Keywords

Neuroinflammation, humoral immunity, multiple sclerosis, Alzheimer’s disease, amyotrophic lateral sclerosis, ageing, precision medicine

Neuroinflammation is now recognized as a driver, rather than a mere bystander, across neurological diseases, shaping disease onset, trajectory, and therapeutic response [1]. Neuroinflammation is primarily driven by microglia, astrocytes, cytokine networks, and blood–brain barrier (BBB) breakdown [2, 3]. The pathogenic aspect emphasizes glial–neuronal crosstalk, inflammasome signaling, mitochondrial dysfunction, oxidative stress, and impaired resolution of inflammation [2-4]. Reciprocal glial–neuronal communication is mediated through cytokines, chemokines, purinergic signaling, and damage-associated molecular patterns, collectively altering neuronal excitability and synaptic homeostasis [1, 4]. Compounding these processes, BBB breakdown permits peripheral immune cells and circulating cytokines to infiltrate the central nervous system (CNS), reinforcing chronic inflammatory loops [2]. The adaptive immune system, on the other hand, consists of two major coordinated branches: the cell-mediated and the humoral response [5]. While T cells drive the cell-mediated arm, the humoral immune response is a subdivision of the adaptive immune system mediated by B cells and the antibodies they produce [6]. Humoral responses function by generating antigen-specific antibodies, enabling neutralization of pathogens, opsonization, and complement activation, while the broader adaptive immune system also includes T cell–driven mechanisms such as cytotoxicity and immune regulation [5, 7].
Humoral immunity plays a major role in the pathogenesis, progression, and potential treatment of multiple neurological diseases [8]. Crosstalk between microglia, astrocytes, and B cells, integrating cytokines, chemokines, and antigen-presenting interactions, creates a bidirectional signaling network that shapes both protective and pathogenic aspects of humoral immunity within the CNS [5]. Each neurological disease features a distinct interplay of these mechanisms (Figure 1).

Recent advances in neuroimmunology have clarified how antibodies, B cell subsets, and immune–brain communication contribute to neuroinflammation, neurodegeneration, and autoimmunity [8]. Across neurological diseases, humoral immunity plays diverse roles, from pathogenic autoantibody-driven injury in multiple sclerosis (MS) to complex protective vs. inflammatory B cell functions in Alzheimer’s disease (AD), and broad immune–brain interactions in Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and stroke (Table 1) [9-12]. Biomarkers rooted in humoral activity increasingly support diagnosis, monitoring, and therapeutic development, while targeted immunotherapies are reshaping treatment paradigms [13-16].

All of the above reinforce the concept of neurological diseases as systemic disorders involving central–peripheral immune crosstalk. Therefore, this review presents a multi-disease comparative analysis (MS–AD–PD–ALS–stroke), focusing on autoimmune, neurodegenerative, and cerebrovascular diseases, presenting mechanisms of neuroinflammation and humoral immunity in the context of ageing, strengths and limitations of biomarkers, therapeutic implications, analysis of challenges, controversies, and future directions.

Neuroimmune mechanisms represent a shared but highly heterogeneous axis across major neurological diseases. Rather than acting as isolated processes, neuroinflammatory and humoral immune pathways form interconnected, disease-specific networks, whose relative contributions define distinct immunopathological profiles ranging from classical autoimmunity to secondary immune activation (Table 2).

Neuroinflammatory mechanisms

Across neurological disorders, neuroinflammation consistently emerges as the central pathological framework, yet its origin and immunological architecture differ fundamentally (Figure 2A). At one end of the spectrum, MS exemplifies a fully integrated neuroinflammatory–adaptive immune process, in which peripheral immune activation directly initiates CNS pathology [17]. Disruption of the BBB enables sustained infiltration of autoreactive T lymphocytes and myeloid cells, driving demyelination and axonal injury [18]. Within the CNS, activated microglia and astrocytes amplify this response through cytokine release, chemokine signaling, and reactive oxygen species (ROS) production, establishing a chronic, self-sustaining inflammatory environment tightly coupled to adaptive immunity (Figure 2B) [19].

In contrast, neurodegenerative diseases such as AD, PD, and ALS are characterized by neuroinflammation that arises primarily as a response to intrinsic neuronal pathology, yet progressively assumes a pathogenic role. In AD, neuroinflammation is dominated by chronic microglial activation and impaired protein clearance, particularly of amyloid-β (Aβ), accompanied by inflammasome activation and immunometabolic dysfunction [11]. The transition of microglia from a homeostatic to a pro-inflammatory phenotype results in sustained cytokine production, synaptic injury, and progressive neuronal loss, positioning innate immunity as the primary driver of disease propagation rather than a secondary epiphenomenon (Figure 2C) [20].
A similar but mechanistically distinct pattern is observed in PD, where misfolded α-synuclein acts as a potent trigger of innate immune activation. Through interaction with microglial receptors, including Toll-like receptors, α-synuclein induces pro-inflammatory signaling and neurotoxicity [21]. Although BBB dysfunction and T cell infiltration are present, they remain subsidiary to innate immune activation and do not reach the pathogenic prominence seen in MS (Figure 2D) [22].
ALS further expands this paradigm by combining robust glial activation with emerging adaptive immune contributions. Persistent activation of microglia and astrocytes generates a toxic inflammatory milieu that directly contributes to motor neuron degeneration [23, 24]. At the same time, recent evidence of autoreactive CD4⁺ T-cell responses indicates that adaptive immunity may influence disease heterogeneity and progression, although it does not dominate the process to the same extent as in autoimmune disorders [25]. Neuroinflammation in ALS thus represents a feed-forward system in which degeneration and immune activation reinforce each other (Figure 2E).
Stroke represents a fundamentally different context, defined by an acute and temporally dynamic neuroinflammatory response. Within minutes of ischemic injury, metabolic failure and oxidative stress activate microglia and astrocytes, initiating a cascade of cytokine release, BBB disruption, and leukocyte recruitment [26, 27]. Unlike chronic diseases, neuroinflammation in stroke evolves through distinct phases, from early innate activation to later immune cell infiltration and tissue remodeling, making it both a driver of secondary injury and a determinant of recovery (Figure 2F) [28].
Taken together, neuroinflammation across these conditions can be conceptualized along a continuum, from adaptive immune–driven (MS) to innate-dominant (AD, PD, ALS, stroke), with important variations in timing, cellular composition, and pathological impact.

