Estimating cerebrospinal fluid biomarkers using brain perfusion SPECT in Alzheimer’s disease

Estimating cerebrospinal fluid biomarkers using brain perfusion SPECT in Alzheimer’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Estimating cerebrospinal fluid biomarkers using brain perfusion SPECT in Alzheimer’s disease Takashi NAKATA, Kenichi SHIMADA, Akira TERASHIMA, Haruhiko ODA, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8804350/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Alzheimer’s disease (AD) involves amyloid-β (Aβ) and phosphorylated tau (p-tau) pathology. Cerebrospinal fluid (CSF) biomarkers reflect these changes but require invasive lumbar puncture, whereas amyloid positron emission tomography (PET) is less invasive but expensive and typically limited to a single reimbursed scan. 2-Deoxy-2-[ 18 F]-fluoro-D-glucose (FDG)-PET is less invasive than lumbar puncture; however, it is not covered by health insurance in Japan. In contrast, brain perfusion single photon emission computed tomography (SPECT) is widely available, repeatable, and cost-effective compared to FDG-PET. We investigated the correlation between regional relative cerebral blood flow (rCBF) measured by SPECT and CSF biomarkers, and evaluated whether rCBF reductions could predict CSF biomarker levels. Methods In this retrospective study, 88 patients underwent Mini-Mental State Examination (MMSE), MRI, N -isopropyl- p -[ 123 I] iodoamphetamine (IMP)-SPECT, and CSF biomarker assessments. SPECT data were normalized to cerebellar counts and co-registered to MRI. Voxel-wise analyses identified the regions where decreased rCBF correlated with CSF biomarkers. Simple regression evaluated correlations between the standard uptake value ratios (SUVRs) of posterior cingulate (PC), bilateral parietal cortices, MMSE, age, sex and biomarker levels. Multiple regression models incorporated the three SUVRs, MMSE, and age. Predicting validity was assessed using correlation coefficient (r), coefficient of determination (r²), and Bland–Altman analysis. Results Voxel-wise analysis revealed positive correlations between CSF Aβ42 level and CBF in PC and angular gyrus, while CSF t-tau and p-tau correlated negatively with parietal hypoperfusion. CSF Aβ40 showed no significant correlations. Simple regression demonstrated weak correlations, such as CSF Aβ42 with right parietal cortex (r = 0.47). Multiple regression yielded moderate predictability for CSF Aβ42 (r² = 0.42), whereas other biomarkers were poorly predicted. Conclusion SPECT revealed AD-typical hypoperfusion patterns and demonstrated modest potential to estimate CSF Aβ42 levels, but not CSF Aβ40, t-tau, or p-tau. Although SPECT cannot substitute CSF biomarker measurements or amyloid/tau PET, SPECT may serve as a pre-screening tool to identify patients requiring definitive biomarker testing. Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers single photon emission computed tomography (SPECT) regional relative cerebral blood flow (rCBF) voxel-wise analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alzheimer’s disease (AD) [ 1 , 2 ] is a progressive neurodegenerative disorder and the most common type of dementia around the world ( https://www.who.int/news-room/fact-sheets/detail/dementia ). AD is pathologically characterized by senile plaques and neurofibrillary tangles, the main components of which are amyloid-β (Aβ) and phosphorylated tau (p-tau), respectively [ 3 , 4 ]. The ATN framework categorizes biomarkers into amyloid (A), tau (T), and neurodegeneration (N), and defines biomarker profiles within the Alzheimer’s continuum, including A + T+N+ (Alzheimer’s disease), A + T−N− (Alzheimer’s pathologic change), and A + T−N+ (Alzheimer’s disease with concomitant suspected non-Alzheimer’s pathologic change) [ 5 – 7 ]. In recent years, monoclonal antibodies targeting Aβ have been approved, but their therapeutic effect is limited to the prodromal and early stages including the mild cognitive impairment (MCI) and the mild dementia phase [ 8 – 10 ]. AD pathology can be confirmed either by cerebrospinal fluid (CSF) biomarkers or amyloid positron emission tomography (PET) [ 5 , 6 ]. Although blood-based biomarkers have recently attracted considerable attention due to their potential clinical utility, their diagnostic reliability has not yet been established at a level comparable to that of CSF biomarkers. CSF measures such as Aβ42, Aβ40, t-tau, and p-tau are reliable but require an invasive lumbar puncture, which carries risks [ 11 – 18 ]. Amyloid positron emission tomography (PET) is less invasive but costly and usually limited to a single reimbursed scan [ 19 – 21 ], this examination is expensive and only single scanning under medical insurance coverage [ 20 , 22 ]. 2-Deoxy-2-[ 18 F]-fluoro-D-glucose positron emission tomography (FDG-PET) is less invasive than lumbar puncture and has been extensively used in dementia research, however the clinical application is restricted, not covered by health insurance in Japan and the associated costs remain substantial. On the other hand, single photon emission computed tomography (SPECT), which provides information on regional relative cerebral blood flow (rCBF) patterns, has the advantage that is relatively inexpensive and can be used for repeated examinations under health insurance coverage in dementia clinical practice [ 22 – 24 ]. Brain perfusion SPECT is particularly used for differentiating subtypes of neurodegenerative dementia in clinical practice [ 25 ]. We focused on the widespread availability and cost-effectiveness of brain perfusion SPECT compared with FDG-PET. If this approach could be established as a pre-screening tool to predict CSF biomarker levels using the standardized uptake value ratio (SUVR) of decreased cerebral blood flow (CBF) in voxels of interest (VOIs) derived from brain perfusion SPECT, we believe that this approach may aims to reduce the need for lumbar puncture or allow for the invasive procedure to be performed with confidence in the likelihood of AD. Therefore, this study aimed to investigate the extent to which amyloid deposition can be predicted using brain perfusion SPECT in a clinical practice prior to the performance of amyloid PET imaging or CSF biomarker assessment. Methods Subjects We retrospectively selected the clinical and imaging data from the patients admitted to the infirmary at Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center) for the evaluation of dementia between January 2014 and March 2024. During this period, 7,542 first-visit patients were screened. Of these, 245 patients met the following inclusion criteria: (i) age > 40 years; (ii) presence of clinical symptoms with MMSE score; (iii) availability of T1-weighted MRI and IMP-SPECT; (iv) availability of CSF biomarkers (Aβ42, Aβ40, p-tau, and total tau [t-tau]). After excluding 157 patients with missing or confounding data (e.g., cerebrovascular disease, epilepsy, encephalitis, depression, and schizophrenia), 88 patients remained (Fig. 1 ). All 88 patients were examined by neurologists and psychiatrists and underwent standard neurological and neuropsychological examinations, laboratory testing, head MRI, and brain perfusion SPECT (Table 1). In this study, typical AD was defined as an amnestic-predominant clinical syndrome characterized by early and prominent episodic memory impairment, consistent with the classical hippocampal-type AD. All typical AD met the diagnostic criteria for probable AD according to the National Institute on Aging–Alzheimer’s Association (NIA–AA) guidelines, and did not exhibit clinical features of atypical AD variants, such as posterior cortical atrophy, logopenic variant primary progressive aphasia, or frontal variant AD [ 5 – 7 , 11 , 26 – 28 ]. Clinical symptoms included fluctuation in cognitive function, recurrent visual hallucinations, auditory hallucinations, spontaneous parkinsonism, rapid eye movement (REM) sleep behavior disorder (RBD), visual/visuospatial cognitive impairment, gait