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Editorial

From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory

by
Masaru Tanaka
1,* and
Simone Battaglia
2,3
1
Danube Neuroscience Research Laboratory, HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Tisza Lajos krt. 113, H-6725 Szeged, Hungary
2
Center for Studies and Research in Cognitive Neuroscience, Department of Psychology “Renzo Canestrari”, Cesena Campus, Alma Mater Studiorum, University of Bologna, 47521 Bologna, Italy
3
Department of Psychology, University of Turin, 10124 Turin, Italy
*
Author to whom correspondence should be addressed.
Biomedicines 2025, 13(9), 2226; https://doi.org/10.3390/biomedicines13092226
Submission received: 31 July 2025 / Accepted: 9 September 2025 / Published: 10 September 2025
(This article belongs to the Special Issue Crosstalk between Depression, Anxiety, Dementia, and Chronic Pain: Comorbidity in Behavioral Neurology and Neuropsychiatry Volume III)
Versions Notes

1. Setting the Stage: Mood, Memory and Pain in Clinical Convergence

Mounting evidence situates mood disturbance, memory decline, and chronic pain within a single neuro-immune conversation [1,2]. Cytokines, glial activation, and hypothalamic-pituitary-adrenal signals braid nociceptive and limbic circuits, so a flare in one domain can echo as affective dysregulation or cognitive drift [3,4,5]. Yet biology tells only half the story; social support, fear-avoidance beliefs, and lifestyle patterns modulate this signaling milieu, amplifying or dampening vulnerability [6,7]. Multimodal neuroimaging and plasma proteomics now reveal convergent fingerprints across depression, anxiety, dementia, and pain, hinting at transdiagnostic biomarkers and inviting predictive models that respect context [1,3,8,9,10]. Appreciating this intricate biopsychosocial weave is the first step toward interventions that target both the molecule and the milieu [11,12,13].
Despite rapid expansion of multimodal datasets across depression, anxiety, dementia, and chronic pain, robust transdiagnostic biomarkers remain elusive [14,15]. Meta-analytic neuroimaging reveals both shared and disorder-specific network alterations, while inflammatory and self-referential markers fragment across symptom clusters, underscoring heterogeneity that defeats single-signature diagnostics [8,16,17,18,19]. Clinical translation falters further when biomarker readouts shift with body mass, activity, sleep, medication exposure, and broader lifestyle ecologies; in some cohorts, sickness behavior outperforms cytokines for explaining psychological comorbidity [14,15,20,21]. These inconsistencies demand predictive modeling pipelines that integrate dimensional symptoms, multimodal biology, and environmental context to support individualized risk stratification and adaptive care pathways [15,16,22,23,24]. Without such integrative frameworks, precision interventions will continue to lag behind need in practice [14,15,25,26,27].
This editorial pursues four interconnected goals. First, we integrate preclinical and clinical evidence that maps convergent neuro-immune circuitry linking affective, cognitive, and nociceptive phenotypes. Second, we appraise the most promising, yet still provisional, transdiagnostic biomarkers—from network-level signatures to inflammatory proteomes—assessing their reproducibility and translational readiness. Third, we interrogate how lifestyle and environmental exposures, such as diet, physical activity, and psychosocial enrichment, recalibrate these pathways and modulate symptom trajectories. Finally, we outline robust predictive-modeling architectures that fuse multimodal biology with contextual data, ultimately offering a roadmap toward individually tailored prevention and treatment strategies for co-occurring mood, memory, and pain disorders.

2. Where the Gaps Were

Despite prolific datasets, the biological choreography linking affective dysregulation, mnemonic decline, and nociception remains speculative [28,29,30,31,32]. Neuro-immune signals correlate with symptom clusters, yet causal arrows are blurred by cross-sectional snapshots that average out temporal dynamics [28,33,34]. Most human studies still lean on single magnetic resonance imaging (MRI) scans and one-off cytokine panels, analytical conveniences that miss diurnal swings in network excitability or immune tone [28,33,34,35,36]. Consequently, conflicting associations proliferate and mechanistic narratives stall [28,30,33,37,38]. Progress will require multi-scale frameworks in which cell-type gene expression, circuit oscillations, and whole-brain connectomes are integrated within longitudinal designs, permitting inference on how perturbations ripple upward and downward across the neuro-immune hierarchy [28,30,34,39,40].
Longitudinal knowledge is perilously thin. Outside a handful of decade-long registries, few cohorts follow patients as mood symptoms, cognitive drift, and pain wax and wane across treatment cycles; without these trajectories, models cannot learn temporal signatures of risk or recovery [41,42,43]. At the same time, granular information that wearables, smartphones, and in-home sensors could supply remains largely untapped, leaving laboratory snapshots to stand in for lived experience [44,45,46]. When candidate biomarkers do emerge, translation stalls within siloed pipelines: discovery groups rarely collaborate with device engineers, regulators, frontline clinicians, and health-system decision-makers, so results languish in preclinical vaults. These fissures collectively stifle predictive algorithms, thwart stratified trials, and delay the timely delivery of precision care [44,47,48].
Five deficits frame the road ahead: first, a lack of mechanistic alignment linking molecular signals to whole-brain networks [49,50,51]; second, the absence of large, harmonized longitudinal cohorts to chart symptom trajectories [50,52,53]; third, biomarker panels that ignore contextual nuance and remain unstandardized [50,53,54]; fourth, predictive algorithms seldom stress-tested across diverse populations [49,50,52]; and fifth, fragile pipelines that fail to convert discoveries into patient-centered tools [49,52,54] (Table 1).

3. Snapshot of the Ten Papers

3.1. Chronic Pain and Mechanisms

Zhang and colleagues uncover a unidirectional molecular cascade where spinal neurotrophic tyrosine kinase receptor type 1 (NTRK1) drives insulin-like growth factor II (IGF2) expression, fueling endoplasmic reticulum (ER) stress and sustaining chronic postsurgical pain [55]. Using the skin/muscle incision–retraction (SMIR) rat model, they show that blocking tropomyosin receptor kinase A (TrkA) or silencing IGF2 breaks this pathogenic loop. Their findings highlight a novel, non-opioid target for precision pain therapy. In this prospective real-world study, Della Vecchia and colleagues reveal that anti-calcitonin gene-related peptide/receptor (CGRP/R) monoclonal antibodies do more than just ease migraine pain [56]. They also alleviate depression, anxiety, fatigue, and allodynia in treatment-resistant patients. Interestingly, even non-responders showed some psychiatric improvement, hinting at deeper neuroaffective effects worth exploring. (Table 2).