Humoral immune mechanisms

In contrast to the central role of neuroinflammation, humoral immunity demonstrates a highly variable and context-dependent contribution, ranging from primary pathogenic driver to secondary modulator.
MS again represents the clearest example of pathogenic humoral immunity, where B cells actively shape disease evolution beyond antibody production alone [17]. Through antigen presentation and interaction with T cells, B cells reinforce inflammatory circuits and can form ectopic lymphoid structures within the meninges [10]. Intrathecal IgG synthesis, reflected by oligoclonal bands (OCBs), indicates compartmentalized CNS immune activity and remains a defining diagnostic hallmark [10, 29]. Importantly, IgG, particularly complement-activating subclasses such as IgG1 and IgG3, directly contributes to demyelination via classical complement activation, establishing humoral immunity as a co-dominant pathogenic force alongside cellular mechanisms (Figure 2B) [19, 30].
In neurodegenerative diseases, however, humoral immune responses assume a markedly different role. Rather than acting as primary drivers, they are generally secondary, modulatory, or reflective of underlying neurodegeneration. In AD, complement activation is closely associated with amyloid plaques and modulates microglial behavior, influencing phagocytosis and inflammatory signaling [7, 12]. Despite the presence of various autoantibodies, there is no evidence that IgG-mediated cytotoxicity drives disease pathology [31]. Instead, humoral components function within a broader innate immune environment, shaping but not initiating the neuroinflammatory response (Figure 2C).
A similar interpretative framework applies to PD, where α-synuclein–reactive autoantibodies and B cell involvement have been described, but remain inconsistent across studies (Figure 2D) [32, 33]. These findings are most probably explained as immune responses to ongoing neuronal damage, rather than causative mechanisms. The absence of reproducible pathogenic IgG signatures further distinguishes PD from classical autoimmune conditions.
In ALS, humoral contributions are more evident at the level of complement activation and immune complex formation, which modulate inflammatory signaling and glial activation [7, 12]. Although autoantibodies are frequently detected, they are generally regarded as biomarkers of neuronal injury rather than direct mediators of pathology [34]. Instead, emerging evidence highlights adaptive T-cell responses as a more credible contributor to disease-specific immune mechanisms (Figure 2E) [25].
In stroke, humoral immunity plays a secondary but dynamically important role, particularly following BBB disruption. The extravasation of IgG into brain tissue promotes complement activation and amplifies microglial responses, contributing to secondary injury [35]. Concurrently, infiltration of B cells and production of cytokines and antibodies reflect systemic immune activation (Figure 2F) [35]. Unlike MS, however, these processes are context-dependent and temporally regulated, arising after the initial insult rather than initiating it [36].
When considered together, these diseases illustrate a spectrum in which the relationship between neuroinflammation and humoral immunity shifts from tight integration to functional dissociation. MS occupies one extreme, defined by synergistic interactions between adaptive cellular and antibody-mediated mechanisms. Neurodegenerative diseases occupy an intermediate position, where innate immune activation dominates and humoral responses serve supportive or reactive roles. Stroke represents a distinct acute model, in which both neuroinflammatory and humoral processes are rapidly induced and temporally coordinated following tissue injury.

