Background: In many disorders, metabolic and inflammatory derangements that originate in peripheral organs have a deleterious impact on the brain. Brain functional impairment, defined as hepatic encephalopathy, is one of the main diagnostic criteria for acute liver failure (ALF), a severe complication of acute liver injury (ALI). While brain inflammation (neuroinflammation) and metabolic alterations significantly contribute to hepatic encephalopathy, their non-invasive evaluation remains challenging. Methods: To address this limitation, we utilized dual radiotracer [18F]-fluoro-2-deoxy-2-D-glucose ([18F]FDG) and [11C]-peripheral benzodiazepine receptor ([11C]PBR28) microPET imaging followed by conjunction analysis and metabolic connectivity mapping. We applied this advanced methodology in mice with high dose acetaminophen (N-acetyl-p-aminophenol, APAP)-induced ALI, which can progress into ALF. Results: We observed hepatocellular damage, liver and systemic inflammation, and increased density of hippocampal microglia in mice with ALI. MicroPET imaging analysis characterized the presence of brain region-specific neuroinflammation and altered brain energy metabolism in mice with ALI. We also identified both gains and losses in connectivity, as well as a dual role of neuroinflammation. These results revealed brain "neuroinflammetabolic" signatures of ALI. Conclusion: These findings provide a platform for non-invasively diagnosing early signs of hepatic encephalopathy with the goal of informing timely diagnoses and targeted therapies. Our approach can be further utilized in non-invasive brain assessments in liver diseases and other disorders classically characterized by peripheral immune and metabolic dysregulation.

Dystonia is increasingly recognized as a disorder of brain networks. This review integrates multimodal evidence from human studies to characterize the network-level pathophysiology of dystonia. Structural MRI studies using voxel-based morphometry and diffusion imaging reveal alterations in gray matter volume and white matter connectivity across the sensorimotor cortex, basal ganglia, cerebellum, and thalamus. Functional imaging modalities, including PET, fMRI, EEG, MEG, and fNIRS, demonstrate aberrant activity and connectivity in cortico-striato-pallido-thalamocortical and cerebello-thalamocortical loops. Invasive electrophysiological recordings from deep brain stimulation (DBS) provide high-resolution insights into abnormal oscillatory activity and effective connectivity within these circuits. Non-invasive brain stimulation (NIBS) techniques such as TMS, TES, and TUS provide a means of actively interrogating those networks through transient perturbation. They also provide an avenue for personalized neuromodulation. Computational models, including The Virtual Brain platform, enable integration of multimodal data to simulate dynamic network behavior. Across focal, generalized, and genetic forms of dystonia, shared patterns of network dysfunction are observed, though phenotypic and genotypic subtypes exhibit distinct topographies and circuit-level alterations. These findings underscore the importance of network dysfunction underlying dystonia. This network perspective informs the development of more targeted and individualized diagnostic and therapeutic approaches, including circuit-guided neuromodulation and closed-loop brain stimulation. Advancing multimodal and integrative methodologies will be essential to unraveling the complex dynamics underlying dystonia and translating mechanistic insights into precision interventions.

Functional neuroimaging techniques are increasingly being used to advance the diagnosis and management of Parkinson's disease (PD). Methods such as [18F]-fluorodeoxyglucose positron emission tomography (FDG PET), resting-state functional magnetic resonance imaging (rs-fMRI), arterial spin labeling (ASL) MRI, and single-photon emission computed tomography (SPECT) enable the identification of disease-specific patterns like the PD-related pattern (PDRP) and PD cognition-related pattern (PDCP), which correlate with motor and cognitive symptoms. Network analysis using graph theory further elucidates the alterations in brain connectivity associated with PD, providing insights into disease progression and response to treatment. Moreover, these neuroimaging patterns assist in distinguishing PD from atypical parkinsonian syndromes, enhancing diagnostic accuracy. Understanding the impact of genetic variants like LRRK2 and GBA1 on functional connectivity highlights the potential for precision medicine in PD. As neuroimaging technologies evolve, their integration into clinical practice will be pivotal in the personalized management of PD, offering improved diagnostic precision and targeted therapeutic interventions.