disturbance, hearing loss, urinary disturbance, attention dysfunction, apraxia, insomnia, depression, delusion, irritation and agitation. CSF Biomarker Measurement All of CSF samples were outsourced for testing over a period of 10 years. CSF samples were collected by lumbar puncture, stored at -80℃, and analyzed using assay platform: LUMIPULSE® G1200 (chemiluminescent enzyme immunoassay [CLEIA]; FUJIREBIO Inc., Tokyo, Japan) [ 29 , 30 ]. CSF Aβ42, Aβ40, t-tau and p-tau levels were measured, while patient’s information and clinical data were blinded. In our study, CSF AD Index was calculated on t-tau × Aβ40/Aβ42 [ 31 , 32 ]. Brain Perfusion SPECT and MRI Acquisition Details of the brain perfusion SPECT procedure are provided elsewhere [ 23 , 24 ]. In brief, brain perfusion SPECT scans were initiated in the resting state with the eyes closed, 15 minutes after each patient was administered an injection of 111 MBq of N -isopropyl- p -[ 123 I]-iodoamphetamine. All SPECT scans were performed using a rotating dual-headed gamma camera (E-CAM, Siemens, Erlangen, Germany) with a low–medium energy, general purpose collimator. Brain perfusion SPECT images were obtained with a 128 × 128 matrix, 2.5 minutes/rotation × 12 rotations. For SPECT image reconstruction, a Butterworth filter (cutoff frequency: 0.58 cycles/cm, order: 8) was used. Attenuation correction was performed using Chang’s method (µ = 0.09 cm − 1 ) and scatter correction was performed with a triple energy window. MRI scanning was performed using a 3 T Achieva or a 1.5 T Ingenia (Philips, Best, Netherlands). The scan protocol included sagittal T1-weighted three-dimensional whole-brain images (Achieva: slice thickness 1.2 mm, 140 slices, matrix size 256 × 256, field of view 25.6 × 25.6 cm, echo time 3.11 ms, repetition time 6.7 ms, flip angle 8°; Ingenia: slice thickness 1.2 mm, 140 slices, matrix size 192 × 192, field of view 24.0 × 24.0 cm, echo time 4.0 ms, repetition time 8.6 ms, flip angle 8°). A 3 T Achieva or 1.5 T Ingenia scanner was performed under each condition, total scan duration time 3 min 30 s or 3 min 47 s, and acceleration factor 2 each other. To obtain rCBF values, the voxel counts for each SPECT image were normalized by dividing them by the cerebellar count [ 23 , 24 ] measured with our homemade cerebellar voxels of interest (VOIs), because global cerebral blood flow is often decreased in AD and normalization according to the global counts may lead to underestimation of rCBF values [ 33 ]. Data Analysis In the following three steps, the aims of this study were to: (i) identify regions of rCBF reduction common to AD, (ii) examine correlations between CSF biomarkers and SUVRs of AD specific regions, MMSE, age, and sex, and (iii) evaluate the predictive performance of multiple regression models. Voxel-wise analysis was performed using Statistical Parametric Mapping 12 (SPM12) ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). The correlation between decreased rCBF in SPECT and CSF biomarker levels was evaluated. Each individual SPECT image was co-registered to the corresponding MRI. The MRI was segmented into gray matter (GM), white matter, and CSF using the SPM12 segmentation program. GM images were spatially normalized to a standard template using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) [ 34 ]. The resulting normalization parameters were applied to the co-registered SPECT images, which were then spatially normalized to the Montreal Neurological Institute (MNI) space. All images were smoothed with a 12 mm Gaussian filter. Next, SPM12 was used for voxel. Voxel-wise analysis was performed between GM or CBF images and CSF biomarker levels, MMSE scores, age or sex. The significance threshold was set at p < 0.001 (uncorrected), with a voxel extent threshold of 300. First, we defined the regions of significantly reduced rCBF common in AD patients. Next, we used simple regression to develop predictive equations for CSF biomarker levels ​​based on the SUVRs of individual VOIs extracted from normalized SPECT images. Predictive accuracy was evaluated using the correlation coefficient (r), coefficient of determination (r 2 ), and Bland-Altman plots. We then constructed multiple regression models incorporating five predictors: (i) demographic and clinical variables (MMSE score, age, and sex); (ii) SUVRs ​​from three AD-relevant VOIs (posterior cingulate [PC], left parietal cortex [lt-Par], and right parietal cortex [rt-Par]). Predicted CSF biomarker levels ​​were compared to actual measurements, this predictive performance was evaluated using r, r 2 , and Bland-Altman analysis. Results Patient Characteristics The final cohort comprised 88 patients (31 males, 57 females; mean age 70.5 ± 10.9 years [range: 43–87 years]; mean MMSE score 23.2 ± 4.9 [rage: 8–30]). Among them, 35 patients exhibited underlying AD pathology, including typical AD (described above in detail), mild cognitive impairment (MCI) due to AD, posterior cortical atrophy (PCA)-AD, corticobasal syndrome (CBS)-AD, and AD with comorbid idiopathic normal pressure hydrocephalus (iNPH). The remaining 53 patients had non-AD diagnoses such as dementia with Lewy bodies (DLB), classified according to the established diagnostic criteria. Table 1 summarizes patient characteristics. The mean age was 71.5 years in males and 68.2 years in females. The mean MMSE score was 23.2 points for males and 22.6 points for females; AD group (n = 35): mean age 69.5 ± 11.4 years (range: 49–86 years), mean MMSE score 22.8 ± 4.7 points (range: 14–30 points); non-AD group (n = 53): mean age 71.2 ± 10.7 years (range: 43–87 years), mean MMSE score 23.4 ± 5.1 points (range: 8–30 points). Clinical Symptoms Table 2 presents the clinical symptoms and neuropsychological findings. Parkinsonism and visual/visuospatial cognitive impairment were observed in both AD and non-AD groups. However, gait disturbance was significantly more frequent in non-AD patients (p = 0.010, Student’s t-test). Voxel-wise Analysis of CSF Biomarkers and Brain Perfusion SPECT The correlations between rCBF from SPECT and CSF biomarker levels are summarized in Table 3 and Fig. 2 . In the AD group: CSF Aβ42 level was positively correlated with rCBF in the right ventral posterior cingulate (Brodmann area [BA] 23), left angular gyrus (BA39), and right medial temporal gyrus (BA21). CSF Aβ40 and sex presented no significant correlations. The Aβ42/40 ratio showed positive correlations in bilateral angular gyri. In contrast, the CSF AD Index [ 31 , 32 ], CSF t-tau, and p-tau levels were negatively correlated with the angular gyrus. MMSE scores were positively correlated with the inferior and medial temporal gyri, posterior cingulate, and frontal eye fields, and negatively correlated with premotor regions. Age correlated positively with the somatosensory and visual association cortices, and negatively correlated with the insula, posterior cingulate, and parahippocampal gyrus. Simple Regression Analysis The 88 patients were randomly divided into a training set (n = 61) and a validation set (n = 27) (Table 4). Simple regression analyses examined correlations between CSF biomarker concentrations and SUVRs from three VOIs (PC, lt-Par and rt-Par), MMSE score, age and sex. The correlation coefficients are summarized in Table 5. Overall, no strong correlations were observed. Age did not present significant correlations with any CSF biomarker. Bland–Altman plots demonstrated substantial variability between the predicted and actual CSF biomarker levels (Fig. 3 and Fig. 4 ). Validation Subgroup Comparison Both the training and validation sets showed no significant difference in age or MMSE between the AD and non-AD subgroups. In the training set, sex distribution was balanced. In the validation set, sex distribution differed significantly between AD and non-AD groups. Multiple Regression Analysis A multiple regression model was developed using three regional SUVRs (PC, lt-Par and rt-Par), MMSE score, and age (Table 6). Sex was excluded due to a