Ageing impairs BBB and blood–cerebrospinal fluid barrier (BCSFB) function, allowing peripheral cytokines and immune cells to access the CNS more readily, which amplifies neuroinflammation and influences disease trajectories in neurological diseases [37]. With ageing, both innate and adaptive immunity decline (thymic involution, skewing toward memory T cells, altered B and natural killer (NK) cell function), while chronic low-grade inflammation (inflammaging) increases [38]. Ageing shifts microglia towards pro-inflammatory phenotypes with mitochondrial and lysosomal dysfunction and reduced clearance of proteopathic aggregates [37]. Astrocytes also adopt reactive and senescent states that sustain inflammaging and impair neuronal and synaptic support [39]. Together, these changes increase vulnerability to neurodegeneration, influence disease progression, and may affect therapeutic responses in older adults [37-39].
In MS, ageing promotes a shift from relapsing to progressive disease through immunosenescence and inflammaging, resulting in reduced relapse activity but increased neurodegeneration and diminished CNS repair capacity [37, 38].
Ageing is the strongest risk factor for AD, facilitating Aβ and tau pathology through impaired cellular homeostasis, increased neuroinflammation, oxidative stress, and synaptic dysfunction, ultimately accelerating cognitive decline [40].
In PD, substantia nigra dopaminergic neurons are particularly vulnerable to age-associated mitochondrial dysfunction, oxidative stress, impaired proteostasis and chronic neuroinflammation [41]. BBB dysfunction and age-associated changes in cerebrospinal fluid (CSF) biomarkers further reflect more complex disease mechanisms in older patients [42].
Ageing is also a major risk factor for ALS, as key hallmarks of ageing—including genomic instability, loss of proteostasis, mitochondrial dysfunction, impaired autophagy, and chronic inflammation—overlap with core pathogenic mechanisms driving motor neuron degeneration [43].
Similarly, ageing increases stroke susceptibility through endothelial dysfunction, BBB impairment, oxidative stress, and neurovascular decline. These changes exacerbate post-stroke neuroinflammation, worsen neurological outcomes, and reduce the brain’s capacity for recovery [44-46].

Neuroinflammation has emerged as a central pathological process across a broad spectrum of neurological diseases, and consequently as a major source of candidate biomarkers with diagnostic, prognostic, and monitoring potential. Despite shared inflammatory pathways, including cytokine signaling, glial activation, and immune-mediated tissue injury, the biological meaning, specificity, and clinical utility of neuroinflammatory biomarkers vary substantially across diseases, reflecting their underlying immunopathological architecture. In parallel, humoral biomarkers derived from CSF and blood provide complementary insights into immune activation, neurodegeneration, and systemic responses, though their interpretative value is shaped by disease context, methodological constraints, and variability across patient populations (Table 3).

Comparative performance of neuroinflammatory biomarkers

The clinical utility of neuroinflammatory biomarkers is most robust in diseases where inflammation is proximal to the primary pathogenic mechanism. MS represents the strongest example of this relationship. Because MS is fundamentally an immune-mediated disorder, CSF biomarkers of inflammation closely mirror active CNS pathology. Markers such as chitinase-3-like protein 1 (YKL-40), glial fibrillary acidic protein (GFAP), S100 calcium-binding protein B (S100B), and neurofilament light chain (NfL) consistently show elevation and correlate with disease stage, progression, and phenotypic variation [47]. Longitudinal cytokines and chemokines, including IL-6, chemokine C-X-C motif ligand 13 (CXCL13), and osteopontin, further provide prognostic insight into disease activity and cognitive decline [48]. The emergence of extracellular vesicles as minimally invasive indicators of glial activation extends this framework, enabling dynamic monitoring of immune signaling in real time [49].
However, even in this most favorable context, important limitations persist. Only a limited subset of markers, most notably OCBs, has been integrated into routine clinical practice. Many candidate biomarkers remain insufficiently standardized, and their interpretation is complicated by disease heterogeneity, demographic influences, and differences between CSF and blood measurements, with peripheral markers generally showing reduced specificity [9, 49].
In contrast, in neurodegenerative diseases such as AD, PD, and ALS, neuroinflammatory biomarkers reflect reactive or modulatory processes rather than primary disease drivers, which fundamentally alters their clinical role. In AD, markers of glial activation, including GFAP, YKL-40, and soluble TREM2, capture early immune responses associated with amyloid and tau pathology and show promise for early disease detection, particularly in preclinical stages [50, 51]. Composite inflammatory signatures integrating cytokines, chemokines, and complement components outperform individual markers and more accurately reflect underlying CNS processes, including hippocampal atrophy and neurodegeneration [52, 53]. Importantly, these inflammatory profiles enhance diagnostic models when combined with established biomarkers and genetic risk factors [50, 53]. However, their lack of disease specificity, given that similar elevations occur in other neurodegenerative and inflammatory conditions, limits their standalone diagnostic value. Temporal variability, particularly in markers such as sTREM2, further complicates interpretation across disease stages [54].
A similar pattern of limited specificity and high biological heterogeneity is observed in PD. Inflammatory gene signatures and transcriptomic profiles suggest the existence of biologically distinct subtypes characterized by cytokine dysregulation and altered metabolic pathways [55]. Neuroimaging markers of microglial activation provide additional evidence linking inflammation to clinical severity, bridging molecular and clinical domains [56]. Nevertheless, inflammatory biomarkers alone are insufficient to distinguish PD from related disorders [42], reinforcing the need for multimodal approaches that integrate molecular, imaging, and protein aggregation markers.
In ALS, neuroinflammatory biomarkers provide insight into disease activity and progression, with cytokine profiles in CSF and blood correlating with functional decline and systemic inflammatory indices [57]. However, their clinical utility remains limited by variability and lack of specificity. Notably, neurodegeneration-specific biomarkers, particularly neurofilaments, consistently outperform inflammatory markers in diagnostic and prognostic contexts [58]. This highlights a key distinction: in ALS, inflammation is biologically relevant but not the most informative biomarker domain for clinical decision-making [59, 60].
Stroke introduces an additional layer of complexity through its temporal dynamics. Neuroinflammatory markers such as IL-1β, IL-6, TNF-α, and IL-8 rise rapidly following ischemic injury and correlate with infarct size, neurological deterioration, and systemic complications [61, 62]. Later increases in anti-inflammatory cytokines reflect repair processes [61]. Easily accessible systemic indices, such as the neutrophil-to-lymphocyte ratio, provide practical prognostic indicators [62]. However, the rapidly evolving and bidirectional nature of post-stroke inflammation limits the utility of single time-point measurements [61]. Moreover, the non-specific nature of inflammatory responses prevents these markers from replacing neuroimaging in acute diagnosis [63, 64] (Table 4).