BACKGROUND:Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, yet it remains under-recognised and misdiagnosed, which delays treatment, causes inaccurate prognosis and limits research opportunities. Imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive DLB biomarker. We evaluated a multivariate, quantifiable metabolic network biomarker, termed DLB-related pattern (DLBRP), for its further clinical translation across centres and disease stages. METHODS:We analysed demographic, clinical and FDG PET imaging data of 1180 participants from 14 tertiary centres and two multicentre datasets. We included 379 DLB, 28 mild cognitive impairment-LB (MCI-LB), 195 dementia due to Alzheimer's disease (ADD), 172 MCI-AD without α-synuclein co-pathology (MCI-AD-S-), and 73 MCI-AD with α-synuclein co-pathology (S+) patients, along with a comparative group of 333 normal controls (NCs). From the scans, we calculated the expression of DLBRP, AD-related pattern (ADRP) and Parkinson's disease-related pattern (PDRP) and compared them across groups. DLBRP scores were correlated with clinical measurements. RESULTS:Across independent cohorts, DLBRP robustly distinguished DLB from NCs (sensitivity >89%, specificity >90%), and scores correlated with Unified Parkinson's Disease Rating Scale Part III and independently predicted Mini-Mental State Examination. DLBRP was elevated already in MCI-LB. In a small longitudinal dataset, we observed steady increases in DLBRP expression with scores exceeding the diagnostic threshold prior to dementia onset. DLBRP and PDRP discriminated DLB from ADD (sensitivity, 74%-90%; specificity, 80%). In MCI-AD groups, ADRP was expressed, whereas DLBRP and PDRP were increased only in MCI-AD-S+, although comparatively less than in MCI-LB. CONCLUSIONS:This study demonstrates the value of DLBRP in diagnosing prodromal and manifest DLB and distinguishing them from their AD counterparts. While overlap between patterns may reflect actual co-pathology, this possibility cannot be accepted without thorough pathological confirmation. The current findings support the use of DLBRP in patient evaluation and in future trial design.

BACKGROUND AND PURPOSE:Our purpose was to identify spatial covariance patterns of dual-phase [18F]PI-2620 PET in biomarker-confirmed Alzheimer disease (AD) and healthy control subjects by applying data-driven multivariate analysis to multimodality neuroimaging data. We also tested the ability of pattern expression values in individual subjects to predict amyloid status and evaluate the efficacy of [18F]PI-2620 as a single, universal biomarker for the amyloid/tau/neurodegeneration (A/T/N) classification. MATERIALS AND METHODS:Twenty-five subjects (15 men, 10 women, mean age: 64.5 ± 10.1, range 51-89) including 15 with amyloid-positive AD and 10 amyloid-negative healthy controls were analyzed. Brain PET images of dual-phase [18F]PI-2620 (early-phase for cerebral perfusion; late-phase for tau pathology) and late-phase [18F]florbetaben for amyloid pathology were acquired in each participant alongside high-resolution brain MRI. PET images were converted into maps of standard uptake value ratio by using cerebellar GM as reference region. Spatial covariance analysis was performed separately in the standard brain space by using a well-established computing toolbox in the public domain. RESULTS:We identified distinct Alzheimer disease-related patterns (ADRP) of spatial covariance capturing prominent abnormal features in brain amyloid, tau, and perfusion (ADRP-amyloid, ADRP-tau, and ADRP-perfusion) underlying this disease. There was some overlap among these topographies particularly between ADRP-tau and ADRP-amyloid. ADRP expression scores of each pattern differentiated AD from healthy controls (P < .0001) with group discriminant analysis yielding an accuracy of 96% in predicting amyloid status with the combination of ADRP-tau and ADRP-perfusion scores. These scores correlated positively among themselves and with several clinical measures of disease severity in the combined subject group. CONCLUSIONS:Analysis of AD and normal controls suggests a potential role of [18F]PI-2620 as a single biomarker for the A/T/N classification.

Parkinson's disease (PD) is associated with substantial placebo effects. We used network analysis to identify a specific sham surgery-related pattern (SSRP) in metabolic PET data from a double-blind PD gene therapy trial. Baseline SSRP expression measured before randomization correlated with motor improvement under the blind after sham surgery. To validate this predictive relationship, we measured baseline SSRP levels in two independent placebo-controlled trials of pharmacologic PD treatments administered orally or by subcutaneous injection. As with sham surgery, pre-randomization SSRP expression correlated with placebo responses in each of the validation groups. Using magnetic resonance diffusion tensor imaging (DTI), we found that individual differences in SSRP expression and placebo response were attributable to variation in the density of fiber tracts linking key network nodes, particularly the nucleus accumbens and the anterior cingulate cortex. The findings support SSRP as a network-based imaging marker of placebo susceptibility in PD clinical trials.

Quantitative imaging markers to aid in the selection of Parkinson's disease (PD) patients for surgical interventions such as subthalamic nucleus deep brain stimulation (STN-DBS) are currently lacking. Using metabolic PET and network analysis we identified and validated a treatment-induced topography, termed STN StimNet. Stimulation-mediated changes in network expression correlated with concurrent motor improvement in independent STN-DBS cohorts scanned on and off stimulation. Moreover, STN StimNet measurements off stimulation correlated with local field potentials recorded from the STN, whereas intraoperative modulation of cortical activity by STN stimulation correlated with contributions to the network from corresponding brain regions. These findings suggested that stimulation-mediated clinical responses are influenced by baseline StimNet expression. Indeed, we found that motor outcomes following STN-DBS were predicted by preoperative network expression measured using metabolic PET or resting state fMRI. To illustrate the potential utility of these measures in selecting optimal candidates for DBS surgery, STN StimNet expression was computed in scans from 175 PD patients (0-21 years from diagnosis). The resulting values were used to identify those individuals likely to derive meaningful benefit from a potential STN-DBS procedure. This approach suggests that preoperative network quantification provides unique information regarding baseline brain circuitry, which may be useful in surgical decision making.

Neuroimaging with positron emission tomography (PET) has been instrumental in elucidating neurobiological mechanisms behind therapeutical trials in Parkinson's disease (PD). A variety of medical and neurosurgical interventions have been evaluated using many radioligands that reveal molecular basis for target engagement and brain responses in relation to clinical outcome measures. This review article describes major applications of metabolic brain network analysis in therapeutical studies in non-demented PD to restore functional abnormality by drug therapy, ablative lesioning, deep brain stimulation, gene therapy, and cell transplantation alongside placebo effects. The findings with brain network biomarkers using multivariate analysis are supported by regionally specific metabolic changes and clinical correlations detected by complementary univariate analysis. The review demonstrates a powerful methodology of combining multimodal neuroimaging data and network modeling approaches followed by some perspectives on future directions in this specialty area of translational research. Different neuroimaging biomarkers have been compared in light of recent advances in biofluid biomarkers. These efforts not only bring more precise understanding on mechanisms of action associated with different therapies, but also provide a road map for conducting successful clinical trials of emerging disease-modifying therapies in PD and related disorders. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The delusions and hallucinations that characterize Alzheimer’s disease psychosis (AD + P) are associated with violence towards caregivers and an accelerated cognitive and functional decline whose management relies on the utilization of medications developed for young people with schizophrenia. The development of novel therapies requires biomarkers that distinguish AD + P from non-psychotic Alzheimer’s disease. We investigated whether there might exist a brain metabolic network that distinguishes AD + P from non-psychotic Alzheimer’s disease that could be used as a biomarker to predict and track the course of AD + P for use in clinical trials. Utilizing F-18 fluorodeoxyglucose positron emission tomography scans from cohorts of cognitively healthy elderly (N = 174), those with Alzheimer’s disease without psychosis (N = 174) and those with AD + P (N = 88) participating in the Alzheimer’s Disease Neuroimaging Initiative study, we employed a convolutional neural network to identify and validate the Alzheimer’s Psychosis Network. We analysed network progression, clinical correlations and psychosis prediction using expression scores and network organization using graph theory. The Alzheimer’s Psychosis Network accurately distinguishes AD + P from controls (97%), with increasing scores correlating with cognitive decline. The Alzheimer’s Psychosis Network–based approach predicts psychosis in Alzheimer’s disease with 77% accuracy and identifies specific brain regions and connections associated with psychosis. Alzheimer’s Psychosis Network expression was found to be associated with increased cognitive and functional decline that characterizes AD + P. The increased metabolic connectivity between motor and language/social cognition regions in AD + P may drive delusions and agitated behaviour. Alzheimer’s Psychosis Network holds promise as a biomarker for AD + P, aiding in treatment development and patient stratification.

The Parkinson's disease (PD) cognition-related covariance pattern (PDCP) was derived from network analysis of metabolic positron emission tomography (PET). The expression score is a feasible imaging biomarker that correlates with neuropsychological test performance. Graph analysis within specific networks characterizes brain function, particularly in terms of assortativity, which reflects the tendency to connect to nodes with similar degree values. However, PDCP assortativity is unclear. A total of 102 patients with PD underwent neuropsychological testing and [ 18 F]-fluorodeoxyglucose PET. Patients scoring less than 130 on the Dementia Rating Scale-2 (DRS-2) were classified as having PD with dementia (PDD). PD with mild cognitive impairment in single or multiple functions (sMCI or mMCI) was defined as having one (sMCI) or two or more (mMCI) of the following criteria: DRS-2 subscore (Attention < 35, Initiation/Perseveration < 35, Construction < 6, Conceptualization < 35, Memory < 24), Wisconsin Card Sorting Test < 13 (executive), Hooper Visual Organization Test < 30 (visuospatial), and Boston Naming Test < 48 (language). The others were defined as having normal cognition (PD-NC). We identified 35 anatomical regions of interest (ROIs) as nodes corresponding to the PDCP network. The pairwise correlation at each node of normalized metabolic activity derived from FDG-PET data was calculated in each group by 100 bootstrapping iterations. The assortativity coefficient was calculated as the Pearson correlation coefficient of degrees between connected node pairs. Group differences were tested by repeated measures analysis of variance as group and threshold coefficients. There were 22 PD-NC, 24 sMCI, 31 mMCI, and 25 PDD. Demographic data are shown in Table 1. PDCP assortativity showed no significant differences between PD-NC and sMCI (P = 0.23). However, it was increased in mMCI and PDD compared to PD-NC and sMCI (P < 0.01). In addition, it was higher in PDD than in mMCI (P < 0.001) (Figure 1). Assortativity in the PDCP network is a potential predictor of the transition from MCI to dementia in PD patients. Furthermore, patients with cognitive impairment in multiple functions are at risk of developing dementia.

Rigidity, a cardinal symptom of Parkinson’s disease (PD), remains challenging to assess objectively. A torque-angle instrument was developed to quantify muscle tone, providing two parameters: bias difference and elastic coefficient. This study aimed to investigate the association of the instrument-measured rigidity with clinical assessments and brain function. In 30 patients with PD, the muscle tone in both arms was evaluated. Ten with wearing-off phenomenon were assessed twice, off and on condition. Twentynine patients underwent brain perfusion single-photon emission computed tomography (SPECT), and expression of PD-related covariance pattern (PDRP) was computed. Bias difference and elastic coefficient showed positive correlations with physician-rated rigidity (P < 0.002). Bias difference decreased after dopaminergic medication (P = 0.022) and was associated with lower body mass index (P = 0.012). Elastic coefficient positively correlated with the Unified PD Rating Scale Part III and PDRP scores (P < 0.044). Furthermore, the higher bias difference correlated with decreased sensory-motor cortex and increased substantia nigra perfusion (P < 0.001). The Torque-angle instrument is a viable tool for quantifying rigidity in PD. The bias difference reflects treatment responsiveness and is associated with the function in the sensory-motor cortex and substantia nigra. The elastic coefficient is indicative of overall Parkinsonism severity.

BACKGROUND:Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features. METHODS:The retrospective study included 41 PSP patients, 21 with Richardson's syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score). RESULTS:The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP. CONCLUSIONS:FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.

OBJECTIVE:Patients with Lewy body diseases have an increased risk of dementia, which is a significant predictor for survival. Posterior cortical hypometabolism on [18F]fluorodeoxyglucose positron emission tomography (PET) precedes the development of dementia by years. We therefore examined the prognostic value of cerebral glucose metabolism for survival. METHODS:We enrolled patients diagnosed with Parkinson's disease (PD), Parkinson's disease with dementia, or dementia with Lewy bodies who underwent [18F]fluorodeoxyglucose PET. Regional cerebral metabolism of each patient was analyzed by determining the expression of the PD-related cognitive pattern (Z-score) and by visual PET rating. We analyzed the predictive value of PET for overall survival using Cox regression analyses (age- and sex-corrected) and calculated prognostic indices for the best model. RESULTS:Glucose metabolism was a significant predictor of survival in 259 included patients (n = 118 events; hazard ratio: 1.4 [1.2-1.6] per Z-score; hazard ratio: 1.8 [1.5-2.2] per visual PET rating score; both p < 0.0001). Risk stratification with visual PET rating scores yielded a median survival of 4.8, 6.8, and 12.9 years for patients with severe, moderate, and mild posterior cortical hypometabolism (median survival not reached for normal cortical metabolism). Stratification into 5 groups based on the prognostic index revealed 10-year survival rates of 94.1%, 78.3%, 34.7%, 0.0%, and 0.0%. INTERPRETATION:Regional cerebral glucose metabolism is a significant predictor of survival in Lewy body diseases and may allow an earlier survival prediction than the clinical milestone "dementia." Thus, [18F]fluorodeoxyglucose PET may improve the basis for therapy decisions, especially for invasive therapeutic procedures like deep brain stimulation in Parkinson's disease. ANN NEUROL 2024;96:539-550.

Background:Acute liver injury (ALI) that progresses into acute liver failure (ALF) is a life-threatening condition with an increasing incidence and associated costs. Acetaminophen (N-acetyl-p-aminophenol, APAP) overdosing is among the leading causes of ALI and ALF in the Northern Hemisphere. Brain dysfunction defined as hepatic encephalopathy is one of the main diagnostic criteria for ALF. While neuroinflammation and brain metabolic alterations significantly contribute to hepatic encephalopathy, their evaluation at early stages of ALI remained challenging. To provide insights, we utilized post-mortem analysis and non-invasive brain micro positron emission tomography (microPET) imaging of mice with APAP-induced ALI. Methods:Male C57BL/6 mice were treated with vehicle or APAP (600 mg/kg, i.p.). Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), liver damage (using H&E staining), hepatic and serum IL-6 levels, and hippocampal IBA1 (using immunolabeling) were evaluated at 24h and 48h. Vehicle and APAP treated animals also underwent microPET imaging utilizing a dual tracer approach, including [11C]-peripheral benzodiazepine receptor ([11C]PBR28) to assess microglia/astrocyte activation and [18F]-fluoro-2-deoxy-2-D-glucose ([18F]FDG) to assess energy metabolism. Brain images were pre-processed and evaluated using conjunction and individual tracer uptake analysis. Results:APAP-induced ALI and hepatic and systemic inflammation were detected at 24h and 48h by significantly elevated serum ALT and AST levels, hepatocellular damage, and increased hepatic and serum IL-6 levels. In parallel, increased microglial numbers, indicative for neuroinflammation were observed in the hippocampus of APAP-treated mice. MicroPET imaging revealed overlapping increases in [11C]PBR28 and [18F]FDG uptake in the hippocampus, thalamus, and habenular nucleus indicating microglial/astroglial activation and increased energy metabolism in APAP-treated mice (vs. vehicle-treated mice) at 24h. Similar significant increases were also found in the hypothalamus, thalamus, and cerebellum at 48h. The individual tracer uptake analyses (APAP vs vehicle) at 24h and 48h confirmed increases in these brain areas and indicated additional tracer- and region-specific effects including hippocampal alterations. Conclusion:Peripheral manifestations of APAP-induced ALI in mice are associated with brain neuroinflammatory and metabolic alterations at relatively early stages of disease progression, which can be non-invasively evaluated using microPET imaging and conjunction analysis. These findings support further PET-based investigations of brain function in ALI/ALF that may inform timely therapeutic interventions.