lack of correlation with rCBF. The best performance was observed for CSF Aβ42 prediction (r² = 0.42, r = 0.65) in this research. Other CSF biomarkers (Aβ40, t-tau, and p-tau) indicated only weak predictive performance. Bland–Altman plots confirmed large variability in predictions (Fig. 5 ). Discussion This study identified AD-typical hypoperfusion in the parietotemporal and PC regions, consistent with findings reported in previous FDG-PET studies [ 35 ]. Correlations between brain perfusion SPECT–derived SUVRs and CSF biomarkers were modest but directionally concordant with earlier reports [ 36 – 41 ]. Positive correlations were observed between regional SUVRs and the CSF Aβ42/40 ratio in the parietal cortex and PC, whereas CSF tau-related biomarkers exhibited negative correlations in these regions. Among all CSF biomarkers, only CSF Aβ42 demonstrated the highest predictive performance (moderate) with a coefficient of determination of r² = 0.42. Voxel-wise analyses revealed that CSF Aβ42 levels were positively correlated with rCBF in PC, the angular gyrus, and medial temporal cortex. Similarly, the CSF Aβ42/40 ratio correlated positively with the angular gyrus, while CSF t-tau and CSF p-tau were negatively correlated within overlapping cortical areas. MMSE score was positively correlated with hypoperfusion in the temporal and cingulate cortices, and negatively correlated with the premotor cortex. Age exhibited both positive and negative correlations across multiple brain regions. These findings are consistent with previous FDG-PET studies reporting the parietotemporal and PC hypometabolism in AD [ 35 ], providing complementary insights into the CSF biomarker level–rCBF correlations. Simple regression analyses identified weak but consistent correlations: CSF Aβ42 correlated with the right parietal cortex (r = 0.47), the CSF Aβ42/40 ratio correlated with the bilateral parietal cortex, the CSF AD index correlated with PC and the right parietal cortex, and CSF t-tau correlated negatively with PC. An unexpected negative correlation between CSF Aβ40 levels and parietal SUVRs was observed, although its biological significance remains unclear. A multiple regression model incorporating SUVRs from the posterior cingulate and bilateral parietal cortices, together with MMSE score and age, achieved the best performance for predicting CSF Aβ42 (r² = 0.42). In contrast, other CSF biomarkers were only weakly predicted (Table 6, Fig. 5 ). Despite substantial variability, these findings indicate that brain perfusion SPECT has limited yet but measurable potential for estimating CSF Aβ42 concentrations. Correlations between reduced rCBF and CSF biomarkers have been investigated using brain perfusion SPECT and FDG-PET [ 36 – 44 ]. Inferior parietal hypoperfusion correlated with CSF Aβ42 using [ 99m Tc]-Technetium-99-hexamethyl-propyleneamine oxime ([ 99m Tc]-HMPAO)-SPECT, consistent with right parietal cortex findings (r = 0.47) [ 38 ]. In contrast, [ 99m Tc]-Technetium-99-ethyl cysteinate dimer ([ 99m Tc]-ECD)-SPECT indicated strong negative correlations with CSF t-tau (r = − 0.69) and CSF p-tau (r = − 0.70), but not Aβ42 [ 39 ]. FDG-PET demonstrated moderate correlations with CSF Aβ42 (r = 0.33) and CSF p-tau (r = 0.34), and weaker correlations with CSF t-tau (r = 0.24) [ 40 ]. CSF Aβ42 reflects amyloid burden, whereas tau biomarkers represent neurofibrillary pathology [ 41 ]. FDG-PET SUVRs were positively correlated with CSF Aβ42 in PC (r = 0.38) and parietal cortex (r = 0.39), and parietal SUVRs were associated with CSF Aβ42/40 ratio (r = 0.34). Conversely, SUVRs in prefrontal cortex (r = − 0.41), PC (r = − 0.24), and parietal cortex (r = − 0.28) were negatively correlated with CSF t-tau, while prefrontal (r = − 0.36) and parietal cortex (r = − 0.27) SUVRs were negatively correlated with CSF p-tau [ 36 ]. Similar patterns were observed with FDG uptake across the frontal, PC, parietal, and temporal cortices, indicating positive correlations with CSF Aβ42 (r = 0.28–0.35) and negative correlations with CSF t-tau (r = − 0.36 to − 0.22) and CSF p-tau (r = − 0.31 to − 0.18) [ 45 ]. Previous investigations closely aligned with our results. Brain perfusion SPECT and FDG-PET have indicated significant correlations with CSF biomarkers, however r values were not as high as expected [ 36 , 38 – 40 , 45 ]. Discrepancies involving CSF Aβ40 may reflect methodological differences or cohort characteristics. Recent imaging-based predictive approaches have focused on amyloid PET [ 19 , 34 – 37 , 45 ]. While methods such as the easy Z-score imaging system (eZIS) and specific VOI analysis (SVA) predict amyloid PET positivity [ 37 ], this study differs by applying SPM12/DARTEL-based voxel-wise analysis [ 34 ] and directly predicting numerical CSF biomarker concentrations using multiple regression models. Advanced machine-learning approaches using FDG-PET, including support vector machine models, have reported high predictive accuracy for amyloid PET status [ 19 ]. Although this research employed a relatively basic machine-learning framework, future implementation of more advanced artificial intelligence (AI) models may improve predictive accuracy and clinical usability. Our research group plans to report these results elsewhere. These findings highlight the potential of this approach that brain perfusion SPECT–based prediction of CSF biomarkers as a pre-screening tool, particularly CSF Aβ42, may serve as a practical adjunct for early diagnosis, clinical decision-making and reducing caregiver burden. To improve the prediction performance, further improvements are expected through the use of more advanced artificial intelligence models, the conduct of large prospective studies and the combination with neuroimaging modalities. Limitations This study has several limitations. First, it was retrospective with a relatively small sample of 88 cases. Second, the data were collected before the approval of anti-Aβ monoclonal antibodies [ 8 ], and included several atypical AD variants [ 11 , 26 ], complicating interpretation. Third, the CSF biomarker assays were performed at different laboratories using varying platforms across the 10-year period, introducing potential variability. Moreover, in Japan, health insurance coverage allows only a single amyloid PET or CSF test for Aβ pathology per a patient, limiting simultaneous CSF biomarker validation. Research budgetary constraints also precluded concurrent PET and CSF evaluations. In the ATN framework, brain perfusion SPECT primarily reflects the neurodegeneration (N) marker, which may partly explain the weak correlations with amyloid (A) and tau (T) marker [ 46 ]. Without concurrent amyloid or tau PET [ 47 ], the correlation between SPECT SUVRs and CSF biomarker levels remains indirect and modest. Conclusion In summary, brain perfusion SPECT revealed AD-typical perfusion patterns and modest correlations between rCBF and CSF biomarker levels, particularly CSF Aβ42. Although SPECT cannot completely substitute amyloid/tau PET or CSF biomarker measurements, brain perfusion SPECT may serve as a pre-screening tool for triaging patients for definitive testing. Further studies are needed to improve the predictive potential of CSF biomarker derived from brain perfusion SPECT in AD. Abbreviations AD, Alzheimer’s disease; CBF, cerebral blood flow; rCBF, regional relative cerebral blood flow; DLB, dementia with Lewy bodies; CSF, cerebrospinal fluid; Aβ, amyloid-β; SPECT, single photon emission computed tomography; MMSE, Mini-Mental State Examination; BA, Brodmann area; PET, positron emission tomography; VOI, voxel of interest; SUVR, the standardized uptake value ratio; MRI, magnetic resonance imaging; MCI, mild cognitive impairment; GM, gray matter; SPM12, Statical Parametric Mapping 12 Software; DARTEL, Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra; iNPH, idiopathic normal pressure hydrocephalus; CBS, corticobasal syndrome; PCA, posterior cortical atrophy, REM, rapid eye movement; RBD, rapid eye