Comparative role of humoral biomarkers

Humoral biomarkers provide a second, complementary dimension, but their contribution varies markedly depending on whether humoral immunity is pathogenic, reactive, or secondary to tissue injury. In MS, humoral biomarkers are deeply embedded in disease biology. Intrathecal IgG synthesis, reflected by OCBs, remains a defining diagnostic feature, while NfL has emerged as a critical marker of neuroaxonal injury with strong correlations to magnetic resonance imaging (MRI) activity, relapse frequency, and long-term disability [9, 49]. Advances in ultrasensitive detection methods have enabled reliable measurement of NfL in blood, supporting longitudinal monitoring [49, 65]. Despite these strengths, limitations remain significant: NfL lacks disease specificity and is influenced by physiological variables such as age and body composition, while broader humoral markers, including cytokines and B cell activation signatures, show substantial variability and lack standardized interpretation frameworks [49, 66].
In AD, the landscape of humoral biomarkers has undergone rapid transformation, particularly with the development of blood-based assays reflecting amyloid and tau pathology. Plasma biomarkers such as Aβ42/40 ratio and phosphorylated tau isoforms show strong correspondence with core disease mechanisms and cognitive decline, and recent technological advances have significantly improved sensitivity and scalability [50, 67]. Nevertheless, these markers do not consistently map to specific cognitive domains, and questions remain regarding their impact on patient outcomes and appropriate use in population-level screening. Methodological variability across assays further complicates implementation.
In PD, humoral biomarker development has focused on detecting misfolded α-synuclein, with seed amplification assays representing a major advance in diagnostic accuracy. These assays provide strong molecular evidence of synucleinopathy and address limitations of traditional biomarkers [68]. However, many other humoral candidates, including inflammatory and lysosomal proteins, lack specificity and show overlap with related disorders. Practical challenges such as reliance on CSF, pre-analytical variability, and lack of standardization continue to limit widespread clinical adoption [68].
In ALS, humoral biomarkers, particularly NfL and phosphorylated neurofilament heavy chain, demonstrate the most consistent diagnostic and prognostic performance across neurological diseases [59, 69]. These markers correlate strongly with disease progression, survival, and therapeutic response, and have been successfully integrated into clinical trials [60]. Emerging exosome-derived biomarkers further expand the field by capturing intracellular pathological proteins and molecular signatures [70]. Despite this progress, no biomarker has achieved full clinical validation, and issues of specificity, cohort variability, and methodological inconsistency remain unresolved.
In stroke, humoral biomarkers reflect the broad systemic and CNS response to acute injury, encompassing coagulation, inflammation, endothelial dysfunction, and neuronal damage [64]. Multi-marker panels and machine-learning–derived signatures demonstrate improved diagnostic performance compared to individual markers, highlighting the value of integrative approaches [13, 15]. However, substantial variability across populations and the inability of any biomarker to reliably distinguish stroke from mimics in the acute phase limit their current clinical use, maintaining neuroimaging as the diagnostic gold standard (Table 5).

Integrated comparative perspective

The comparative analysis highlights a fundamental principle: biomarker performance is determined not only by analytical sensitivity, but by alignment with disease biology. Neuroinflammatory markers are most informative when inflammation directly drives pathology, while humoral biomarkers achieve maximal relevance when they reflect either core disease mechanisms or quantifiable tissue injury.
Future progress will depend on integrating these domains into multimodal biomarker frameworks, combining inflammatory, neurodegenerative, molecular, and imaging data. Such approaches are essential to overcome current limitations in specificity, reproducibility, and clinical translation, and to enable precision diagnostics across heterogeneous neurological diseases.

Neuroinflammation and humoral immunity represent interdependent therapeutic targets across MS, AD, PD, ALS, and stroke, but their relative importance varies by disease biology and stage, defining distinct treatment strategies and therapeutic windows (Table 6).