3.2. Comorbidity and Cognitive Decline

This study brings fresh light to an urgent issue: how gum health might reflect brain health. Using data from the PerioMind Colombia Cohort, the authors show that specific periodontal indices—like gingival redness and pocket depth—closely align with mild cognitive impairment in older adults [57]. Their models suggest dentists could become frontline detectors of cognitive decline. This compelling study explores how comorbidities influence the transition from mild cognitive impairment to dementia. By clustering patients based on their comorbidity profiles, the authors uncover striking risk patterns—diabetes and obesity sharply accelerate decline, while traumatic brain injury shows surprisingly low progression [58]. It is a sharp call to personalize dementia risk management. This study offers a fresh lens on predicting dementia. Tracking 132 individuals with mild neurocognitive disorder over two years, the authors crafted a model that nails an 83.7% accuracy rate [59]. As for risk, it rises with alcohol and a higher body mass index (BMI). In terms of protection, female sex, strong praxis, and surprisingly, higher scores on the geriatric depression scale (GDS) prevail. This study uses simple inputs to obtain powerful foresight.

3.3. Neuro-Immune Stress Circuitry

Smagin and colleagues dive deep into the biological aftermath of social stress [60]. Using a robust mouse model, they reveal striking shifts in blood plasma proteins linked to depression and neurodegeneration. What stands out? A telltale signature of amyloid and apolipoproteins—mirroring patterns seen in Alzheimer’s. A compelling read. This elegant review explores how neuroinflammation, oxidative stress, and mitochondrial dysfunction converge in major depression—and how phytochemicals might disrupt that vicious cycle [61]. Drawing from both bench and bedside, the authors highlight natural compounds like curcumin and flavonoids as promising multitarget tools for mood modulation, with fewer side effects and broader neuroprotective impact than traditional drugs [65].

3.4. Lifestyle and Biopsychosocial Angles

In this timely study, the authors reveal how being inactive physically can turn up the volume on the connection between anxiety, depression, and neck pain in breast cancer survivors. Surprisingly, this link does not hold for low back pain [62]. Their message is clear: movement matters—and not just for the body, but the mind too. This timely and thorough review dives deep into the tangled web of chronic orofacial pain [63]. It challenges outdated diagnostic norms and champions a biopsychosocial lens—merging the International Classification of Diseases, Eleventh Revision (ICD-11); the International Classification of Headache Disorders, Third Edition (ICHD-3); the International Classification of Orofacial Pain (ICOP); and the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. This way, the authors spark a necessary rethinking of classification and care, urging a unified, patient-centered approach.

3.5. Neurodevelopment and Novel Targets

Samra and colleagues thrust the carotenoid astaxanthin into the autism spotlight [60]. In valproic acid (VPA)-exposed rats, the pigment steered the renegade renin–angiotensin axis toward its protective arm, muted Notch signaling and nuclear factor kappa B (NF-κB) activation, untangled microtubule-associated protein tau, softened gliosis, and, crucially, coaxed the pups back into sociability. Biology and behavior clicked in one tidy preclinical arc.

4. Bridges Built: How These Studies Advance the Field

By deploying the SMIR rat paradigm, investigators traced a spinal NTRK1-triggered cascade that amplifies IGF2 expression and culminates in maladaptive ER stress, a trio now validated as driver of postsurgical pain [55]. Parallel real-world trials with anti-CGRP/R monoclonals show that targeting sensory peptides simultaneously softens migraine, depression, anxiety, and mechanical allodynia [56]. Together these studies fuse molecular neurobiology with bedside pain management, sketching an immediate roadmap for non-opioid interventions and broader psychiatric benefit in chronic headache populations [66,67,68].
Periodontal indices now function as surrogate biomarkers of neural integrity, with gingival redness, pocket depth, and alveolar loss tracking early cognitive slippage [57]. Clustering comorbidities such as diabetes, obesity, and cardiovascular burden sharpens dementia risk stratification by exposing synergistic rather than additive trajectories [58]. This cross-pollination between dentistry, neurology, and geriatric psychiatry recasts the dental clinic as a cognitive sentinel, enabling nuanced risk profiles that drive personalized interventions and feed next-generation prediction algorithms [57,58,59].
Plasma proteomic profiling after controlled social stress uncovers a signature that bridges cytokine flux with neurodegenerative cascades [60,69]. Parallel animal and cell studies show curcumin, quercetin, and related phytochemicals dampening microglial activation and improving mitochondrial function, positioning natural compounds as innovative neuroimmune modulators [61,70]. Integrating immunologic analytics with psychiatric phenotyping clarifies how inflammatory and mitochondrial crosstalk drives depressive symptoms [71]. These converging insights nominate biomarkers for depression and prodromal Alzheimer’s disease while advancing low-toxicity phytochemical therapeutics over conventional antidepressants [72,73,74].
By isolating physical inactivity within post-cancer cohorts, targeted analyses compute novel multivariate paths, exposing sedentarism as a catalyst of the anxiety-depression-pain triad in breast cancer survivors [62,75,76]. Psychology, oncology, and rehabilitation converge, refreshing the biopsychosocial paradigm [77,78,79]. Such transdisciplinary clarity reorients clinical priorities. Evidence thus advocates early, personalized activity prescriptions to blunt cascading affective and nociceptive burdens and to integrate mental-health prevention into routine oncology pain care [75,77,78,80].
By fusing ICD-11, ICHD-3, ICOP, and DSM-5 within a biopsychosocial scaffold, these studies recalibrate neurodevelopmental disorder nosology, exposing phenotypes that transcend categorical silos [63,81,82]. Such conceptual innovation invites neurologists, psychiatrists, dentists, and developmental scientists into a shared space, aligning biomarkers with behavioral heuristics and unveiling novel therapeutic targets [81,83]. Clinically, patient-centered algorithms and interdisciplinary guidelines emerge, accelerating therapeutics from bench to bedside [81,82,84].

5. Still Unpaved Roads: Future Research Directions

5.1. Multi-Omics Mega-Cohorts

Big data only matters if it stays in step with biology [85,86,87]. The next wave should launch mega-cohorts that blend single-cell transcriptomics, proteomics, metabolomics, and connectomics with repeated neuroimaging and biosamples every three to six months [85,88,89,90]. Such “rolling deep phenotypes” will let us watch how a flare of neuropathic pain, a dip in mood, or a slip in recall leaves footprints from microglia to default-mode hubs [91,92,93]. Harmonized pipelines and open cloud workspaces will finally allow algorithms to trace directional signals, validating the mechanistic hypotheses flagged in the gaps above and building a living atlas that evolves with each new participant—and across diverse ancestries, socioeconomic contexts, and life stages, capturing latent resilience alongside vulnerability [94,95].

5.2. Longitudinal Pain-Mood Tracking and Lifestyle-Targeted Randomized Controlled Trials (RCTs)

Pair those omics streams with real-world longitudinal tracking. Smartphone diaries, passive gait sensors, and cross-platform ecological momentary assessment (EMA) can capture minute-to-minute swings in pain intensity, affect, sleep, and cognitive fog [96,97,98,99]. By feeding those trajectories into adaptive, lifestyle-targeted randomized controlled trials (RCTs)—think exercise-timing, personalized nutrition, light therapy, or mindfulness-dose algorithms—we can test causal levers in near real time [98,100]. Each participant becomes a micro-trial, and aggregated n-of-1 readouts will reveal who benefits, when, and why [100]. This approach does more than shrink sample sizes; it closes the discovery-translation loop, letting mechanistic insights instantly inform behavioral prescriptions delivered at the wrist [98,100].