movement sleep behavior disorder; PD, Parkinson’s disease; CLEIA, chemiluminescent enzyme immunoassay; PC, posterior cingulate; Par, parietal cortex; [ 123 I]-IMP, N -isopropyl- p -[ 123 I] iodoamphetamine; [ 99m Tc]-ECD, [ 99m Tc]-Technetium-99-ethyl cysteinate dimer; [ 99m Tc]-HMPAO, [ 99m Tc]-Technetium-99-hexamethylpropyleneamine oxime; [ 18 F]-FDG, 2-deoxy-2-[ 18 F]-fluoro-D- glucose; ATN, amyloid-tau-neurodegeneration Declarations Author contributions: Conceptual idea for the article was by TN and KI. TN performed the literature search. TN, KS, HO, and AT were involved in the clinical neurological and neuropsychological data acquisition. YS, YK, RK, and KI were involved in the imaging data acquisition. TN, TY and KI performed the data analysis. The first draft of the manuscript was written by TN and was reviewed by KI. All authors provided critical revisions on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: This manuscript received no funding. Compliance with ethical standards Ethics: This study was approved by the Ethics Committee of Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center). The requirement for written informed consent was waived due to the retrospective nature of the study. Declaration of Competing Interest: All authors declare no conflicts of interest. Informed consent: All investigations were carried out according to the Declaration of Helsinki. We confirmed that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. All co-authors have read and approved the submission. This study was examined by the Ethics Committee of the Hyogo Prefectural Harima-Himeji General Medical Center, and informed consent was made by opt-out information disclosure. Acknowledgements No potential conflicts of interest were disclosed. We would like to express our gratitude to all hospital staff at the Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center) for providing us with the chance to perform this research. Special thanks to all patient and staff in the memory clinic in the Neurocognitive Disorders Medical Center at the Hyogo Prefectural Harima-Himeji General Medical Center. References McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34(7):939-44. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr., Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):263-9. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science. 2002;297(5580):353-6. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239-59. Jack CR, Jr., Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, et al. Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement. 2024;20(8):5143-69. Jack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535-62. Jack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539-47. Yadollahikhales G, Rojas JC. Anti-Amyloid Immunotherapies for Alzheimer's Disease: A 2023 Clinical Update. Neurotherapeutics. 2023;20(4):914-31. van Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in Early Alzheimer's Disease. N Engl J Med. 2023;388(1):9-21. Sims JR, Zimmer JA, Evans CD, Lu M, Ardayfio P, Sparks J, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. Jama. 2023;330(6):512-27. Dubois B, von Arnim CAF, Burnie N, Bozeat S, Cummings J. Biomarkers in Alzheimer's disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther. 2023;15(1):175. Witbracht MG, Bernstein OM, Lin V, Salazar CR, Sajjadi SA, Hoang D, et al. Education and Message Framing Increase Willingness to Undergo Research Lumbar Puncture: A Randomized Controlled Trial. Front Med (Lausanne). 2020;7:493. Day GS, Rappai T, Sathyan S, Morris JC. Deciphering the factors that influence participation in studies requiring serial lumbar punctures. Alzheimers Dement (Amst). 2020;12(1):e12003. Costerus JM, Brouwer MC, van de Beek D. Technological advances and changing indications for lumbar puncture in neurological disorders. Lancet Neurol. 2018;17(3):268-78. Moulder KL, Besser LM, Beekly D, Blennow K, Kukull W, Morris JC. Factors Influencing Successful Lumbar Puncture in Alzheimer Research. Alzheimer Dis Assoc Disord. 2017;31(4):287-94. Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer's disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292-323. Edwards C, Leira EC, Gonzalez-Alegre P. Residency training: a failed lumbar puncture is more about obesity than lack of ability. Neurology. 2015;84(10):e69-72. Wright BL, Lai JT, Sinclair AJ. Cerebrospinal fluid and lumbar puncture: a practical review. J Neurol. 2012;259(8):1530-45. Yamada T, Kimura Y, Watanabe S, Watanabe A, Honda M, Nagaoka T, et al. Evaluation of amyloid PET positivity using machine learning on (18)F-FDG PET images. Jpn J Radiol. 2025;43(9):1541-9. Ruan D, Sun L. Amyloid-beta PET in Alzheimer's disease: A systematic review and Bayesian meta-analysis. Brain Behav. 2023;13(1):e2850. Fantoni ER, Chalkidou A, JT OB, Farrar G, Hammers A. A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer's Disease. J Alzheimers Dis. 2018;63(2):783-96. Ferrando R, Damian A. Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option? Front Neurol. 2021;12:629442. Nakata T, Shimada K, Iba A, Oda H, Terashima A, Koide Y, et al. Differential diagnosis of MCI with Lewy bodies and MCI due to Alzheimer's disease by visual assessment of occipital hypoperfusion on SPECT images. Jpn J Radiol. 2024;42(3):308-18. Nakata T, Shimada K, Iba A, Oda H, Terashima A, Koide Y, et al. Correlation between noise pareidolia test scores for visual hallucinations and regional cerebral blood flow in dementia with Lewy bodies. Ann Nucl Med. 2022;36(4):384-92. Matsuda H. Role of neuroimaging in Alzheimer's disease, with emphasis on brain perfusion SPECT. J Nucl Med. 2007;48(8):1289-300. Graff-Radford J, Yong KXX, Apostolova LG, Bouwman FH, Carrillo M, Dickerson BC, et al. New insights into atypical Alzheimer's disease in the era of biomarkers. Lancet Neurol. 2021;20(3):222-34. Ossenkoppele R, Jansen WJ, Rabinovici GD, Knol DL, van der Flier WM, van Berckel BN, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. Jama. 2015;313(19):1939-49. Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614-29. Kurihara M, Kondo S, Ohse K, Nojima H, Kikkawa-Saito E, Iwata A. Relationship Between Cerebrospinal Fluid Alzheimer's Disease Biomarker Values Measured via Lumipulse Assays and Conventional ELISA: Single-Center Experience and Systematic Review. J Alzheimers Dis. 2024;99(3):1077-92. Hisashi Nojima, Satoshi Ito, Akira Kushida, Aki Abe, Wataru Motsuchi, David Verbel, et al. Clinical utility of cerebrospinal fluid biomarkers measured by LUMIPULSE system. Annals of Clinical and Translational Neurology. 2022;9(12):1898-909. Shoji M. CEREBROSPINAL FLUID Aβ40 AND Aβ42: NATURAL COURSE AND CLINICAL USEFULNESS. Frontiers in Bioscience. 2002;7:d997-1006. Shoji M, Kanai M. Cerebrospinal fluid Aβ40 and Aβ42: Natural course and clinical usefulness. J Alzheimers Dis. 2001;3(3):313-21. Ishii K, Yamada T, Hanaoka K, Kaida H, Miyazaki K, Ueda M, et al. Regional gray matter-dedicated SUVR with 3D-MRI detects positive amyloid deposits in equivocal amyloid PET images. Ann Nucl Med. 2020;34(11):856-63. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95-113. Ishii K. Clinical application of positron emission tomography for diagnosis of dementia. Annals of Nuclear Medicine. 2002;16(8):515-25. Woyk K, Hansen N, Wiltfang J, Lange C, Bouter C. The relationship of (18)F-FDG-PET to other common biomarkers of dementia in a clinical cohort with memory deficits. J Alzheimers Dis Rep. 2025;9:25424823251314392. Asada T, Kakuma T, Tanaka M, Araki W, Lebowitz AJ, Momose T, Matsuda H. A statistical method for predicting amyloid-β deposits from severity, extend, and ratio indices of the (99m)Tc-ECD SPECT. J Alzheimers Dis. 2025:13872877251324222. Andriuta D, Moullart V, Schraen S, Devendeville A, Meyer ME, Godefroy O. Inferior Parietal Cortex Hypoperfusion is the Most Specific Imaging Marker for AD Patients With Positive CSF Biomarker Assays in a Memory Clinic in France. Alzheimer Dis Assoc Disord. 