Evaluate [18F]-fluorodopa (18F-DOPA) positron emission tomography (PET) signal 18 months post transplantation (6 months after cessation of immunosuppression) in 12 participants who received intraputamenal transplants of bemdaneprocel.

To understand how neuroinflammation is sustained in the absence of systemic inflammation we have studied a mouse model of neuropsychiatric lupus (NPSLE) which is triggered by the penetration of neurotoxic antibodies into the hippocampus. The subsequent activation of microglia leads to dendritic pruning of hippocampal neurons. This process becomes continuous as the neurons with reduced dendritic arborization secrete HMGB1 which contributes to neuroinflammation through two independent mechanisms. First, it binds NMDA receptors forming a bridge for C1q to decorate synapses to target them for pruning. Second, it activates microglia to secrete C1q. Thus, there is a feed forward loop with activated microglia engaging in dendritic pruning. The resulting neuronal damage leads to increased secretion of HMGB1, which in turn activates microglia. In mice, this inflammatory cycle can be sustained for at least a year. ACE inhibitors, which correct the cognitive deficit in this model, act in part by increasing expression of LAIR-1, an inhibitory receptor for C1q on microglia. This converts C1q from an eat me signal into an immunosuppressive signal.

Synaptic dysfunction is recognized as an early step in the pathophysiology of parkinsonism. Several genetic mutations affecting the integrity of synaptic proteins cause or increase the risk of developing disease. We have identified a candidate causative mutation in synaptic “SH3GL2 Interacting Protein 1” (SGIP1), linked to early-onset parkinsonism in a consanguineous Arab family. Additionally, affected siblings display intellectual, cognitive, and behavioral dysfunction. Metabolic network analysis of [18F]-fluorodeoxyglucose positron emission tomography scans shows patterns very similar to those of idiopathic Parkinson’s disease. We show that the identified SGIP1 mutation causes a loss of protein function, and analyses in newly created Drosophila models reveal movement defects, synaptic transmission dysfunction, and neurodegeneration, including dopaminergic synapse loss. Histology and correlative light and electron microscopy reveal the absence of synaptic multivesicular bodies and the accumulation of degradative organelles. This research delineates a putative form of recessive parkinsonism, converging on defective synaptic proteostasis and opening avenues for diagnosis, genetic counseling, and treatment.

Alzheimer’s Disease psychosis (AD + P) is characterized by accelerated cognitive decline and tau pathology. Through exploring the AD + P network (ADPN), the aim is to predict psychosis in AD and understand its mechanisms. Utilizing FDG PET scans from ADNI control and AD groups, we employed a convolutional neural network to identify and validate the ADPN. We analyzed network progression, clinical correlations, and psychosis prediction using expression scores, and network organization using graph theory. The ADPN accurately distinguishes AD + P from controls (97%), with increasing scores correlating with cognitive decline. ADPN-based approach predicts psychosis with 77% accuracy and identifies specific brain regions and connections associated with psychosis. Deep learning identified ADPN, linked to cognitive and functional decline. The increased metabolic connectivity between motor and language/social cognition regions in AD + P may drive delusions and agitated behavior. ADPN holds promise as a biomarker for AD + P, aiding in treatment development and patient stratification.