5.3. Cross-Talking Neuro-Immune Combination Therapies

The final gap is therapeutic: neuro-immune dialog rarely respects disciplinary borders. Hybrid interventions that co-modulate cytokine tone and neural circuit excitability—such as low-dose interleukin (IL)-1 antagonists paired with transcranial alternating current, vagus-paced breathing, or gut-microbiota modulators—could rewrite maladaptive set points [101,102,103]. We need factorial trials that cross these modalities, mapped against the multi-omics atlas, to pinpoint synergistic nodes [104,105,106]. A regulatory sandbox shared by neuroscientists, immunologists, and digital-health engineers would speed iteration, while federated analytics guard privacy. Success would move us from symptom silos to network rewiring, turning the still-unpaved road into an integrated highway toward precision recovery [107].

6. Take-Home Message

Together, these ten papers narrow down the critical mechanistic and translational gaps—further developing our understanding of how mood, memory, and pain braid through neuro-immune circuitry—while simultaneously prying open fresh investigative frontiers. From spinal NTRK1-IGF2 loops that propel postsurgical hyperalgesia to gingival biomarkers that foreshadow cognitive drift, this collection delivers concrete answers yet sparks new questions about bidirectional brain–body crosstalk. Proteomic signatures, connectomic shifts, and wearable-captured behaviors now coalesce into a reproducible risk compass, steering clinicians toward earlier, kinder interventions. Lifestyle, comorbidity, and social context emerge not as confounders but as tunable levers, suggesting that a step counter or food log can be as informative as a positron emission tomography (PET) scan. The horizon beckons with multi-omics cohorts tethered to smartphone phenotyping, adaptive n-of-1 trials that turn each patient into their own laboratory, and combination neuro-immune therapies poised to retune both cytokine tone and cortical rhythm. With the stage now set, the next wave of precision relief is already cresting.

Author Contributions

Conceptualization, M.T. and S.B.; methodology, M.T.; software, M.T.; validation, M.T. and S.B.; formal analysis, M.T. and S.B.; investigation, M.T. and S.B.; resources, M.T.; data curation, M.T.; writing—original draft preparation, M.T.; writing—review and editing, M.T. and S.B.; visualization, M.T.; supervision, M.T.; project administration, M.T. and S.B.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by HUN-REN Hungarian Research Network funding to M.T. S.B. is supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)—a multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11 October 2022)—and Bial Foundation, Portugal (235/22). The views and opinions expressed are solely those of the authors and do not necessarily reflect those of the European Union, nor can the European Union be held responsible for them.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
CGRP/RCalcitonin gene-related peptide/receptor
DSM-5Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
EREndoplasmic reticulum
IGF2insulin-like growth factor II
ICD-11International Classification of Diseases, Eleventh Revision
ICHD-3International Classification of Headache Disorders, Third Edition
ICOPInternational Classification of Orofacial Pain
NTRK1Neurotrophic tyrosine kinase receptor type 1

References

  1. Li, M.; She, K.; Zhu, P.; Li, Z.; Liu, J.; Luo, F.; Ye, Y. Chronic Pain and Comorbid Emotional Disorders: Neural Circuitry and Neuroimmunity Pathways. Int. J. Mol. Sci. 2025, 26, 436. [Google Scholar] [CrossRef]
  2. Krsek, A.; Ostojic, L.; Zivalj, D.; Baticic, L. Navigating the Neuroimmunomodulation Frontier: Pioneering Approaches and Promising Horizons—A Comprehensive Review. Int. J. Mol. Sci. 2024, 25, 9695. [Google Scholar] [CrossRef]
  3. Sălcudean, A.; Bodo, C.R.; Popovici, R.A.; Cozma, M.M.; Păcurar, M.; Crăciun, R.E.; Crisan, A.I.; Enatescu, V.R.; Marinescu, I.; Cimpian, D.M.; et al. Neuroinflammation—A Crucial Factor in the Pathophysiology of Depression—A Comprehensive Review. Biomolecules 2025, 15, 502. [Google Scholar] [CrossRef]
  4. Mokhtari, T.; Irandoost, E.; Sheikhbahaei, F. Stress, pain, anxiety, and depression in endometriosis-Targeting glial activation and inflammation. Int. Immunopharmacol. 2024, 132, 111942. [Google Scholar] [CrossRef]
  5. Szabó, Á.; Galla, Z.; Spekker, E.; Szűcs, M.; Martos, D.; Takeda, K.; Ozaki, K.; Inoue, H.; Yamamoto, S.; Toldi, J.; et al. Oxidative and Excitatory Neurotoxic Stresses in CRISPR/Cas9-Induced Kynurenine Aminotransferase Knockout Mice: A Novel Model for Despair-Based Depression and Post-Traumatic Stress Disorder. Front. Biosci. (Landmark Ed) 2025, 30, 25706. [Google Scholar] [CrossRef]
  6. Yin, W.; Swanson, S.P.; Biltz, R.G.; Goodman, E.J.; Gallagher, N.R.; Sheridan, J.F.; Godbout, J.P. Unique brain endothelial profiles activated by social stress promote cell adhesion, prostaglandin E2 signaling, hypothalamic-pituitary-adrenal axis modulation, and anxiety. Neuropsychopharmacology 2022, 47, 2271–2282. [Google Scholar] [CrossRef] [PubMed]
  7. Kokkosis, A.G.; Madeira, M.M.; Hage, Z.; Valais, K.; Koliatsis, D.; Resutov, E.; Tsirka, S.E. Chronic psychosocial stress triggers microglial-/macrophage-induced inflammatory responses leading to neuronal dysfunction and depressive-related behavior. GLIA 2024, 72, 111–132. [Google Scholar] [CrossRef]
  8. Brandl, F.; Weise, B.; Mulej Bratec, S.; Jassim, N.; Hoffmann Ayala, D.; Bertram, T.; Ploner, M.; Sorg, C. Common and specific large-scale brain changes in major depressive disorder, anxiety disorders, and chronic pain: A transdiagnostic multimodal meta-analysis of structural and functional MRI studies. Neuropsychopharmacology 2022, 47, 1071–1080. [Google Scholar] [CrossRef] [PubMed]
  9. Sim, M.A.; Doecke, J.D.; Liew, O.W.; Wong, L.L.; Tan, E.S.J.; Chan, S.P.; Chong, J.R.F.; Cai, Y.; Hilal, S.; Venketasubramanian, N.; et al. Plasma proteomics for cognitive decline and dementia—A Southeast Asian cohort study. Alzheimer’s Dement. 2025, 21, e14577. [Google Scholar] [CrossRef]
  10. Tanaka, M.; Battaglia, S. Dualistic Dynamics in Neuropsychiatry: From Monoaminergic Modulators to Multiscale Biomarker Maps. Biomedicines 2025, 13, 1456. [Google Scholar] [CrossRef] [PubMed]
  11. Lin, H.; Liu, C.; Hu, A.; Zhang, D.; Yang, H.; Mao, Y. Understanding the immunosuppressive microenvironment of glioma: Mechanistic insights and clinical perspectives. J. Hematol. Oncol. 2024, 17, 31. [Google Scholar] [CrossRef]
  12. Fang, J.; Lu, Y.; Zheng, J.; Jiang, X.; Shen, H.; Shang, X.; Lu, Y.; Fu, P. Exploring the crosstalk between endothelial cells, immune cells, and immune checkpoints in the tumor microenvironment: New insights and therapeutic implications. Cell Death Dis. 2023, 14, 586. [Google Scholar] [CrossRef] [PubMed]
  13. Martos, D.; Lőrinczi, B.; Szatmári, I.; Vécsei, L.; Tanaka, M. Decoupling Behavioral Domains via Kynurenic Acid Analog Optimization: Implications for Schizophrenia and Parkinson’s Disease Therapeutics. Cells 2025, 14, 973. [Google Scholar] [CrossRef]
  14. Carvalho, A.F.; Solmi, M.; Sanches, M.; Machado, M.O.; Stubbs, B.; Ajnakina, O.; Sherman, C.; Sun, Y.R.; Liu, C.S.; Brunoni, A.R.; et al. Evidence-based umbrella review of 162 peripheral biomarkers for major mental disorders. Transl. Psychiatry 2020, 10, 152. [Google Scholar] [CrossRef]
  15. McQuaid, R.J. Transdiagnostic biomarker approaches to mental health disorders: Consideration of symptom complexity, comorbidity and context. Brain Behav. Immun. Health 2021, 16, 100303. [Google Scholar] [CrossRef]
  16. Franklyn, S.I.; Stewart, J.; Beaurepaire, C.; Thaw, E.; McQuaid, R.J. Developing symptom clusters: Linking inflammatory biomarkers to depressive symptom profiles. Transl. Psychiatry 2022, 12, 133. [Google Scholar] [CrossRef] [PubMed]
  17. Łoś, K.; Waszkiewicz, N. Biological Markers in Anxiety Disorders. J. Clin. Med. 2021, 10, 1744. [Google Scholar] [CrossRef]
  18. Battaglia, S.; Servajean, P.; Friston, K.J. The paradox of the self-studying brain. Phys. Life Rev. 2025, 52, 197–204. [Google Scholar] [CrossRef]
  19. Gregorio, F.D.; Battaglia, S. The intricate brain-body interaction in psychiatric and neurological diseases. Adv. Clin. Exp. Med. 2024, 33, 321–326. [Google Scholar] [CrossRef] [PubMed]
  20. Sabbah, S.G.; Northoff, G. The self in depression and anxiety as a transdiagnostic and differential-diagnostic neural marker: A systematic review. Neurosci. Biobehav. Rev. 2025, 169, 106034. [Google Scholar] [CrossRef]
  21. Goldsmith, D.R.; Bekhbat, M.; Mehta, N.D.; Felger, J.C. Inflammation-Related Functional and Structural Dysconnectivity as a Pathway to Psychopathology. Biol. Psychiatry 2023, 93, 405–418. [Google Scholar] [CrossRef] [PubMed]
  22. Prompiengchai, S.; Dunlop, K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024, 50, 230–245. [Google Scholar] [CrossRef]
  23. Lobato-Delgado, B.; Priego-Torres, B.; Sanchez-Morillo, D. Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis. Cancers 2022, 14, 3215. [Google Scholar] [CrossRef]
  24. Tanaka, M. From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care. Biomedicines 2025, 13, 167. [Google Scholar] [CrossRef]
  25. Ahmed, Z. Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum. Genom. 2020, 14, 35. [Google Scholar] [CrossRef]
  26. Abdelaziz, E.H.; Ismail, R.; Mabrouk, M.S.; Amin, E. Multi-omics data integration and analysis pipeline for precision medicine: Systematic review. Comput. Biol. Chem. 2024, 113, 108254. [Google Scholar] [CrossRef]
  27. Tanaka, M.; Szatmári, I.; Vécsei, L. Quinoline Quest: Kynurenic Acid Strategies for Next-Generation Therapeutics via Rational Drug Design. Pharmaceuticals 2025, 18, 607. [Google Scholar] [CrossRef] [PubMed]
  28. Hagenberg, J.; Brückl, T.M.; Erhart, M.; Kopf-Beck, J.; Ködel, M.; Rehawi, G.; Röh-Karamihalev, S.; Sauer, S.; Yusupov, N.; Rex-Haffner, M.; et al. Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation. Brain Behav. Immun. 2025, 123, 353–369. [Google Scholar] [CrossRef]
  29. Richards, J.H.; Freeman, D.D.; Detloff, M.R. Myeloid Cell Association with Spinal Cord Injury-Induced Neuropathic Pain and Depressive-like Behaviors in LysM-eGFP Mice. J. Pain 2024, 25, 104433. [Google Scholar] [CrossRef] [PubMed]
  30. Hanč, P.; Gonzalez, R.J.; Mazo, I.B.; Wang, Y.; Lambert, T.; Ortiz, G.; Miller, E.W.; von Andrian, U.H. Multimodal control of dendritic cell functions by nociceptors. Science 2023, 379, eabm5658. [Google Scholar] [CrossRef]
  31. Battaglia, S.; Fazio, C.D.; Borgomaneri, S.; Avenanti, A. Cortisol Imbalance and Fear Learning in PTSD: Therapeutic Approaches to Control Abnormal Fear Responses. Curr. Neuropharmacol. 2025, 23, 835–846. [Google Scholar] [CrossRef] [PubMed]
  32. Battaglia, S.; Andero, R.; Thayer, J.F. Translational cross-species evidence of heart-related dynamics in threat learning. Neurosci. Biobehav. Rev. 2025, 176, 106273. [Google Scholar] [CrossRef] [PubMed]
  33. Sass, D.; Saligan, L.; Fitzgerald, W.; Berger, A.M.; Torres, I.; Barb, J.J.; Kupzyk, K.; Margolis, L. Extracellular vesicle associated and soluble immune marker profiles of psychoneurological symptom clusters in men with prostate cancer: An exploratory study. Transl. Psychiatry 2021, 11, 440. [Google Scholar] [CrossRef]
  34. Vinkers, C.H.; Kuzminskaite, E.; Lamers, F.; Giltay, E.J.; Penninx, B. An integrated approach to understand biological stress system dysregulation across depressive and anxiety disorders. J. Affect. Disord. 2021, 283, 139–146. [Google Scholar] [CrossRef]
  35. Lee, A.T.; Chang, E.F.; Paredes, M.F.; Nowakowski, T.J. Large-scale neurophysiology and single-cell profiling in human neuroscience. Nature 2024, 630, 587–595. [Google Scholar] [CrossRef]
  36. Hoang, N.; Sardaripour, N.; Ramey, G.D.; Schilling, K.; Liao, E.; Chen, Y.; Park, J.H.; Bledsoe, X.; Landman, B.A.; Gamazon, E.R.; et al. Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale. PLoS Biol. 2024, 22, e3002782. [Google Scholar] [CrossRef] [PubMed]
  37. Emani, P.S.; Liu, J.J.; Clarke, D.; Jensen, M.; Warrell, J.; Gupta, C.; Meng, R.; Lee, C.Y.; Xu, S.; Dursun, C.; et al. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384, eadi5199. [Google Scholar] [CrossRef]
  38. van den Oord, E.; Aberg, K.A. Fine-grained cell-type specific association studies with human bulk brain data using a large single-nucleus RNA sequencing based reference panel. Sci. Rep. 2023, 13, 13004. [Google Scholar] [CrossRef]
  39. Alemán-Gómez, Y.; Griffa, A.; Houde, J.C.; Najdenovska, E.; Magon, S.; Cuadra, M.B.; Descoteaux, M.; Hagmann, P. A multi-scale probabilistic atlas of the human connectome. Sci. Data 2022, 9, 516. [Google Scholar] [CrossRef]
  40. Luo, C.; Liu, H.; Xie, F.; Armand, E.J.; Siletti, K.; Bakken, T.E.; Fang, R.; Doyle, W.I.; Stuart, T.; Hodge, R.D.; et al. Single nucleus multi-omics identifies human cortical cell regulatory genome diversity. Cell Genom. 2022, 2, 100107. [Google Scholar] [CrossRef]
  41. Balliu, B.; Douglas, C.; Seok, D.; Shenhav, L.; Wu, Y.; Chatzopoulou, D.; Kaiser, W.; Chen, V.; Kim, J.; Deverasetty, S. Personalized mood prediction from patterns of behavior collected with smartphones. NPJ Digit. Med. 2024, 7, 49. [Google Scholar] [CrossRef] [PubMed]
  42. Asare, K.O.; Moshe, I.; Terhorst, Y.; Vega, J.; Hosio, S.; Baumeister, H.; Pulkki-Råback, L.; Ferreira, D. Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive Mob. Comput. 2022, 83, 101621. [Google Scholar] [CrossRef]
  43. Rykov, Y.G.; Ng, K.P.; Patterson, M.D.; Gangwar, B.A.; Kandiah, N. Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning. Comput. Biol. Med. 2024, 180, 108959. [Google Scholar] [CrossRef]
  44. Shah, R.V.; Grennan, G.; Zafar-Khan, M.; Alim, F.; Dey, S.; Ramanathan, D.; Mishra, J. Personalized machine learning of depressed mood using wearables. Transl. Psychiatry 2021, 11, 338. [Google Scholar] [CrossRef]
  45. Li, B.; Sano, A. Extraction and interpretation of deep autoencoder-based temporal features from wearables for forecasting personalized mood, health, and stress. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2020, 4, 49. [Google Scholar] [CrossRef]
  46. Chen, J.; Abbod, M.; Shieh, J.S. Pain and Stress Detection Using Wearable Sensors and Devices—A Review. Sensors 2021, 21, 1030. [Google Scholar] [CrossRef]
  47. Patel, J.; Hung, C.; Katapally, T.R. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: A systematic review. Psychiatry Res. 2025, 343, 116277. [Google Scholar] [CrossRef] [PubMed]
  48. Vitali, D.; Olugbade, T.; Eccleston, C.; Keogh, E.; Bianchi-Berthouze, N.; Williams, A.C.d.C. Sensing behavior change in chronic pain: A scoping review of sensor technology for use in daily life. Pain 2024, 165, 1348–1360. [Google Scholar] [CrossRef] [PubMed]
  49. Dura-Bernal, S.; Herrera, B.; Lupascu, C.; Marsh, B.M.; Gandolfi, D.; Marasco, A.; Neymotin, S.; Romani, A.; Solinas, S.; Bazhenov, M.; et al. Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons. J. Neurosci. 2024, 44, e1236242024. [Google Scholar] [CrossRef]
  50. Xie, L.; Das, S.R.; Li, Y.; Wisse, L.E.M.; McGrew, E.; Lyu, X.; DiCalogero, M.; Shah, U.; Ilesanmi, A.; Denning, A.E.; et al. A multi-cohort study of longitudinal and cross-sectional Alzheimer’s disease biomarkers in cognitively unimpaired older adults. Alzheimer’s Dement. 2025, 21, e14492. [Google Scholar] [CrossRef]
  51. Suárez, L.E.; Markello, R.D.; Betzel, R.F.; Misic, B. Linking Structure and Function in Macroscale Brain Networks. Trends Cogn. Sci. 2020, 24, 302–315. [Google Scholar] [CrossRef]
  52. McDonnell, E.I.; Xie, S.; Marder, K.; Cui, F.; Wang, Y. Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks. Stat. Med. 2024, 43, 4131–4147. [Google Scholar] [CrossRef]