2018;32(2):89-93. Habert MO, de Souza LC, Lamari F, Daragon N, Desarnaud S, Jardel C, et al. Brain perfusion SPECT correlates with CSF biomarkers in Alzheimer's disease. Eur J Nucl Med Mol Imaging. 2010;37(3):589-93. Tapiola T, Alafuzoff I, Herukka SK, Parkkinen L, Hartikainen P, Soininen H, Pirttilä T. Cerebrospinal fluid {beta}-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol. 2009;66(3):382-9. Jagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193-9. Yamakuni R, Abe M, Ukon N, Matsuda H, Takano H, Sawamoto N, et al. Comparison and cutoff values of two amyloid PET scaling methods: centiloid scale and amyloid-β load. Ann Nucl Med. 2025;39(8):799-812. Takenaka A, Nihashi T, Sakurai K, Notomi K, Ono H, Inui Y, et al. Interrater agreement and variability in visual reading of [18F] flutemetamol PET images. Ann Nucl Med. 2025;39(1):68-76. Shang C, Sakurai K, Nihashi T, Arahata Y, Takeda A, Ishii K, et al. Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods. Ann Nucl Med. 2024;38(6):460-7. Bouter Y, Glasnek RM, Wenzel JM, Bouter C. (18)F-FDG-PET and Multimodal Biomarker Integration: A Powerful Tool for Alzheimer's Disease Diagnosis. Nucl Med Mol Imaging. 2025;59(6):453-71. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):270-9. Matsuda H, Hanyu H, Kaneko C, Ogura M, Yamao T. Highly specific amyloid and tau PET ligands for ATN classification in suspected Alzheimer's disease patients. Ann Nucl Med. 2025;39(5):458-65. Tables Tables 1 to 6 are available in the supplementary files section Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8804350","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589233983,"identity":"20948661-b16e-457a-a8bc-a23f55ff1190","order_by":0,"name":"Takashi NAKATA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYHACZhAhZwAiGRsgQgb41POAtSQwGJOuJXEDsha8wF66+bHBzx926dv5Vyd+YNxxWE7evYGhuACfLTLHjBN7EpJzd854u1mC8cxhY8MzBxiMZ+DTIpFgfIAngTl3w42z2xgY2w4nbpwB9BkPXi3pnw/+SahPNyBBS45xMk/C4QSD870QLfMlCGm5kVNsLJN23HDDDd7NEolt6cYGPAcb8PqFfUb6Zsk3NtXyBufPbvzwsc1aTr69+ZgxvhBDAKB7gPHTzGBwgLHNmCgdDPwHQGQdg3wDA/Nj4rSMglEwCkbBCAEA4YhQH74B1M4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6652-9447","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Takashi","middleName":"","lastName":"NAKATA","suffix":""},{"id":589233984,"identity":"f0352259-9ee1-4829-91a1-bf3b06135f1b","order_by":1,"name":"Kenichi SHIMADA","email":"","orcid":"","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Kenichi","middleName":"","lastName":"SHIMADA","suffix":""},{"id":589233985,"identity":"e46f6262-8f95-4de1-99e5-13e2b060edae","order_by":2,"name":"Akira TERASHIMA","email":"","orcid":"","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"TERASHIMA","suffix":""},{"id":589233986,"identity":"4fbc3c7d-bc44-42be-9d0f-93dbdaf76199","order_by":3,"name":"Haruhiko ODA","email":"","orcid":"","institution":"Hyogo Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Haruhiko","middleName":"","lastName":"ODA","suffix":""},{"id":589233987,"identity":"3977958f-8d2d-4e2b-ac0c-0e6f6fc99d9a","order_by":4,"name":"Yuko SUENAGA","email":"","orcid":"","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yuko","middleName":"","lastName":"SUENAGA","suffix":""},{"id":589233988,"identity":"8075e38a-5c94-4d1c-bb92-1432182dc648","order_by":5,"name":"Yutaka KOIDE","email":"","orcid":"","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yutaka","middleName":"","lastName":"KOIDE","suffix":""},{"id":589233989,"identity":"63259c8b-201c-45f9-967a-9ddc3b85f1e2","order_by":6,"name":"Ryota KAWASAKI","email":"","orcid":"","institution":"Hyogo Prefectural Harima-Himeji General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ryota","middleName":"","lastName":"KAWASAKI","suffix":""},{"id":589233990,"identity":"cd23f45a-1d28-4e70-8e24-274c064d041b","order_by":7,"name":"Takahiro YAMADA","email":"","orcid":"","institution":"Kindai University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Takahiro","middleName":"","lastName":"YAMADA","suffix":""},{"id":589233991,"identity":"da38dcc2-65a2-4fc4-a28a-2efa99d5223d","order_by":8,"name":"Kazunari ISHII","email":"","orcid":"","institution":"Kobe University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kazunari","middleName":"","lastName":"ISHII","suffix":""}],"badges":[],"createdAt":"2026-02-06 08:18:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8804350/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8804350/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102758126,"identity":"3bd9bdf6-9303-4815-9f04-4015f25640ff","added_by":"auto","created_at":"2026-02-16 10:07:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patient inclusion and exclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOver a period of 10 years, 7,542 patients first visited our memory clinic. Of these, 245 patients met our criteria in this study. After excluding 157 patients, 88 patients remained.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/2f5e96179550971f0b5df371.png"},{"id":102758127,"identity":"d3a3ce4a-238c-4cce-9631-fb487d08a5ec","added_by":"auto","created_at":"2026-02-16 10:07:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1548113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHypoperfusion region with positive or negative correlations with CSF biomarkers, MMSE score, age and sex\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegions showing significant correlations are displayed in red.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/9c691e39c1af05ce1db62fb5.png"},{"id":102758124,"identity":"fce1ed57-831f-4473-995d-e97b536f1396","added_by":"auto","created_at":"2026-02-16 10:07:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":203785,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between each CSF biomarker level and SUVR in three VOIs (PC and bilateral parietal cortices)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation between each biomarker level and SUVR in each VOI was plotted. The CSF Aβ42 and Aβ42/40 ratio showed a positive correlation, no correlation between CSF Aβ40 and the SUVR value in bilateral Par was observed. While CSF p-tau, t-tau and AD Index tended to indicate a negative correlation.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/229b7555f7e01dca85f2bd72.png"},{"id":102758123,"identity":"b3de0f14-e0cf-462d-b911-b059449a6879","added_by":"auto","created_at":"2026-02-16 10:07:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174809,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland-Altman plot of each CSF biomarker level, MMSE, age and sex obtained by single regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variability between the actual and predictive values\u003cbr\u003e\nfor each CSF biomarker level was presented. The horizontal axis showed the mean and the vertical axis showed the difference. Although a few outliers were observed for each biomarker, the degree of variability was roughly the same. In CSF Aβ40, negative correlation was observed between the mean and difference between the actual and predictive values in bilateral parietal cortices.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/9965ccf90f2a4a4af62c2a01.png"},{"id":102962356,"identity":"08263d86-174b-4dc2-993d-030f8bf023ce","added_by":"auto","created_at":"2026-02-19 04:07:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":76924,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland-Altman plot of each CSF biomarker level obtained by multiple regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variability between the actual and predictive values\u003cbr\u003e\nfor each CSF biomarker level was demonstrated. The horizontal axis showed the mean and the vertical axis showed the difference. Although a few outliers were observed for each biomarker, the degree of variability was roughly the same.