In MS, given the central role of autoreactive lymphocytes, B cell–mediated immunity, trained innate immune responses, and compartmentalized CNS inflammation, therapeutic strategies primarily aim to suppress adaptive immune activation while increasingly targeting chronic neuroinflammatory processes. Treatment has classically focused on adaptive immunity through disease-modifying therapies such as anti-CD20 B cell depletion, S1P modulators, interferons, and glatiramer acetate [71]. However, expanding evidence indicates that innate immune reprogramming (“trained immunity”) and compartmentalized CNS inflammation contribute to progression [72]. This has shifted attention toward brain-border immune niches (meninges, choroid plexus, perivascular spaces) and local immune regulation, including microglial modulation, Treg-based approaches, and emerging cell therapies. These strategies are increasingly integrated with microbiome modulation and BBB-preserving approaches [73]. Clinically, biomarker-guided personalization (e.g., B-cell profiles, cytokines, NfL) and combination approaches targeting both adaptive and innate pathways are becoming central, particularly in progressive MS, where controlling smouldering inflammation remains a key challenge [9, 66].
In AD, because Aβ accumulation initiates downstream tau pathology, microglial activation, and neuroinflammatory signaling, current therapeutic approaches focus on reducing amyloid burden while expanding toward multi-target interventions addressing neuroinflammation and neurodegeneration. Disease-modifying therapy has advanced with anti-amyloid monoclonal antibodies such as lecanemab and donanemab, which reduce amyloid burden and modestly slow clinical decline [74, 75]. However, the limited magnitude of clinical benefit has reinforced the view that monotherapy is insufficient, and that AD requires combination strategies targeting tau pathology, neuroinflammation, and vascular dysfunction [76, 77]. Treatment implementation depends on biomarker-based patient selection and careful safety monitoring (e.g., amyloid-related imaging abnormalities [ARIA] [78], while blood-based biomarkers increasingly support scalable detection and follow-up [50]. The field is moving toward integrated, multi-target interventions aligned with disease stage and biology.
In PD, as α-synuclein aggregation and microglia-driven innate immune activation contribute to progressive neuronal loss, emerging therapies increasingly target both pathological protein accumulation and associated inflammatory pathways. Therapeutic development is increasingly oriented toward microglial modulation and innate immune signaling pathways, particularly those linked to α-synuclein [79]. Pathological α-synuclein activates inflammatory cascades (e.g., NOD2/RIPK2), offering novel immunotherapeutic targets [21]. Early, even presymptomatic, intervention is considered critical, supported by biomarker frameworks such as translocator protein (TSPO) positron emission tomography (PET) and fluid markers [14, 56]. Alongside symptomatic dopaminergic therapy, approaches targeting neuroinflammation, including miRNA regulation and monoclonal antibodies against α-synuclein, are being explored, although clinical efficacy remains variable [80]. Neuromodulation strategies (e.g., deep brain stimulation) further illustrate the bidirectional interaction between neural circuits and immune signaling [81].
In ALS, given the contribution of protein aggregation, complement activation, and dysregulated innate and adaptive immune responses to motor neuron degeneration, therapeutic development is shifting toward integrated multi-mechanistic interventions. ALS represents a paradigm of therapeutic complexity and heterogeneity, driving a shift toward multi-mechanistic and adaptive trial designs. Current strategies aim to combine modulation of innate inflammation, enhancement of Treg function, and targeting of protein aggregation [23, 82]. Complement activation (C1q, C3) has emerged as a key mechanism, supporting the development of complement inhibitors, while additional approaches include antisense therapies and metabolic interventions [82]. Biomarker-based stratification and adaptive platform trials are increasingly used to improve efficiency and personalize treatment allocation [82]. Despite progress, therapeutic translation remains challenging, requiring careful safety monitoring and better alignment of interventions with disease subtypes.
In stroke, because ischemic injury triggers BBB disruption, innate immune activation, cytokine release, and secondary inflammatory damage, therapeutic strategies aim to limit acute tissue injury while modulating post-stroke neuroinflammation. Immunotherapy is fundamentally shaped by timing and the acute nature of injury. Beyond reperfusion, therapeutic strategies target cytokine pathways (e.g., IL-1, IL-6), promote anti-inflammatory signaling (IL-10, TGF-β), and aim to preserve BBB integrity to limit secondary damage, including IgG extravasation [83]. Modulation of neutrophil, microglial, and T-cell responses, particularly γδ T cells and IL-17 pathways, represents additional avenues [84, 85]. Biomarkers such as NLR, PLR, SII, and cytokine panels support risk stratification and may guide treatment timing [83, 85]. However, variability and rapid temporal dynamics require precise, stage-specific intervention strategies.

Cross‑disease clinical framework and shared themes

Across diseases, several shared principles emerge (Table 7). First, peripheral immunomodulation is most effective in early or acute stages (e.g., MS, stroke), whereas microglial and astrocyte targeting becomes critical in chronic neurodegeneration (AD, PD, ALS, progressive MS) [86, 87]. Second, humoral therapies, particularly monoclonal antibodies, are most effective where pathogenic proteins or B-cell–mediated mechanisms are central, as in MS and AD. Third, preserving BBB integrity and limiting secondary inflammatory amplification confer cross-disease benefits.

Importantly, therapeutic efficacy increasingly depends on biomarker-driven precision approaches, integrating fluid and imaging markers (e.g., NfL, cytokines, complement, PET imaging, TSPO) to align treatment with disease stage and immunopathological endotype. This has led to a shift toward combination immunotherapy, reflecting the polymechanistic nature of neurological diseases, such as combining anti-amyloid and anti-inflammatory treatments in AD, adaptive and innate targeting in MS, dopaminergic and immunomodulatory approaches in PD, or complement and T-cell modulation in ALS [71, 77, 79, 82].
Finally, these advances highlight the need for robust clinical infrastructure and safety frameworks, including monitoring for treatment-related complications (e.g., ARIA, infection risk, immunosuppression), and emphasize shared decision-making and long-term follow-up as integral components of modern neuroimmunological therapy [88].

