons.