The research, published in Frontiers in Neuroimaging, is a trans-disease meta-analysis led by a team at Tianjin Medical University General Hospital in China. The investigators pooled voxel-based morphometry studies published between 2001 and 2024, assembling neuroimaging data from 2,070 patients with multiple sclerosis against 1,625 healthy controls, and 1,624 patients with Alzheimer’s disease against 1,607 healthy controls, drawn from 84 articles. Voxel-based morphometry is a technique that compares grey matter volume across the whole brain voxel by voxel, allowing researchers to detect regional shrinkage that often precedes symptoms. By statistically combining the peak coordinates of significant grey matter clusters from dozens of independent studies using the Seed-based Mapping software, the team produced disease-level atrophy maps of unusual statistical power.

The maps revealed a striking split. Multiple sclerosis-specific atrophy localized to the bilateral dorsomedial thalamic nucleus and the precentral gyrus — the deep grey matter and sensorimotor cortex that reflect the disease’s demyelinating character. Alzheimer’s-specific atrophy, in the age-unadjusted comparisons, centered on the bilateral hippocampus-amygdala complex, the medial temporal structures long known as the disease’s earliest casualties. Cytoarchitectonic mapping using the probabilistic Julich Brain Atlas added anatomical precision: the left hippocampal peak in Alzheimer’s disease was assigned to the CA1 subfield with a 52.5 percent probability, while the right parahippocampal peak fell within the laterobasal amygdala. The team also found that some of these between-disease differences, particularly in the thalamus and hippocampal-amygdala regions, were attenuated after age adjustment — a reminder that the twenty-year gap in typical onset age between the two conditions shapes part of the anatomical picture.

But the more consequential finding was the overlap. When the investigators computed the conjunction of the two atrophy maps, common grey matter loss emerged in the bilateral superior temporal gyrus, the insulae, the posterior cingulate cortex, the amygdalae, the right rectus gyrus, and striatal components including the caudate nucleus and putamen. These regions form the backbone of two major brain networks: the auditory-language network, anchored by the superior temporal gyrus, and the emotional processing network, a circuit spanning the amygdala, insula, orbitofrontal cortex, and striatum. Neurosynth decoding — a cross-sample spatial correlation between the atrophy map and thousands of brain activation maps for cognitive terms — confirmed the functional relevance, with the top terms encompassing speech production, auditory processing, face recognition, pain perception, and emotional regulation.

The convergence is not merely anatomical. Postmortem studies cited in the paper have documented pronounced cortical microglial activation in both multiple sclerosis and Alzheimer’s disease with closely similar patterns, and Alzheimer’s-type pathology, including beta-amyloid plaques and neurofibrillary tangles, has been identified in a subset of multiple sclerosis cases. Case reports have described patients with multiple sclerosis and progressive cognitive impairment who proved amyloid-positive and were subsequently diagnosed with coexisting Alzheimer’s disease. Conversely, recent work suggests that certain aspects of multiple sclerosis biology, or specific genetic factors in those patients, may offer unexpected protection against amyloid pathology — hinting at undiscovered neuroprotective crosstalk between the two conditions.

To probe the molecular underpinnings of the shared atrophy pattern, the team turned to spatial transcriptomics. They extracted gene expression profiles from the Allen Human Brain Atlas, a postmortem dataset mapping genome-wide expression across the adult human brain, and linked each tissue sample to the grey matter Z-value at its corresponding coordinate in the meta-analytic atrophy map. A partial least squares regression model then identified gene-expression patterns spatially associated with the atrophy profile. Of the five components estimated, PLS2 explained 13.10 percent of the variance and was the only one to survive a spatial autocorrelation-preserving permutation test — a critical safeguard, because brain maps are spatially smooth and naive statistics can inflate gene-imaging correlations. The PLS2 score correlated positively with the Z-scores of the overlapping atrophy regions, and 834 genes emerged as significantly associated with the common atrophy pattern, split into a positive set of 290 genes and a negative set of 544.

Functional enrichment analysis of these gene sets converged on four core biological themes: neurodegenerative injury, immune-inflammatory regulation, energy metabolic homeostasis, and synaptic-axonal maintenance. The positive-weighted genes predominantly encode how cells respond to internal and external stress — genomic damage, metabolic and energy challenges — and determine cellular fate through DNA stability maintenance, cell cycle arrest, metabolic remodeling, and quality control mechanisms such as mitophagy and apoptosis. This theme resonates deeply with established pathology. Mitochondrial dysfunction has been identified as a cause of axonal degeneration in multiple sclerosis, where energy deficits in demyelinated axons disrupt transport and promote cell death, while reduced mitochondrial density in Alzheimer’s disease signals an oxidative stress burden. The positive gene set also showed significant enrichment in adaptive immune cell differentiation and activation pathways, including T-cell receptor signaling — the very processes that drive inflammatory lesion formation in multiple sclerosis, where T-cell migration across the blood-brain barrier is a precondition for demyelination.

The negative-weighted genes tell the complementary story. They are functionally characterized by active suppression of excessive inflammation — inhibiting the production of interleukin-1 beta, interferon-beta, and interleukin-6 — along with specification of immune cell subsets such as marginal zone B cells and follicular dendritic cells, and maintenance of fundamental processes like DNA replication and synaptic vesicle transport. Cell-type-specific expression analysis showed the negative set was specifically expressed in immune cells, Pnoc-positive neurons of cortex, the habenula, and the hypothalamus, while the positive set was significantly expressed in the striatum. In other words, the shared atrophy pattern is spatially aligned with genes that both execute stress responses and actively restrain them — an immunological homeostasis imbalance that may represent the common molecular soil in which grey matter loss grows under two very different etiologies.

The authors are careful about what these findings do and do not mean. The spatial correlations link normative postmortem gene expression to disease-related atrophy; they are not evidence of differential gene expression in patients or direct causal effects of the identified genes on grey matter loss. Multiple sclerosis clinical subtypes carry distinct neurobiological features, and genetic heterogeneity across them could influence the results. Most importantly, because the two diseases have different primary neuropathological mechanisms, the shared atrophy regions and transcriptomic patterns may represent phenotypic outcomes induced by different disease processes rather than proof of a shared pathogenic mechanism — and the results cannot replace existing clinical diagnostic criteria for either condition.

Even with those caveats, the implications are considerable. The identification of a shared cortical-limbic-striatal atrophy nexus, validated across more than 6,900 brain scans and anchored to 834 genes spanning stress response, immune regulation, and energy metabolism, offers a template for transdiagnostic biomarkers of cognitive impairment and for therapeutic targets with relevance across disorders. The authors suggest that neuromodulation targeting the amygdala-insula-striatum circuit, such as transcranial magnetic stimulation, might simultaneously improve emotional symptoms in both conditions. As people with multiple sclerosis live longer and age into the risk window for neurodegenerative disease, the boundary between the two illnesses may matter less than the molecular machinery they share — and that machinery, this study argues, is now mapped.

Subject of Research: Neuroscience

Article Title: Grey matter atrophy signatures and their genetic associations: a trans-disease study of Alzheimer’s disease and multiple sclerosis

Article References: Zhao, Y., Sun, J., Reheman, B., Bai, X., Zhang, C., Ding, W., Zou, B., Zhang, H., Zhang, Z., & Zhang, N. (2026). Grey matter atrophy signatures and their genetic associations: a trans-disease study of Alzheimer’s disease and multiple sclerosis. Frontiers in Neuroimaging, 5, Article 1917609. https://doi.org/10.3389/fnimg.2026.1917609

Image Credits: AI Generated

DOI: 10.3389/fnimg.2026.1917609

Keywords: Alzheimer's disease, multiple sclerosis, grey matter atrophy, meta-analysis, spatial transcriptomics, Allen Human Brain Atlas, neuroimaging, neurodegeneration, gene expression, Frontiers in Neuroimaging, Grey, matter

The study, published in BMC Psychiatry, enrolled 66 patients with systemic lupus erythematosus and 33 healthy volunteers, all recruited from Taizhou People’s Hospital. Participants underwent neuropsychological assessment and resting-state functional magnetic resonance imaging, a technique that records brain activity while subjects lie still and think of nothing in particular. Rather than treating this spontaneous activity as a static signal, the team focused on a measure called dynamic regional homogeneity, or dReHo. Regional homogeneity reflects how synchronized the activity of neighboring voxels is at any given moment; high homogeneity suggests that a local patch of cortex is firing in a coordinated fashion, while low homogeneity suggests disarray. By computing this metric across sliding time windows, the researchers could track how that local coordination fluctuates over the course of a scan, capturing a dimension of brain function that conventional static analyses miss entirely.

The results were striking. Seven brain regions showed abnormal dynamic regional homogeneity in the lupus patients compared with healthy controls: the left gyrus rectus, the left and right middle temporal gyri, the left and right middle occipital gyri, the left precuneus, and the right calcarine fissure. What unites most of these regions is their role in vision. The calcarine fissure is the cortical landing strip for signals arriving from the eyes, the primary visual processing hub of the brain, while the middle occipital and middle temporal gyri form successive stages of the visual hierarchy, extracting motion, form, and spatial relationships from the raw retinal input. The precuneus, tucked into the medial parietal lobe, weaves visual and memory signals into self-referential thought and social cognition, and the gyrus rectus on the underside of the frontal lobe contributes to emotional and social processing. Functional annotation of the affected regions confirmed this impression, linking the abnormality map primarily to social cognition and visual processing.

Identifying where the brain behaves abnormally is only half the story. The deeper question is why those particular regions, and not others, are vulnerable in lupus. To address this, the team turned to an increasingly popular strategy known as imaging transcriptomics. The Allen Human Brain Atlas provides gene expression data from postmortem tissue sampled across the entire cerebral cortex, and researchers can ask whether the spatial fingerprint of an imaging abnormality, region by region, tracks the spatial fingerprint of any particular set of genes. If brain areas with high expression of a certain gene family are precisely the areas showing the greatest functional disruption, that gene family becomes a plausible molecular suspect. It is an inferential approach, and the authors are careful to describe their findings as hypothesis-generating, but it offers something that pure imaging studies cannot: a bridge from circuits to molecules.

The transcriptomic analysis pointed in a coherent direction. Genes whose cortical expression patterns matched the dReHo alteration map were significantly enriched for functions related to chemical synaptic transmission, the fundamental mechanism by which neurons communicate across the tiny gaps between them. The map also aligned with markers of excitatory neurons, the workhorse cells that drive cortical activity, and with genes preferentially expressed in the visual cortex. Two specific excitatory neuron subclasses stood out: Ex3-Granule-L4 cells, which populate the middle layer of the cortex where sensory input first arrives, and Ex8-Corticothalamic-Projecting-L6 cells, which send feedback from deep cortical layers back down to the thalamus, the brain’s relay station. In other words, the regions most affected in lupus are those built around a dense machinery of excitatory synaptic transmission in the sensory processing stream, suggesting that the disease may be disrupting the very synapses that carry visual and social information through the cortex.

The team went further, correlating the dReHo abnormality map with molecular maps of neurotransmitter systems, each derived from independent imaging and pharmacological data. Eight systems showed meaningful spatial correspondence: the dopamine transporter DAT, the serotonin receptors 5-HT2A and 5-HT6, the inhibitory transmitter GABA, the kappa-opioid receptor, the muscarinic acetylcholine receptor M1, the synaptic vesicle glycoprotein SV2A, and histone deacetylase, or HDAC. Each of these carries its own implications. Serotonin and dopamine are the classic targets of psychiatric pharmacology, and their involvement hints at why lupus patients experience depression, anxiety, and psychosis. GABA is the brain’s principal brake, and a relative loss of inhibitory control could explain the local desynchronization captured by the dReHo metric. SV2A marks the density of synaptic terminals themselves, while HDAC sits at the interface between gene regulation and inflammation, a pathway of particular interest in autoimmune disease.