Distinguishing pathogenic from bystander antibodies in the inflamed CNS

A persistent challenge is distinguishing antibodies that actively drive neuroinflammation from those that arise secondarily due to tissue injury or immune activation. Pathogenic antibodies typically bind extracellular neuronal or glial targets and induce direct functional effects, such as receptor internalization, complement activation, or microglial fragment crystallizable receptor-mediated synaptic stripping, thereby amplifying neuroinflammatory cascades involving NLRP3 inflammasome activation, TNF-α/IL-1β release, and ROS production [3, 7, 32, 89]. In contrast, many “bystander” antibodies recognize intracellular antigens exposed during neurodegeneration and correlate more with debris-driven microglial phagocytosis, astrocyte A1 polarization, and complement tagging rather than causal pathogenicity [89]. Intrathecal synthesis accompanied by elevated CSF cytokines (e.g., CXCL13, IL-6), microglial NLRP3 inflammasome activation, and complement deposition strengthens evidence of pathogenicity, while isolated low-titre serum findings rarely indicate active neuroinflammatory disease [3, 7]. These findings complicate a binary assignment of antibodies as “driver” or “epiphenomenon”, and suggest that cellular state, ligand avidity, and local complement availability determine whether humoral signals amplify or merely accompany neuroinflammation [7]. Distinguishing these categories remains essential for neurological diseases where neuroinflammation and humoral immunity coexist, including MS and neurodegenerative diseases.

Assay variability and standardization problem

Classical immunoassays such as enzyme-linked immunosorbent assay (ELISA), chemiluminescent enzyme immunoassay (CLEIA), and single molecule array (SIMOA) remain central to fluid-based detection, offering high sensitivity and clinical validation [65, 67]. However, assay variability complicates interpretation in neurological diseases where neuroinflammation and humoral activity intersect. Interpretation of autoantibody results remains limited by assay design (antigen conformation, live vs. fixed substrates), secondary reagents (complement-containing vs. IgG1-specific detection), and threshold definitions. Live cell-based assays outperform fixed assays for sensitivity while maintaining high specificity [90, 91]. Recent real-world evidence shows that flow-cytometric live-cell immunofluorescence offers superior sensitivity compared with fixed-cell assays [90]. Insufficient assay specificity risks misinterpreting systemic immune background activity as CNS autoimmunity, while inadequate sensitivity may result in the loss of genuinely pathogenic signals [90, 91]. Harmonized protocols, external quality controls, and orthogonal validation are especially important for antibodies whose pathogenicity depends on conformational epitopes that modulate glial activation, BBB integrity, or local complement amplification [7]. Without such standardization, non-specific inflammatory background in serum can be misinterpreted as disease-relevant autoimmunity.

Interpretation of serum vs. CSF findings

Distinguishing serum from CSF antibody profiles is crucial in neurological diseases where central neuroinflammation drives compartmentalized immune responses [92]. Antibodies detected only in serum may indicate systemic immune activation rather than CNS pathology, whereas CSF-restricted antibodies typically coexist with evidence of intrathecal inflammation, such as OCBs, CXCL13 elevation, microglial activation markers, and BBB compromise [92, 93]. For example, in diseases with strong neuroinflammatory components, such as MS, CSF findings more accurately reflect pathogenic processes, including microglial cytokine release, inflammasome signaling, and complement-mediated injury [19]. Elevated CXCL13 in lesions and CSF correlates with B cell aggregates and immunoglobulin synthesis, and its index (CSF/serum adjustment) has prognostic value for subsequent MS activity [92]. Differentiating systemic humoral activity from CNS-compartmentalized inflammation remains essential to minimize diagnostic pitfalls.

Current gaps in neuroinflammatory biomarkers beyond proteinopathy

Despite advances in profiling humoral immunity, many neurological diseases lack validated biomarkers that capture the interplay between antibodies and neuroinflammation. In ALS and PD, reproducible extracellular autoantibodies remain elusive, even though neuroinflammatory processes such as microglial activation (TSPO, CSF1R), astrocyte A1 conversion, mitochondrial dysfunction, and complement cascade engagement are well documented [33, 34, 94]. Similarly, in AD, fluid biomarkers predominantly reflect proteinopathy rather than inflammatory or humoral mechanisms, leaving a gap in tools capturing complement-mediated synaptic loss, BBB dysfunction, B cell activity, or cytokine-driven microglial activation [52, 53]. The lack of validated biomarkers linking humoral and innate inflammatory mechanisms highlights the need for integrated neuroimmune indicators.