  53. Lori, A.; Schultebraucks, K.; Galatzer-Levy, I.; Daskalakis, N.P.; Katrinli, S.; Smith, A.K.; Myers, A.J.; Richholt, R.; Huentelman, M.; Guffanti, G.; et al. Transcriptome-wide association study of post-trauma symptom trajectories identified GRIN3B as a potential biomarker for PTSD development. Neuropsychopharmacology 2021, 46, 1811–1820. [Google Scholar] [CrossRef]
  54. Maric, D.; Jahanipour, J.; Li, X.R.; Singh, A.; Mobiny, A.; Van Nguyen, H.; Sedlock, A.; Grama, K.; Roysam, B. Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks. Nat. Commun. 2021, 12, 1550. [Google Scholar] [CrossRef] [PubMed]
  55. Zhang, C.; Zhang, K.; Zhang, W.; Jiao, B.; Cao, X.; Yu, S.; Zhang, M.; Zhang, X. Unidirectional Crosstalk Between NTRK1 and IGF2 Drives ER Stress in Chronic Pain. Biomedicines 2025, 13, 1632. [Google Scholar] [CrossRef]
  56. Della Vecchia, A.; De Luca, C.; Becattini, L.; Curto, L.; Ferrari, E.; Siciliano, G.; Gori, S.; Baldacci, F. Beyond Pain Relief: Unveiling the Multifaceted Impact of Anti-CGRP/R mAbs on Comorbid Symptoms in Resistant Migraine Patients. Biomedicines 2024, 12, 677. [Google Scholar] [CrossRef]
  57. Arévalo-Caro, C.; López, D.; Sánchez Milán, J.A.; Lorca, C.; Mulet, M.; Arboleda, H.; Losada Amaya, S.; Serra, A.; Gallart-Palau, X. Periodontal Indices as Predictors of Cognitive Decline: Insights from the PerioMind Colombia Cohort. Biomedicines 2025, 13, 205. [Google Scholar] [CrossRef]
  58. Menegon, F.; De Marchi, F.; Aprile, D.; Zanelli, I.; Decaroli, G.; Comi, C.; Tondo, G. From Mild Cognitive Impairment to Dementia: The Impact of Comorbid Conditions on Disease Conversion. Biomedicines 2024, 12, 1675. [Google Scholar] [CrossRef] [PubMed]
  59. Tsiakiri, A.; Bakirtzis, C.; Plakias, S.; Vlotinou, P.; Vadikolias, K.; Terzoudi, A.; Christidi, F. Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data. Biomedicines 2024, 12, 1232. [Google Scholar] [CrossRef] [PubMed]
  60. Smagin, D.A.; Bezryadnov, D.V.; Zavialova, M.G.; Abramova, A.Y.; Pertsov, S.S.; Kudryavtseva, N.N. Blood Plasma Markers in Depressed Mice under Chronic Social Defeat Stress. Biomedicines 2024, 12, 1485. [Google Scholar] [CrossRef] [PubMed]
  61. Figueiredo Godoy, A.C.; Frota, F.F.; Araújo, L.P.; Valenti, V.E.; Pereira, E.; Detregiachi, C.R.P.; Galhardi, C.M.; Caracio, F.C.; Haber, R.S.A.; Fornari Laurindo, L.; et al. Neuroinflammation and Natural Antidepressants: Balancing Fire with Flora. Biomedicines 2025, 13, 1129. [Google Scholar] [CrossRef]
  62. Grande, G.H.D.; Ferrero-Hernández, P.; de Oliveira, L.R.; Ramos, V.; Palma, M.R.; Vidal, R.; Oishi, A.; Ferrari, G.; Christofaro, D.G.D. Physical Inactivity Amplifies the Link Between Anxiety, Depression, and Neck Pain in Breast Cancer Survivors. Biomedicines 2025, 13, 1089. [Google Scholar] [CrossRef]
  63. Canfora, F.; Ottaviani, G.; Calabria, E.; Pecoraro, G.; Leuci, S.; Coppola, N.; Sansone, M.; Rupel, K.; Biasotto, M.; Di Lenarda, R.; et al. Advancements in Understanding and Classifying Chronic Orofacial Pain: Key Insights from Biopsychosocial Models and International Classifications (ICHD-3, ICD-11, ICOP). Biomedicines 2023, 11, 3266. [Google Scholar] [CrossRef]
  64. Samra, A.I.; Kamel, A.S.; Abdallah, D.M.; El Fattah, M.A.A.; Ahmed, K.A.; El-Abhar, H.S. Preclinical Evidence for the Role of the Yin/Yang Angiotensin System Components in Autism Spectrum Disorder: A Therapeutic Target of Astaxanthin. Biomedicines 2023, 11, 3156. [Google Scholar] [CrossRef] [PubMed]
  65. Barbalho, S.M.; Leme Boaro, B.; da Silva Camarinha Oliveira, J.; Patočka, J.; Barbalho Lamas, C.; Tanaka, M.; Laurindo, L.F. Molecular Mechanisms Underlying Neuroinflammation Intervention with Medicinal Plants: A Critical and Narrative Review of the Current Literature. Pharmaceuticals 2025, 18, 133. [Google Scholar] [CrossRef]
  66. Sacco, S.; Amin, F.M.; Ashina, M.; Bendtsen, L.; Deligianni, C.I.; Gil-Gouveia, R.; Katsarava, Z.; MaassenVanDenBrink, A.; Martelletti, P.; Mitsikostas, D.D.; et al. European Headache Federation guideline on the use of monoclonal antibodies targeting the calcitonin gene related peptide pathway for migraine prevention—2022 update. J. Headache Pain. 2022, 23, 67. [Google Scholar] [CrossRef] [PubMed]
  67. Messina, R.; Huessler, E.M.; Puledda, F.; Haghdoost, F.; Lebedeva, E.R.; Diener, H.C. Safety and tolerability of monoclonal antibodies targeting the CGRP pathway and gepants in migraine prevention: A systematic review and network meta-analysis. Cephalalgia 2023, 43, 3331024231152169. [Google Scholar] [CrossRef] [PubMed]
  68. Russo, A.F.; Hay, D.L. CGRP physiology, pharmacology, and therapeutic targets: Migraine and beyond. Physiol. Rev. 2023, 103, 1565–1644. [Google Scholar] [CrossRef]
  69. Tanaka, M.; Battaglia, S.; Liloia, D. Navigating Neurodegeneration: Integrating Biomarkers, Neuroinflammation, and Imaging in Parkinson’s, Alzheimer’s, and Motor Neuron Disorders. Biomedicines 2025, 13, 1045. [Google Scholar] [CrossRef]
  70. de Lima, E.P.; Laurindo, L.F.; Catharin, V.C.S.; Direito, R.; Tanaka, M.; Jasmin Santos German, I.; Lamas, C.B.; Guiguer, E.L.; Araújo, A.C.; Fiorini, A.M.R.; et al. Polyphenols, Alkaloids, and Terpenoids Against Neurodegeneration: Evaluating the Neuroprotective Effects of Phytocompounds Through a Comprehensive Review of the Current Evidence. Metabolites 2025, 15, 124. [Google Scholar] [CrossRef]
  71. Juhász, L.; Spisák, K.; Szolnoki, B.Z.; Nászai, A.; Szabó, Á.; Rutai, A.; Tallósy, S.P.; Szabó, A.; Toldi, J.; Tanaka, M.; et al. The Power Struggle: Kynurenine Pathway Enzyme Knockouts and Brain Mitochondrial Respiration. J. Neurochem. 2025, 169, e70075. [Google Scholar] [CrossRef] [PubMed]
  72. Darwish, S.F.; Elbadry, A.M.M.; Elbokhomy, A.S.; Salama, G.A.; Salama, R.M. The dual face of microglia (M1/M2) as a potential target in the protective effect of nutraceuticals against neurodegenerative diseases. Front. Aging 2023, 4, 1231706. [Google Scholar] [CrossRef]
  73. Tsai, C.F.; Chen, G.W.; Chen, Y.C.; Shen, C.K.; Lu, D.Y.; Yang, L.Y.; Chen, J.H.; Yeh, W.L. Regulatory Effects of Quercetin on M1/M2 Macrophage Polarization and Oxidative/Antioxidative Balance. Nutrients 2021, 14, 67. [Google Scholar] [CrossRef]
  74. Hooshmand, M.; Asoodeh, A. Coadministration of Monophosphoryl Lipid and Curcumin Modulates Neuroprotective Effects in LPS Stimulated Rat Primary Microglial Cells. Oxid. Med. Cell Longev. 2024, 2024, 9422312. [Google Scholar] [CrossRef]
  75. Doré, I.; Plante, A.; Peck, S.S.; Bedrossian, N.; Sabiston, C.M. Physical activity and sedentary time: Associations with fatigue, pain, and depressive symptoms over 4 years post-treatment among breast cancer survivors. Support. Care Cancer 2022, 30, 785–792. [Google Scholar] [CrossRef]
  76. Hirschey, R.; Bryant, A.L.; Macek, C.; Battaglini, C.; Santacroce, S.; Courneya, K.S.; Walker, J.S.; Avishai, A.; Sheeran, P. Predicting physical activity among cancer survivors: Meta-analytic path modeling of longitudinal studies. Health Psychol. 2020, 39, 269–280. [Google Scholar] [CrossRef]
  77. Andersen, B.L.; Lacchetti, C.; Ashing, K.; Berek, J.S.; Berman, B.S.; Bolte, S.; Dizon, D.S.; Given, B.; Nekhlyudov, L.; Pirl, W.; et al. Management of Anxiety and Depression in Adult Survivors of Cancer: ASCO Guideline Update. J. Clin. Oncol. 2023, 41, 3426–3453. [Google Scholar] [CrossRef]
  78. Sun, M.; Liu, C.; Lu, Y.; Zhu, F.; Li, H.; Lu, Q. Effects of Physical Activity on Quality of Life, Anxiety and Depression in Breast Cancer Survivors: A Systematic Review and Meta-analysis. Asian Nurs. Res. (Korean Soc. Nurs. Sci.) 2023, 17, 276–285. [Google Scholar] [CrossRef]
  79. Van Dijck, S.; De Groef, A.; Kothari, J.; Dams, L.; Haenen, V.; Roussel, N.; Meeus, M. Barriers and facilitators to physical activity in cancer survivors with pain: A systematic review. Support. Care Cancer 2023, 31, 668. [Google Scholar] [CrossRef] [PubMed]
  80. Blount, D.S.; McDonough, D.J.; Gao, Z. Effect of Wearable Technology-Based Physical Activity Interventions on Breast Cancer Survivors’ Physiological, Cognitive, and Emotional Outcomes: A Systematic Review. J. Clin. Med. 2021, 10, 2015. [Google Scholar] [CrossRef] [PubMed]
  81. Michelini, G.; Carlisi, C.O.; Eaton, N.R.; Elison, J.T.; Haltigan, J.D.; Kotov, R.; Krueger, R.F.; Latzman, R.D.; Li, J.J.; Levin-Aspenson, H.F.; et al. Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks? Incorporating a new neurodevelopmental spectrum. World Psychiatry 2024, 23, 333–357. [Google Scholar] [CrossRef]
  82. Gaebel, W.; Stricker, J.; Kerst, A. Changes from ICD-10 to ICD-11 and future directions in psychiatric classification. Dialogues Clin. Neurosci. 2020, 22, 7–15. [Google Scholar] [CrossRef] [PubMed]
  83. Morris-Rosendahl, D.J.; Crocq, M.A. Neurodevelopmental disorders-the history and future of a diagnostic concept. Dialogues Clin. Neurosci. 2020, 22, 65–72. [Google Scholar] [CrossRef]
  84. Stein, D.J.; Szatmari, P.; Gaebel, W.; Berk, M.; Vieta, E.; Maj, M.; de Vries, Y.A.; Roest, A.M.; de Jonge, P.; Maercker, A.; et al. Mental, behavioral and neurodevelopmental disorders in the ICD-11: An international perspective on key changes and controversies. BMC Med. 2020, 18, 21. [Google Scholar] [CrossRef]
  85. Sturchio, A.; Marsili, L.; Vizcarra, J.A.; Dwivedi, A.K.; Kauffman, M.A.; Duker, A.P.; Lu, P.; Pauciulo, M.W.; Wissel, B.D.; Hill, E.J.; et al. Phenotype-Agnostic Molecular Subtyping of Neurodegenerative Disorders: The Cincinnati Cohort Biomarker Program (CCBP). Front. Aging Neurosci. 2020, 12, 553635. [Google Scholar] [CrossRef]
  86. Xu, C.; Lopez, R.; Mehlman, E.; Regier, J.; Jordan, M.I.; Yosef, N. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 2021, 17, e9620. [Google Scholar] [CrossRef]
  87. O’Hara, A.J.; Cohen, T.; Stancliffe, E.; Richardson, A.; Corney, D.; Turner, L.; Latif, H.; Zhou, G. Integrated multiomics unlocks holistic phenotypic insights allowing for deep molecular exploration. Cancer Res. 2025, 85, 3691. [Google Scholar] [CrossRef]
  88. Bringmann, M.; Imam, F.; Krish, V. The Global Neurodegeneration Proteomics Consortium-Biomarker and Drug Target Discovery Across> 40,000 Biosamples for AD, PD, ALS, FTD, and Aging. Alzheimer’s Dement. 2024, 20, e095579. [Google Scholar] [CrossRef]
  89. Ruzicka, W.B.; Mohammadi, S.; Fullard, J.F.; Davila-Velderrain, J.; Subburaju, S.; Tso, D.R.; Hourihan, M.; Jiang, S.; Lee, H.C.; Bendl, J.; et al. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384, eadg5136. [Google Scholar] [CrossRef]
  90. Dries, R.; Chen, J.; Del Rossi, N.; Khan, M.M.; Sistig, A.; Yuan, G.C. Advances in spatial transcriptomic data analysis. Genome Res. 2021, 31, 1706–1718. [Google Scholar] [CrossRef]
  91. Bonté, P.E.; Arribas, Y.A.; Merlotti, A.; Carrascal, M.; Zhang, J.V.; Zueva, E.; Binder, Z.A.; Alanio, C.; Goudot, C.; Amigorena, S. Single-cell RNA-seq-based proteogenomics identifies glioblastoma-specific transposable elements encoding HLA-I-presented peptides. Cell Rep. 2022, 39, 110916. [Google Scholar] [CrossRef]
  92. Dhir, S.; Derue, H.; Ribeiro-da-Silva, A. Temporal changes of spinal microglia in murine models of neuropathic pain: A scoping review. Front. Immunol. 2024, 15, 1460072. [Google Scholar] [CrossRef]
  93. Hiraga, S.I.; Itokazu, T.; Nishibe, M.; Yamashita, T. Neuroplasticity related to chronic pain and its modulation by microglia. Inflamm. Regen. 2022, 42, 15. [Google Scholar] [CrossRef] [PubMed]
  94. Tsuda, M.; Masuda, T.; Kohno, K. Microglial diversity in neuropathic pain. Trends Neurosci. 2023, 46, 597–610. [Google Scholar] [CrossRef]
  95. Liu, Y.; Cai, X.; Shi, B.; Mo, Y.; Zhang, J.; Luo, W.; Yu, B.; Li, X. Mechanisms and Therapeutic Prospects of Microglia-Astrocyte Interactions in Neuropathic Pain Following Spinal Cord Injury. Mol. Neurobiol. 2025, 62, 4654–4676. [Google Scholar] [CrossRef] [PubMed]
  96. de Vries, L.P.; Baselmans, B.M.L.; Bartels, M. Smartphone-Based Ecological Momentary Assessment of Well-Being: A Systematic Review and Recommendations for Future Studies. J. Happiness Stud. 2021, 22, 2361–2408. [Google Scholar] [CrossRef] [PubMed]
  97. Yim, S.J.; Lui, L.M.W.; Lee, Y.; Rosenblat, J.D.; Ragguett, R.M.; Park, C.; Subramaniapillai, M.; Cao, B.; Zhou, A.; Rong, C.; et al. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. J. Affect. Disord. 2020, 274, 602–609. [Google Scholar] [CrossRef]
  98. Mestdagh, M.; Verdonck, S.; Piot, M.; Niemeijer, K.; Kilani, G.; Tuerlinckx, F.; Kuppens, P.; Dejonckheere, E. m-Path: An easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice. Front. Digit. Health 2023, 5, 1182175. [Google Scholar] [CrossRef] [PubMed]
  99. Jacobson, N.C.; Bhattacharya, S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behav. Res. Ther. 2022, 149, 104013. [Google Scholar] [CrossRef]
  100. Russell, M.A.; Gajos, J.M. Annual Research Review: Ecological momentary assessment studies in child psychology and psychiatry. J. Child. Psychol. Psychiatry 2020, 61, 376–394. [Google Scholar] [CrossRef]
  101. Brem, S. Vagus nerve stimulation: Novel concept for the treatment of glioblastoma and solid cancers by cytokine (interleukin-6) reduction, attenuating the SASP, enhancing tumor immunity. Brain Behav. Immun. Health 2024, 42, 100859. [Google Scholar] [CrossRef]
  102. Herhaus, B. Alterations in pro-inflammatory cytokine TNF-alpha and stimulation of the cholinergic anti-inflammatory pathway in individuals with panic disorder using slow-paced breathing with HRV-BF–a randomized controlled trial. Psychoneuroendocrinology 2024, 160, 106751. [Google Scholar] [CrossRef]
  103. Hitch, T.C.A.; Hall, L.J.; Walsh, S.K.; Leventhal, G.E.; Slack, E.; de Wouters, T.; Walter, J.; Clavel, T. Microbiome-based interventions to modulate gut ecology and the immune system. Mucosal Immunol. 2022, 15, 1095–1113. [Google Scholar] [CrossRef] [PubMed]
  104. Di Gregorio, F.; Steinhauser, M.; Maier, M.E.; Thayer, J.F.; Battaglia, S. Error-related cardiac deceleration: Functional interplay between error-related brain activity and autonomic nervous system in performance monitoring. Neurosci. Biobehav. Rev. 2024, 157, 105542. [Google Scholar] [CrossRef]
  105. Battaglia, S.; Nazzi, C.; Di Fazio, C.; Borgomaneri, S. The role of pre-supplementary motor cortex in action control with emotional stimuli: A repetitive transcranial magnetic stimulation study. Ann. N. Y. Acad. Sci. 2024, 1536, 151–166. [Google Scholar] [CrossRef]
  106. Turrini, S.; Bevacqua, N.; Cataneo, A.; Chiappini, E.; Fiori, F.; Battaglia, S.; Romei, V.; Avenanti, A. Neurophysiological Markers of Premotor-Motor Network Plasticity Predict Motor Performance in Young and Older Adults. Biomedicines 2023, 11, 1464. [Google Scholar] [CrossRef]
  107. Tanaka, M.; He, Z.; Han, S.; Battaglia, S. Editorial: Noninvasive brain stimulation: A promising approach to study and improve emotion regulation. Front. Behav. Neurosci. 2025, 19, 1633936. [Google Scholar] [CrossRef] [PubMed]
Table 1. Key research deficits in translational neuropsychiatry. Five major gaps hinder progress in precision neuropsychiatric care, spanning mechanistic integration, longitudinal tracking, biomarker standardization, algorithmic robustness, and translational pipelines.
Table 1. Key research deficits in translational neuropsychiatry. Five major gaps hinder progress in precision neuropsychiatric care, spanning mechanistic integration, longitudinal tracking, biomarker standardization, algorithmic robustness, and translational pipelines.
DeficitReferences
1Lack of mechanistic alignment linking molecular signals to whole-brain networks[49,50,51]
2Absence of large, harmonized longitudinal cohorts to chart symptom trajectories[50,52,53]
3Biomarker panels that ignore contextual nuance and remain unstandardized[50,53,54]
4Predictive algorithms seldom stress-tested across diverse populations[49,50,52]
5Fragile pipelines that fail to convert discoveries into patient-centered tools[49,52,54]
Table 2. Thematic clustering of ten recent papers relevant to mood, memory, and pain convergence. CGRP/R, calcitonin gene-related peptide/receptor; ER, endoplasmic reticulum; IGF2, insulin-like growth factor II; NTRK1, neurotrophic tyrosine kinase receptor type 1.
Table 2. Thematic clustering of ten recent papers relevant to mood, memory, and pain convergence. CGRP/R, calcitonin gene-related peptide/receptor; ER, endoplasmic reticulum; IGF2, insulin-like growth factor II; NTRK1, neurotrophic tyrosine kinase receptor type 1.
Thematic GroupPaper Title (Shortened)References
Chronic Pain and Molecular MechanismUnidirectional Crosstalk Between NTRK1 and IGF2 Drives ER Stress in Chronic Pain[55]
Impact of Anti-CGRP/R mAbs on Comorbid Symptoms in Resistant Migraine[56]
Comorbidities and Cognitive DeclinePeriodontal Indices as Predictors of Cognitive Decline (PerioMind Cohort)[57]
Impact of Comorbid Conditions on Conversion from Mild Cognitive Impairment to Dementia[58]
Predictive Models for Progression to Major Neurocognitive Disorder[59]
Neuroinflammation, Stress, and DepressionBlood Plasma Markers in Mice under Chronic Social Defeat Stress[60]
Neuroinflammation and Natural Antidepressants[61]
Behavioral and Lifestyle Factors in Pain, Depression, and AnxietyPhysical Inactivity Amplifies Anxiety, Depression, and Neck Pain in Breast-Cancer Survivors[62]
Advances in Classifying Chronic Orofacial Pain via Biopsychosocial Models[63]
Neurodevelopmental Disorders and Novel Therapeutic TargetsYin/Yang Angiotensin System Components in Autism Spectrum Disorder; Astaxanthin as Therapy[64]
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Tanaka, M.; Battaglia, S. From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory. Biomedicines 2025, 13, 2226. https://doi.org/10.3390/biomedicines13092226

AMA Style

Tanaka M, Battaglia S. From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory. Biomedicines. 2025; 13(9):2226. https://doi.org/10.3390/biomedicines13092226

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Tanaka, Masaru, and Simone Battaglia. 2025. "From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory" Biomedicines 13, no. 9: 2226. https://doi.org/10.3390/biomedicines13092226

APA Style

Tanaka, M., & Battaglia, S. (2025). From Biomarkers to Behavior: Mapping the Neuroimmune Web of Pain, Mood, and Memory. Biomedicines, 13(9), 2226. https://doi.org/10.3390/biomedicines13092226

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