For clinicians, the study offers a potential explanation for a frustrating clinical reality. Many neuropsychiatric lupus symptoms, from subtle cognitive slowing to visual disturbances and difficulty reading social cues, have historically been attributed to vascular injury, antibody-mediated inflammation, or the psychological burden of chronic illness, yet objective biomarkers have remained scarce. The finding that dynamic local coordination is disrupted specifically in visual and social cognition networks suggests that some of these symptoms may have a measurable functional signature, one that could in principle be detected with a standard resting-state MRI scanner already installed in most hospitals. The involvement of the visual cortex is especially provocative, because visual complaints in lupus are often dismissed as peripheral, confined to the eye itself, when the new data suggest that the brain’s central visual machinery may be quietly reorganizing.

The authors are appropriately measured about what their results can and cannot claim. The sample, while respectable for a study of this kind, comprised 66 patients and 33 controls from a single center, and the imaging-transcriptomic method is correlational by design: it identifies spatial associations between gene expression and functional disruption but cannot prove that any particular gene or neurotransmitter causes the abnormalities. The dReHo alterations could reflect downstream consequences of inflammation, vascular compromise, medication effects, or the immune assault itself, and disentangling these mechanisms will require longitudinal studies that follow patients from disease onset through treatment. Still, the framework is powerful. By anchoring a functional imaging phenotype to synaptic transmission genes, specific excitatory neuron classes, and eight distinct neurotransmitter systems, the study generates a ranked list of molecular targets that pharmacologists and immunologists can now interrogate.

What makes the work emblematic of a broader shift in neuroscience is its methodological synthesis. A decade ago, a rheumatology question would rarely have been answered with a brain atlas of gene expression; today, the tools exist to overlay a patient group’s functional abnormality map onto the transcriptomic landscape of the cortex and read out candidate biology in a single analysis. The Taizhou team’s integration of dynamic fMRI metrics, the Allen Human Brain Atlas, neurotransmitter receptor maps, and single-cell-derived neuron subclass data represents exactly the kind of multi-scale approach that is reshaping how researchers study neuropsychiatric disease. For the estimated millions of people worldwide living with lupus, and particularly for the substantial fraction who experience neurological or psychiatric complications, the study offers something intangible but important: evidence that the fog of neuropsychiatric lupus has structure, that it follows the brain’s molecular geography, and that with the right combination of imaging and genomics, that geography can finally be mapped.

Subject of Research: Dynamic brain activity changes and their transcriptomic correlates in neuropsychiatric systemic lupus erythematosus

Article Title: Dynamic regional homogeneity changes and their genetic correlates in neuropsychiatric systemic lupus erythematosus: a combined transcriptome-neuroimaging study

Article References: Pu, X., Li, Y., Sun, Z., Liu, Y., Ji, W., Yin, X., Wu, Q., Dong, L., Zou, H., Zhang, J., & Xia, J. (2026). Dynamic regional homogeneity changes and their genetic correlates in neuropsychiatric systemic lupus erythematosus: a combined transcriptome-neuroimaging study. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08653-2

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08653-2

Keywords: neuropsychiatric lupus, systemic lupus erythematosus, resting-state fMRI, dynamic regional homogeneity, imaging transcriptomics, Allen Human Brain Atlas, visual cortex, synaptic transmission, neurotransmitters, excitatory neurons, autoimmune disease, brain mapping

A team led by Hui Yuan, Haishan Wu, and Wenbin Guo at the Second Xiangya Hospital of Central South University, working with collaborators across several Chinese hospitals, enrolled 315 treatment-naïve participants: 94 people with depressed bipolar disorder, 94 with major depressive disorder, and 127 healthy controls. Restricting the sample to individuals who had not yet received psychiatric medication was a deliberate design choice, because drugs such as antidepressants and mood stabilizers can themselves reshape brain connectivity and confound group comparisons. All participants underwent resting-state functional magnetic resonance imaging, in which spontaneous blood-oxygen-level-dependent fluctuations are recorded while the subject simply lies still, allowing researchers to map the brain’s intrinsic functional architecture.

The imaging analysis rested on the Dosenbach 160-node functional atlas, a parcellation that divides the cortex and subcortex into 160 regions of interest grouped into canonical networks. Using the DPABI toolbox, the researchers computed static functional connectivity, a measure of how tightly the activity of two brain regions is synchronized over the entire scan. They then went a step further with dynamic functional connectivity, applying a sliding-window approach implemented in the DynamicBC toolbox. This technique slices the resting-state time series into short overlapping segments and computes connectivity within each, revealing how the brain’s coordination patterns fluctuate from moment to moment rather than assuming a single fixed configuration across the whole session.

The static results were the study’s headline finding, and they point in opposite directions for the two disorders. Patients with depressed bipolar disorder showed heightened connectivity within four major networks: the default mode network, the frontoparietal network, the sensorimotor network, and the ventral attention network. Patients with major depressive disorder showed the reverse pattern, with reduced connectivity within these same networks. The default mode network, active during self-referential thought and rumination, and the frontoparietal network, which supports cognitive control and emotion regulation, have both been repeatedly implicated in mood disorders. But the fact that the same networks show increased internal synchronization in one illness and decreased synchronization in the other is what makes the finding biologically interesting rather than merely confirmatory.

This directional contrast suggests that the two conditions, despite their surface similarity, arise from distinct pathophysiological mechanisms rather than a single continuum of depression. In bipolar depression, exaggerated coupling within these networks may reflect a hyperconnected intrinsic state, potentially related to the emotional intensity and cognitive inflexibility characteristic of the illness. In major depressive disorder, weakened within-network integration may correspond to the disconnection between self-referential processing and regulatory control systems that many prior studies have described. The sensorimotor and ventral attention findings add further texture, hinting that psychomotor slowing and attentional biases, both common in depressive states, are mirrored in the functional organization of motor and attention circuitry.

The dynamic analysis, by contrast, found no significant differences between the bipolar and major depression groups in how their connectivity patterns shifted over time. This null result is itself informative. It suggests that the diagnostic signal separating the two illnesses resides primarily in the stable, averaged architecture of brain networks rather than in their moment-to-moment reconfiguration. For researchers designing biomarker studies, that is a practical clue: static connectivity measures, which are simpler and more robust to compute, may carry more discriminative value than the more elaborate dynamic metrics, at least for this clinical distinction.

To probe what might drive these connectivity differences at the molecular level, the team turned to transcriptomic mapping. They linked their neuroimaging results to gene expression data from the Allen Human Brain Atlas, a publicly available resource that charts the expression of roughly 20,000 genes across the human brain. By correlating the spatial pattern of connectivity alterations with the spatial distribution of gene expression, the researchers identified genes whose regional expression profiles track the connectivity changes seen in the patient groups. Enrichment analyses pointed to genes involved in synaptic signaling and in the regulation of the MAPK cascade, a signaling pathway that participates in neuronal plasticity, stress responses, and the downstream effects of several psychotropic drugs.

The transcriptomic layer transforms the study from a descriptive imaging comparison into a hypothesis about mechanism. If the regions most affected in bipolar depression are enriched for genes governing synaptic communication and MAPK-mediated signaling, that hints at where the developmental or molecular vulnerabilities might lie. It also connects the imaging findings to a growing literature in which genome-wide association study loci for psychiatric illness are mapped onto brain gene expression to identify which cell types and circuits are most genetically implicated. The authors report that validation analyses supported the robustness of their findings, an important step given how sensitive connectivity analyses can be to preprocessing choices, motion artifacts, and multiple-comparison thresholds, which the team addressed using false discovery rate correction.

Cognitive assessments were woven into the design as well, reflecting the study’s aim of integrating neuroimaging, cognitive, and genetic evidence into a single framework. Mood disorders are not purely affective conditions; they carry measurable deficits in attention, memory, and executive function, and these deficits differ in profile between bipolar and unipolar illness. By anchoring the imaging results to cognitive performance, the researchers sought to show that the connectivity differences are not abstract statistical contrasts but correspond to differences in how patients actually think and feel. The study, in the authors’ words, offers a comprehensive exploration of intrinsic functional architecture disparities in resting-state activity between the two conditions, delving into both static and dynamic functional connectivity.

The clinical implications are tantalizing but should be tempered with caution. A single cross-sectional study, however well designed, cannot by itself become a diagnostic test. The directional contrast in within-network connectivity is a promising candidate marker, and the fact that it emerged in a treatment-naïve sample strengthens its claim to reflect illness biology rather than medication effects. But replication in independent cohorts, ideally with longitudinal follow-up to see whether connectivity patterns predict illness course or treatment response, will be essential. If the findings hold, they could pave the way toward more precise diagnosis and targeted treatment strategies, helping clinicians identify bipolar disorder earlier, spare unipolar patients unnecessary medication trials, and guide the development of interventions aimed at the specific circuits and molecular pathways that each condition disrupts. For now, the study contributes a valuable piece to the evolving puzzle of mood disorders, demonstrating that two conditions that look identical in the clinic can look like opposites in the brain.

Subject of Research: Distinguishing depressed bipolar disorder from major depressive disorder using static and dynamic functional connectivity, cognitive, and transcriptomic evidence

Article Title: Distinct static functional patterns and dynamic patterns between depressed bipolar disorder and major depressive disorder: integrating neuroimaging, cognitive, and transcriptomic evidence

Article References: Yuan, H., Ding, Y., Teng, Z., Ou, Y., Jin, K., Tang, H., Chen, J., Li, S., Wang, B., Qiu, Y., Xu, X., Li, H., Liu, F., Li, P., Zhao, J., Guo, Y., Feng, Y., Wu, H., & Guo, W. (2026). Distinct static functional patterns and dynamic patterns between depressed bipolar disorder and major depressive disorder: integrating neuroimaging, cognitive, and transcriptomic evidence. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08674-x

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08674-x

Keywords: bipolar disorder, major depressive disorder, functional connectivity, resting-state fMRI, default mode network, frontoparietal network, transcriptomics, Allen Human Brain Atlas, MAPK cascade, synaptic signaling, psychiatry, biomarkers

The research, led by Lubin Gou and Junqiang Lei of the First Hospital of Lanzhou University and Lanzhou University’s First Clinical Medical College, focused on patients with liver cirrhosis without overt hepatic encephalopathy, a stage abbreviated LC-nOHE. This is the window in which the disease has already compromised liver function but has not yet produced the confusion, disorientation, and personality changes that define overt hepatic encephalopathy. Understanding what happens in the brain during this silent phase is critical, because it may reveal the earliest opportunities for intervention before irreversible damage takes hold.

The team recruited 86 patients with liver cirrhosis but no overt encephalopathy, along with 62 healthy controls, and acquired high-resolution three-dimensional T1-weighted magnetic resonance images of every participant’s brain. From these scans, the researchers extracted the volume of the thalamus and three distinct measures of cortical shape: cortical thickness, sulcal depth, and fractal dimension, a measure of the complexity of the brain’s folded surface. Rather than examining these features in isolation, the investigators constructed structural covariance networks, mathematical maps in which brain regions are connected if their anatomical features covary across individuals. Such networks are widely used as proxies for coordinated maturational and degenerative processes, offering a window into how brain regions change together as a system.

The results were striking at multiple levels. Compared with healthy controls, patients with cirrhosis showed enlargement of the thalamus alongside a broad range of cortical morphological abnormalities. At the level of whole-network organization, the thalamo-cortical structural covariance networks of patients displayed reduced segregation, meaning the brain’s specialized modules were less clearly differentiated, and decreased integration, meaning efficient communication across the network was impaired. These two properties, segregation and integration, are hallmarks of a healthy, well-organized brain, and their simultaneous deterioration suggests a fundamental disruption of the architecture that supports cognition.

Zooming in on individual network nodes, the researchers found that the centrality of three regions was significantly reduced in patients: the thalamus itself, the supramarginal gyrus, and the insula. Each of these regions plays a recognizable role in the syndrome. The thalamus relays sensory and motor signals to the cortex and regulates consciousness and alertness; the supramarginal gyrus contributes to language and spatial cognition; and the insula supports interoception and awareness of the body’s internal state. Reduced centrality in these hubs indicates that they had lost influence within the network, becoming less connected to the rest of the brain’s structural architecture.