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/3e5e2f5215d1ef97875c3268.png"},{"id":105563814,"identity":"e2da8a7c-11ba-470d-b811-3d59420842d7","added_by":"auto","created_at":"2026-03-27 12:47:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2662963,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/ec22ff87-43ac-4b7c-8de7-8ae59392ebd7.pdf"},{"id":102758122,"identity":"9624fe1b-bb43-473a-bf1d-06995fb50a57","added_by":"auto","created_at":"2026-02-16 10:07:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39103,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8804350/v1/0ded6e88d3a8c17ad320cd9b.docx"}],"financialInterests":"","formattedTitle":"Estimating cerebrospinal fluid biomarkers using brain perfusion SPECT in Alzheimer’s disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] is a progressive neurodegenerative disorder and the most common type of dementia around the world (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/dementia\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/dementia\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). AD is pathologically characterized by senile plaques and neurofibrillary tangles, the main components of which are amyloid-β (Aβ) and phosphorylated tau (p-tau), respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The ATN framework categorizes biomarkers into amyloid (A), tau (T), and neurodegeneration (N), and defines biomarker profiles within the Alzheimer\u0026rsquo;s continuum, including A\u0026thinsp;+\u0026thinsp;T+N+ (Alzheimer\u0026rsquo;s disease), A\u0026thinsp;+\u0026thinsp;T\u0026minus;N\u0026minus; (Alzheimer\u0026rsquo;s pathologic change), and A\u0026thinsp;+\u0026thinsp;T\u0026minus;N+ (Alzheimer\u0026rsquo;s disease with concomitant suspected non-Alzheimer\u0026rsquo;s pathologic change) [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, monoclonal antibodies targeting Aβ have been approved, but their therapeutic effect is limited to the prodromal and early stages including the mild cognitive impairment (MCI) and the mild dementia phase [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. AD pathology can be confirmed either by cerebrospinal fluid (CSF) biomarkers or amyloid positron emission tomography (PET) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although blood-based biomarkers have recently attracted considerable attention due to their potential clinical utility, their diagnostic reliability has not yet been established at a level comparable to that of CSF biomarkers. CSF measures such as Aβ42, Aβ40, t-tau, and p-tau are reliable but require an invasive lumbar puncture, which carries risks [\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmyloid positron emission tomography (PET) is less invasive but costly and usually limited to a single reimbursed scan [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], this examination is expensive and only single scanning under medical insurance coverage [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. 2-Deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]-fluoro-D-glucose positron emission tomography (FDG-PET) is less invasive than lumbar puncture and has been extensively used in dementia research, however the clinical application is restricted, not covered by health insurance in Japan and the associated costs remain substantial.\u003c/p\u003e \u003cp\u003eOn the other hand, single photon emission computed tomography (SPECT), which provides information on regional relative cerebral blood flow (rCBF) patterns, has the advantage that is relatively inexpensive and can be used for repeated examinations under health insurance coverage in dementia clinical practice [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Brain perfusion SPECT is particularly used for differentiating subtypes of neurodegenerative dementia in clinical practice [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe focused on the widespread availability and cost-effectiveness of brain perfusion SPECT compared with FDG-PET. If this approach could be established as a pre-screening tool to predict CSF biomarker levels using the standardized uptake value ratio (SUVR) of decreased cerebral blood flow (CBF) in voxels of interest (VOIs) derived from brain perfusion SPECT, we believe that this approach may aims to reduce the need for lumbar puncture or allow for the invasive procedure to be performed with confidence in the likelihood of AD. Therefore, this study aimed to investigate the extent to which amyloid deposition can be predicted using brain perfusion SPECT in a clinical practice prior to the performance of amyloid PET imaging or CSF biomarker assessment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eWe retrospectively selected the clinical and imaging data from the patients admitted to the infirmary at Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center) for the evaluation of dementia between January 2014 and March 2024.\u003c/p\u003e \u003cp\u003eDuring this period, 7,542 first-visit patients were screened. Of these, 245 patients met the following inclusion criteria: (i) age\u0026thinsp;\u0026gt;\u0026thinsp;40 years; (ii) presence of clinical symptoms with MMSE score; (iii) availability of T1-weighted MRI and IMP-SPECT; (iv) availability of CSF biomarkers (Aβ42, Aβ40, p-tau, and total tau [t-tau]). After excluding 157 patients with missing or confounding data (e.g., cerebrovascular disease, epilepsy, encephalitis, depression, and schizophrenia), 88 patients remained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All 88 patients were examined by neurologists and psychiatrists and underwent standard neurological and neuropsychological examinations, laboratory testing, head MRI, and brain perfusion SPECT (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eIn this study, typical AD was defined as an amnestic-predominant clinical syndrome characterized by early and prominent episodic memory impairment, consistent with the classical hippocampal-type AD. All typical AD met the diagnostic criteria for probable AD according to the National Institute on Aging\u0026ndash;Alzheimer\u0026rsquo;s Association (NIA\u0026ndash;AA) guidelines, and did not exhibit clinical features of atypical AD variants, such as posterior cortical atrophy, logopenic variant primary progressive aphasia, or frontal variant AD [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eClinical symptoms included fluctuation in cognitive function, recurrent visual hallucinations, auditory hallucinations, spontaneous parkinsonism, rapid eye movement (REM) sleep behavior disorder (RBD), visual/visuospatial cognitive impairment, gait disturbance, hearing loss, urinary disturbance, attention dysfunction, apraxia, insomnia, depression, delusion, irritation and agitation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCSF Biomarker Measurement\u003c/h3\u003e\n\u003cp\u003eAll of CSF samples were outsourced for testing over a period of 10 years. CSF samples were collected by lumbar puncture, stored at -80℃, and analyzed using assay platform: LUMIPULSE\u0026reg; G1200 (chemiluminescent enzyme immunoassay [CLEIA]; FUJIREBIO Inc., Tokyo, Japan) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. CSF Aβ42, Aβ40, t-tau and p-tau levels were measured, while patient\u0026rsquo;s information and clinical data were blinded. In our study, CSF AD Index was calculated on t-tau \u0026times; Aβ40/Aβ42 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eBrain Perfusion SPECT and MRI Acquisition\u003c/h3\u003e\n\u003cp\u003eDetails of the brain perfusion SPECT procedure are provided elsewhere [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In brief, brain perfusion SPECT scans were initiated in the resting state with the eyes closed, 15 minutes after each patient was administered an injection of 111 MBq of \u003cem\u003eN\u003c/em\u003e-isopropyl-\u003cem\u003ep\u003c/em\u003e-[\u003csup\u003e123\u003c/sup\u003eI]-iodoamphetamine. All SPECT scans were performed using a rotating dual-headed gamma camera (E-CAM, Siemens, Erlangen, Germany) with a low\u0026ndash;medium energy, general purpose collimator. Brain perfusion SPECT images were obtained with a 128 \u0026times; 128 matrix, 2.5 minutes/rotation \u0026times; 12 rotations. For SPECT image reconstruction, a Butterworth filter (cutoff frequency: 0.58 cycles/cm, order: 8) was used. Attenuation correction was performed using Chang\u0026rsquo;s method (\u0026micro;\u0026thinsp;=\u0026thinsp;0.09 cm\u0026thinsp;\u0026minus;\u0026thinsp;\u003csup\u003e1\u003c/sup\u003e) and scatter correction was performed with a triple energy window.\u003c/p\u003e \u003cp\u003eMRI scanning was performed using a 3 T Achieva or a 1.5 T Ingenia (Philips, Best, Netherlands). The scan protocol included sagittal T1-weighted three-dimensional whole-brain images (Achieva: slice thickness 1.2 mm, 140 slices, matrix size 256 \u0026times; 256, field of view 25.6 \u0026times; 25.6 cm, echo time 3.11 ms, repetition time 6.7 ms, flip angle 8\u0026deg;; Ingenia: slice thickness 1.2 mm, 140 slices, matrix size 192 \u0026times; 192, field of view 24.0 \u0026times; 24.0 cm, echo time 4.0 ms, repetition time 8.6 ms, flip angle 8\u0026deg;). A 3 T Achieva or 1.5 T Ingenia scanner was performed under each condition, total scan duration time 3 min 30 s or 3 min 47 s, and acceleration factor 2 each other. To obtain rCBF values, the voxel counts for each SPECT image were normalized by dividing them by the cerebellar count [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] measured with our homemade cerebellar voxels of interest (VOIs), because global cerebral blood flow is often decreased in AD and normalization according to the global counts may lead to underestimation of rCBF values [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eIn the following three steps, the aims of this study were to: (i) identify regions of rCBF reduction common to AD, (ii) examine correlations between CSF biomarkers and SUVRs of AD specific regions, MMSE, age, and sex, and (iii) evaluate the predictive performance of multiple regression models.\u003c/p\u003e \u003cp\u003eVoxel-wise analysis was performed using Statistical Parametric Mapping 12 (SPM12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The correlation between decreased rCBF in SPECT and CSF biomarker levels was evaluated.\u003c/p\u003e \u003cp\u003eEach individual SPECT image was co-registered to the corresponding MRI. The MRI was segmented into gray matter (GM), white matter, and CSF using the SPM12 segmentation program. GM images were spatially normalized to a standard template using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The resulting normalization parameters were applied to the co-registered SPECT images, which were then spatially normalized to the Montreal Neurological Institute (MNI) space. All images were smoothed with a 12 mm Gaussian filter. Next, SPM12 was used for voxel. Voxel-wise analysis was performed between GM or CBF images and CSF biomarker levels, MMSE scores, age or sex. The significance threshold was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (uncorrected), with a voxel extent threshold of 300.\u003c/p\u003e \u003cp\u003eFirst, we defined the regions of significantly reduced rCBF common in AD patients. Next, we used simple regression to develop predictive equations for CSF biomarker levels ​​based on the SUVRs of individual VOIs extracted from normalized SPECT images. Predictive accuracy was evaluated using the correlation coefficient (r), coefficient of determination (r\u003csup\u003e2\u003c/sup\u003e), and Bland-Altman plots. We then constructed multiple regression models incorporating five predictors: (i) demographic and clinical variables (MMSE score, age, and sex); (ii) SUVRs ​​from three AD-relevant VOIs (posterior cingulate [PC], left parietal cortex [lt-Par], and right parietal cortex [rt-Par]). Predicted CSF biomarker levels ​​were compared to actual measurements, this predictive performance was evaluated using r, r\u003csup\u003e2\u003c/sup\u003e, and Bland-Altman analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eThe final cohort comprised 88 patients (31 males, 57 females; mean age 70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 years [range: 43\u0026ndash;87 years]; mean MMSE score 23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 [rage: 8\u0026ndash;30]). Among them, 35 patients exhibited underlying AD pathology, including typical AD (described above in detail), mild cognitive impairment (MCI) due to AD, posterior cortical atrophy (PCA)-AD, corticobasal syndrome (CBS)-AD, and AD with comorbid idiopathic normal pressure hydrocephalus (iNPH). The remaining 53 patients had non-AD diagnoses such as dementia with Lewy bodies (DLB), classified according to the established diagnostic criteria. Table\u0026nbsp;1 summarizes patient characteristics. The mean age was 71.5 years in males and 68.2 years in females. The mean MMSE score was 23.2 points for males and 22.6 points for females; AD group (n\u0026thinsp;=\u0026thinsp;35): mean age 69.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 years (range: 49\u0026ndash;86 years), mean MMSE score 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 points (range: 14\u0026ndash;30 points); non-AD group (n\u0026thinsp;=\u0026thinsp;53): mean age 71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.7 years (range: 43\u0026ndash;87 years), mean MMSE score 23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1 points (range: 8\u0026ndash;30 points).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Symptoms\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;2 presents the clinical symptoms and neuropsychological findings. Parkinsonism and visual/visuospatial cognitive impairment were observed in both AD and non-AD groups. However, gait disturbance was significantly more frequent in non-AD patients (p\u0026thinsp;=\u0026thinsp;0.010, Student\u0026rsquo;s t-test).\u003c/p\u003e\n\u003ch3\u003eVoxel-wise Analysis of CSF Biomarkers and Brain Perfusion SPECT\u003c/h3\u003e\n\u003cp\u003eThe correlations between rCBF from SPECT and CSF biomarker levels are summarized in Table\u0026nbsp;3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the AD group: CSF Aβ42 level was positively correlated with rCBF in the right ventral posterior cingulate (Brodmann area [BA] 23), left angular gyrus (BA39), and right medial temporal gyrus (BA21). CSF Aβ40 and sex presented no significant correlations. The Aβ42/40 ratio showed positive correlations in bilateral angular gyri. In contrast, the CSF AD Index [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], CSF t-tau, and p-tau levels were negatively correlated with the angular gyrus. MMSE scores were positively correlated with the inferior and medial temporal gyri, posterior cingulate, and frontal eye fields, and negatively correlated with premotor regions. Age correlated positively with the somatosensory and visual association cortices, and negatively correlated with the insula, posterior cingulate, and parahippocampal gyrus.