Conflicting roles for humoral immunity in neurodegenerative diseases

Neurodegenerative disorders present conflicting data on how humoral immunity interacts with neuroinflammation. Autoantibodies against tau, Aβ, α-synuclein, and TDP-43 have been variably reported as pathogenic, protective, or merely epiphenomenal, reflecting the complexity of inflammatory microenvironments characterized by activated microglia, complement deposition, NLRP3 activation, BBB dysfunction, and astrocyte reactivity [5-7, 31, 32, 95]. In AD, complement-driven microglial synaptic pruning exacerbates pathology, yet natural autoantibodies may enhance clearance of toxic species [6, 7, 31]. In PD, the relationship between α-synuclein antibodies and disease progression is modulated by microglial polarization (M1 vs. M2), inflammasome activation, cytokine milieu, and regional BBB permeability [33, 79]. Such heterogeneity underscores the need for integrative mechanistic studies linking humoral signals to spatially resolved neuroinflammatory microenvironments [8].
Humoral responses in AD, PD, and TDP-43 proteinopathies can be double-edged [6, 32, 95]. Complement-driven microglial pruning accelerates early synapse loss in AD mice, and inflammasome activation (NLRP3–caspase-1) promotes Aβ clearance failure and tau pathology [3, 7]. Conversely, antibody therapies in PD targeting α-synuclein (prasinezumab, cinpanemab) show acceptable safety and pharmacodynamic target engagement, but efficacy signals remain inconsistent across phases and endpoints [96, 97]. Meta-analyses of endogenous α-synuclein autoantibodies reveal small and heterogeneous effects, particularly in early PD, reinforcing the assay- and cohort-dependence of conclusions [33]. For TDP-43, whose pathological emergence was established only after 2006, the immune consequences are increasingly recognized, yet established extracellular antibody targets remain sparse. The field instead centers on cell-intrinsic RNA–protein pathology [95, 98]. The outcome is that spatial context (parenchyma vs. meninges vs. vasculature), glial state, and biophysical features of aggregates likely determine whether humoral immunity is pathogenic, neutral, or protective at a given disease stage [95, 98].

Methodological limitations

The controversy persists due to three unresolved methodological limitations. First, many biomarkers reduce microglial or astrocyte states to single readouts. Emerging radioligands and spatial -omics now enable mapping of pro-inflammatory vs. reparative glia in situ [94, 99]. Second, there is a lack of standardized multimodal panels that combine amyloid/tau/neurodegeneration with immune–vascular axes (complement, CXCL13 index, BBB permeability), which would clarify when humoral mechanisms are upstream drivers vs. downstream responders [93, 100]. Third, the field requires trial designs that incorporate immune phenotyping (autoantibody isotypes/affinity, complement activity, CSF chemokines) to explain heterogeneous outcomes in immunotherapies targeting misfolded proteins [7]. Without these advances, apparent contradictions will persist, reflecting measurement limitations rather than biology.

Single-cell multiomics of B cells in neurological diseases

Single-cell multiomics is transforming our understanding of how B cells integrate into neuroinflammatory architectures [101]. Studies reveal B cell and plasma-cell niches embedded within microglia-rich, complement-active, cytokine-dense environments, particularly in MS, where CSF and meningeal microenvironments show expansion of B lineage and plasma cells and disease-specific transcriptional states [66, 101]. In parallel, tertiary lymphoid structures in the meninges provide BAFF- and APRIL-dependent niches supporting B cell survival and intrathecal maturation, with links to severe cortical pathology and progression [10, 102]. These inflamed niches shape clonal expansion, somatic hypermutation, and antibody secretion, while microglia and astrocytes provide survival cues (BAFF, APRIL), antigen presentation, and pro-inflammatory signaling (IL-6, TNF-α) [66, 102]. Studies using scRNA/scBCR-seq in CSF, along with integrative compendium analyses, map clonal expansion and maturation of B and plasma cell populations, providing the substrate to connect clonotypes with local glial states and intrathecal cytokines and complement [101]. The lymphocyte–microglia–astrocyte axis, driven in part by C1q and complement, is prominent at chronically active lesion rims and frames how B-cell responses may interact with resident glia [71]. Linking B cell clonotypes to local glial activation states and complement deposition will clarify which humoral clones truly drive neuroinflammation [7, 71]. A key next step is to link B cell clonotypes in space and time to microglial (e.g. microglia inflamed in MS) and astroglial states and complement deposition, to distinguish truly pathogenic humoral clones from reactive or reparative ones [66].

High-sensitivity autoantibody profiling

Next-generation platforms (PhIP-Seq, REAP, microarrays, multiplex assays), recent advances in digital immunoassays, and fast protein liquid chromatography (FPLC) offer unprecedented sensitivity for detecting rare antibodies associated with neuroinflammatory processes such as microglial fragment crystallizable receptor activation, astrocyte cytotoxicity, and complement amplification [65, 103-105]. As sensitivity increases, functional validation is essential to determine whether identified antibodies modulate neuronal excitability, induce inflammasome activation, disrupt BBB integrity, amplify synaptic pruning, or interact with glial cytokine networks [3, 7, 32]. As these platforms become clinically scalable, they may distinguish disease-driving humoral signatures from background reactivity generated during chronic glial activation or proteinopathy-associated inflammation [106].

Novel targets for PET imaging

Beyond TSPO, emerging PET tracers aim to visualize neuroinflammatory–humoral interactions in vivo by targeting B cell markers (CD19, CD20), complement activation fragments (C1q, C3b), and microglial states (CSF1R, P2X7, P2Y12) [99]. Tracers marking pro-inflammatory microglia have yielded multicenter data in PD and early signals in MS, though kinetic and quantification challenges remain to be addressed [56, 99, 107]. Progress on P2Y12 (homeostatic/anti-inflammatory microglia) has produced the first brain-permeable tracers, with preclinical and early clinical indications of BBB penetration and phenotype sensitivity [108]. Imaging CSF1R, a regulator of microglial survival, also offers a potential alternative to TSPO for quantifying microglial mass and state [94]. In combination with ultra-high field MRI markers (e.g., paramagnetic rim lesions (PRL)) and complement-targeted trials, such tracers may enable spatial mapping and illuminate how antibody-producing niches interface with inflammatory microenvironments characterized by microglial-mediated synaptic pruning, astrocyte reactivity, and cytokine release [7].
Overcoming challenges in BBB penetration and tracer kinetics is essential, but immuno-PET has the potential to map neuroimmune circuits relevant for both antibody-mediated and purely inflammatory disorders [8, 107].