Perhaps the most clinically significant finding was the relationship between brain changes and liver function. The degree centrality of the thalamus was negatively correlated with liver function measures, meaning that the worse the liver was performing, the more the thalamus had lost its position within the brain’s structural network. This correlation ties the brain imaging directly to the severity of liver disease and supports the idea that the thalamus sits at the front line of the liver-brain axis, the pathway by which hepatic dysfunction, circulating toxins such as ammonia, and systemic inflammation are translated into neural injury.

To move beyond correlation and probe causality, the team applied a technique called causal analysis of structural covariance networks, or CaSCN. This approach examines how changes in one brain region’s morphology relate to changes in others across the progression of disease, allowing researchers to ask which region leads and which follows. The analyses demonstrated that thalamus volume had causal effects on the alterations of cortical morphology as liver dysfunction progressed. In other words, the data are consistent with a model in which the thalamus is not merely another victim of cirrhosis but an active driver, propagating structural changes outward to the cortical regions with which it is connected.

The study then took an unusual additional step: linking the brain imaging to genomics through imaging transcriptomics. Using normative gene-expression profiles from the Allen Human Brain Atlas, the researchers evaluated whether the spatial pattern of causal effects across the cortex overlapped with the spatial distribution of specific genes. It did. The pattern of causal path coefficients was spatially correlated with the expression of particular genes in the normative atlas, suggesting that the cortical regions most vulnerable to thalamus-driven change are also those with distinctive molecular signatures. Gene ontology analyses pointed toward enrichment in biological processes, molecular functions, and cellular components that may help explain why some cortical areas are preferentially affected while others are relatively spared.

Taken together, the findings provide a comprehensive, multilevel view of how the thalamo-cortical circuit becomes progressively vulnerable in liver cirrhosis before overt encephalopathy appears. The enlargement of the thalamus, consistent with processes such as edema or glial changes reported in prior literature on hepatic encephalopathy, appears to initiate a cascade that erodes the segregation and integration of the entire structural network, strips key hubs of their centrality, and reshapes the cortex in patterns governed partly by underlying gene expression. Because the thalamus’s network position tracks liver function, measures of thalamo-cortical network integrity could potentially serve as imaging biomarkers for identifying patients at risk of progressing to overt hepatic encephalopathy, enabling earlier monitoring and treatment.

The authors emphasize that these findings offer a potential mechanism-driven framework for understanding the earliest brain consequences of liver disease, one that connects organ function, network neuroscience, and transcriptomics within a single analytical pipeline. The work was supported by the Lanzhou University First Affiliated Hospital Foundation and the Science and Technology Department of Gansu Province, and it was approved by the Institutional Ethics Committee of the First Hospital of Lanzhou University. As cirrhosis continues to affect millions worldwide, studies like this one bring clinicians closer to detecting, and perhaps preventing, the neurological toll of liver disease before it announces itself in the clinic.

Subject of Research: Structural covariance network alterations of the thalamo-cortical circuit in liver cirrhosis patients without overt hepatic encephalopathy

Article Title: Multilevel structural covariance network alterations of thalamo-cortical circuit in liver cirrhosis patients without overt hepatic encephalopathy: associations with liver function and imaging transcriptomics

Article References: Gou, L., Ren, H., Xu, W., Gao, Y., Wang, S., Zhang, Y., Dou, Y., & Lei, J. (2026). Multilevel structural covariance network alterations of thalamo-cortical circuit in liver cirrhosis patients without overt hepatic encephalopathy: associations with liver function and imaging transcriptomics. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02790-6

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02790-6

Keywords: liver cirrhosis, hepatic encephalopathy, thalamus, thalamo-cortical circuit, structural covariance network, MRI, liver-brain axis, imaging transcriptomics, cortical thickness, liver function, neuroimaging, Allen Human Brain Atlas