Personalized immune profiling and precision medicine

Personalized immune profiling integrates humoral immunity (antibody specificity, clonotypes, isotypes), neuroinflammation (microglial activation markers, CSF cytokine and complement signatures), and imaging signals to stratify patients and guide targeted therapy [9, 66]. This approach enables precise targeting of B-cell depletion, complement inhibition, cytokine blockade, or neuroprotective anti-inflammatory strategies [71]. A personalized immune profile should integrate (i) humoral features (specificities, clonotypes, isotypes or affinity), (ii) intrathecal biomarkers (CXCL13 index, OCBs, complement), (iii) microglial or astrocyte activation readouts, and (iv) multimodal imaging, to define endotypes and guide targeted therapy [92]. This approach is particularly impactful where antibody-driven and neuroinflammatory mechanisms coexist, such as in MS and AD with complement-mediated synapse loss [7, 66]. In AD, anti-Aβ antibodies (e.g., lecanemab) require risk stratification for ARIA (apolipoprotein E ε4 [APOE4] status, antithrombotics, prior microhemorrhages) and longitudinal monitoring, highlighting the need for immune safety panels within precision medicine workflows [74, 78, 88]. Longitudinal immune fingerprinting could also predict treatment response or adverse inflammatory complications such as ARIA [78].

Integrative models combining humoral, cellular, and neuroimaging data

Multimodal machine learning (ML/DL models) integrating antibody profiles, B cell receptor repertoires, cytokine networks, microglial activation states, complement markers, MRI changes (including PRL and atrophy), and next-generation PET signals (TSPO, P2X7, P2Y12, CSF1R) could provide unprecedented insight into how humoral immunity interacts with neuroinflammation to drive pathology [107-111]. Incorporating biological priors (e.g., glial inflammasome pathways, complement-dependent synaptic pruning, BBB breakdown, lymphoid follicle formation) improves interpretability and helps distinguish causal inflammatory processes from reactive changes [110]. In AD, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-based multimodal work shows the benefits of fusing PET/MRI with blood biomarkers to predict amyloid burden and support early diagnosis—a template for similar integrations in neuroimmune diseases [50, 110, 111]. Such integrative models are poised to accelerate biomarker discovery, refine patient endotypes, and enhance precision therapy optimization across neuroimmune and neurodegenerative diseases [8].

Neuroinflammation and humoral immunity form an integrated, bidirectional framework underpinning pathology in MS, AD, PD, ALS, and stroke. All five neurological diseases show a clear, age-dependent increase in incidence. Ageing remains the most powerful epidemiological driver for the rising incidence of major neurological diseases. It not only increases incidence and prevalence but also modulates disease phenotype, treatment responsiveness, and progression.
Although humoral and neuroinflammatory biomarkers have advanced disease characterization, enabling earlier detection, patient stratification, and treatment monitoring, current limitations in specificity, assay standardization, and cross-platform reproducibility restrict widespread clinical use. The most reliable signals (such as neurofilaments in ALS, OCBs in MS, amyloid/tau panels and glial markers in AD) demonstrate the value of aligning biomarkers with biological proximity to core mechanisms. Further progress depends on harmonized protocols, multicenter longitudinal validation, and adoption of integrated analytical models that combine fluid biomarkers with imaging, genomics, and clinical phenotyping.
Therapeutically, rational combinations, anti-amyloid plus anti-tau/anti-inflammatory (in AD), disease-modifying therapies plus innate reprogramming and CNS-niche targeting (in MS), dopaminergic therapy plus microglia-focused immunomodulation (in PD), complement/regulatory T cell (Treg) strategies within adaptive platform trials (in ALS), and timed cytokine blockade with BBB preservation (in stroke)—should be guided by stage-specific and endotype-specific biomarker profiles. Robust safety frameworks (e.g., ARIA monitoring, infection risk management, and immunosuppression oversight) and shared decision-making are essential for clinical translation.
Future directions emphasize single-cell B cell multiomics, high-sensitivity autoantibody profiling, immuno-PET targets, and personalized immune “fingerprinting”. Ultimately, a precision neuroimmunology paradigm, grounded in standardized, composite biomarker panels and integrative analytics, offers the most credible path to disease modification and improved patient-centred outcomes.

Acknowledgments

Special thanks to Prof. Gülderen Yanıkkaya Demirel and EU Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant–AUTOIGG, H2020-MSCA-RISE-2017 for bringing us together. Also, many thanks to Dr Vukica Vujic.

Author contributions

The author contributed solely to the article.

Availability of data and materials

Not applicable.

Financial support and sponsorship

Supported by the Ministry of Education, Science and Technological Development of RS, Contract No. 451-03-34/2026-03/200178, and PRISMA Grant #4242 “NIMOCHIP” Science Fund RS.

Conflicts of interest

All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

AI and AI-assisted tools statement

No AI tool was used to generate the scientific content, analysis, or conclusions of the article. ChatGPT (OpenAI) was used for language and figure editing.

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