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSimple Regression Analysis\u003c/h2\u003e \u003cp\u003eThe 88 patients were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;61) and a validation set (n\u0026thinsp;=\u0026thinsp;27) (Table\u0026nbsp;4). Simple regression analyses examined correlations between CSF biomarker concentrations and SUVRs from three VOIs (PC, lt-Par and rt-Par), MMSE score, age and sex. The correlation coefficients are summarized in Table\u0026nbsp;5. Overall, no strong correlations were observed. Age did not present significant correlations with any CSF biomarker. Bland\u0026ndash;Altman plots demonstrated substantial variability between the predicted and actual CSF biomarker levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eValidation Subgroup Comparison\u003c/h2\u003e \u003cp\u003eBoth the training and validation sets showed no significant difference in age or MMSE between the AD and non-AD subgroups. In the training set, sex distribution was balanced. In the validation set, sex distribution differed significantly between AD and non-AD groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Regression Analysis\u003c/h2\u003e \u003cp\u003eA multiple regression model was developed using three regional SUVRs (PC, lt-Par and rt-Par), MMSE score, and age (Table\u0026nbsp;6). Sex was excluded due to a lack of correlation with rCBF. The best performance was observed for CSF Aβ42 prediction (r\u0026sup2; = 0.42, r\u0026thinsp;=\u0026thinsp;0.65) in this research. Other CSF biomarkers (Aβ40, t-tau, and p-tau) indicated only weak predictive performance. Bland\u0026ndash;Altman plots confirmed large variability in predictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study identified AD-typical hypoperfusion in the parietotemporal and PC regions, consistent with findings reported in previous FDG-PET studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Correlations between brain perfusion SPECT\u0026ndash;derived SUVRs and CSF biomarkers were modest but directionally concordant with earlier reports [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Positive correlations were observed between regional SUVRs and the CSF Aβ42/40 ratio in the parietal cortex and PC, whereas CSF tau-related biomarkers exhibited negative correlations in these regions. Among all CSF biomarkers, only CSF Aβ42 demonstrated the highest predictive performance (moderate) with a coefficient of determination of r\u0026sup2; = 0.42.\u003c/p\u003e \u003cp\u003eVoxel-wise analyses revealed that CSF Aβ42 levels were positively correlated with rCBF in PC, the angular gyrus, and medial temporal cortex. Similarly, the CSF Aβ42/40 ratio correlated positively with the angular gyrus, while CSF t-tau and CSF p-tau were negatively correlated within overlapping cortical areas. MMSE score was positively correlated with hypoperfusion in the temporal and cingulate cortices, and negatively correlated with the premotor cortex. Age exhibited both positive and negative correlations across multiple brain regions. These findings are consistent with previous FDG-PET studies reporting the parietotemporal and PC hypometabolism in AD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], providing complementary insights into the CSF biomarker level\u0026ndash;rCBF correlations.\u003c/p\u003e \u003cp\u003eSimple regression analyses identified weak but consistent correlations: CSF Aβ42 correlated with the right parietal cortex (r\u0026thinsp;=\u0026thinsp;0.47), the CSF Aβ42/40 ratio correlated with the bilateral parietal cortex, the CSF AD index correlated with PC and the right parietal cortex, and CSF t-tau correlated negatively with PC. An unexpected negative correlation between CSF Aβ40 levels and parietal SUVRs was observed, although its biological significance remains unclear.\u003c/p\u003e \u003cp\u003eA multiple regression model incorporating SUVRs from the posterior cingulate and bilateral parietal cortices, together with MMSE score and age, achieved the best performance for predicting CSF Aβ42 (r\u0026sup2; = 0.42). In contrast, other CSF biomarkers were only weakly predicted (Table\u0026nbsp;6, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Despite substantial variability, these findings indicate that brain perfusion SPECT has limited yet but measurable potential for estimating CSF Aβ42 concentrations.\u003c/p\u003e \u003cp\u003eCorrelations between reduced rCBF and CSF biomarkers have been investigated using brain perfusion SPECT and FDG-PET [\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Inferior parietal hypoperfusion correlated with CSF Aβ42 using [\u003csup\u003e99m\u003c/sup\u003eTc]-Technetium-99-hexamethyl-propyleneamine oxime ([\u003csup\u003e99m\u003c/sup\u003eTc]-HMPAO)-SPECT, consistent with right parietal cortex findings (r\u0026thinsp;=\u0026thinsp;0.47) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast, [\u003csup\u003e99m\u003c/sup\u003eTc]-Technetium-99-ethyl cysteinate dimer ([\u003csup\u003e99m\u003c/sup\u003eTc]-ECD)-SPECT indicated strong negative correlations with CSF t-tau (r = \u0026minus;\u0026thinsp;0.69) and CSF p-tau (r = \u0026minus;\u0026thinsp;0.70), but not Aβ42 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. FDG-PET demonstrated moderate correlations with CSF Aβ42 (r\u0026thinsp;=\u0026thinsp;0.33) and CSF p-tau (r\u0026thinsp;=\u0026thinsp;0.34), and weaker correlations with CSF t-tau (r\u0026thinsp;=\u0026thinsp;0.24) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. CSF Aβ42 reflects amyloid burden, whereas tau biomarkers represent neurofibrillary pathology [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFDG-PET SUVRs were positively correlated with CSF Aβ42 in PC (r\u0026thinsp;=\u0026thinsp;0.38) and parietal cortex (r\u0026thinsp;=\u0026thinsp;0.39), and parietal SUVRs were associated with CSF Aβ42/40 ratio (r\u0026thinsp;=\u0026thinsp;0.34). Conversely, SUVRs in prefrontal cortex (r = \u0026minus;\u0026thinsp;0.41), PC (r = \u0026minus;\u0026thinsp;0.24), and parietal cortex (r = \u0026minus;\u0026thinsp;0.28) were negatively correlated with CSF t-tau, while prefrontal (r = \u0026minus;\u0026thinsp;0.36) and parietal cortex (r = \u0026minus;\u0026thinsp;0.27) SUVRs were negatively correlated with CSF p-tau [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similar patterns were observed with FDG uptake across the frontal, PC, parietal, and temporal cortices, indicating positive correlations with CSF Aβ42 (r\u0026thinsp;=\u0026thinsp;0.28\u0026ndash;0.35) and negative correlations with CSF t-tau (r = \u0026minus;\u0026thinsp;0.36 to \u0026minus;\u0026thinsp;0.22) and CSF p-tau (r = \u0026minus;\u0026thinsp;0.31 to \u0026minus;\u0026thinsp;0.18) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Previous investigations closely aligned with our results. Brain perfusion SPECT and FDG-PET have indicated significant correlations with CSF biomarkers, however r values were not as high as expected [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Discrepancies involving CSF Aβ40 may reflect methodological differences or cohort characteristics.\u003c/p\u003e \u003cp\u003eRecent imaging-based predictive approaches have focused on amyloid PET [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. While methods such as the easy Z-score imaging system (eZIS) and specific VOI analysis (SVA) predict amyloid PET positivity [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], this study differs by applying SPM12/DARTEL-based voxel-wise analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and directly predicting numerical CSF biomarker concentrations using multiple regression models. Advanced machine-learning approaches using FDG-PET, including support vector machine models, have reported high predictive accuracy for amyloid PET status [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough this research employed a relatively basic machine-learning framework, future implementation of more advanced artificial intelligence (AI) models may improve predictive accuracy and clinical usability. Our research group plans to report these results elsewhere. These findings highlight the potential of this approach that brain perfusion SPECT\u0026ndash;based prediction of CSF biomarkers as a pre-screening tool, particularly CSF Aβ42, may serve as a practical adjunct for early diagnosis, clinical decision-making and reducing caregiver burden. To improve the prediction performance, further improvements are expected through the use of more advanced artificial intelligence models, the conduct of large prospective studies and the combination with neuroimaging modalities.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, it was retrospective with a relatively small sample of 88 cases. Second, the data were collected before the approval of anti-Aβ monoclonal antibodies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and included several atypical AD variants [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], complicating interpretation. Third, the CSF biomarker assays were performed at different laboratories using varying platforms across the 10-year period, introducing potential variability. Moreover, in Japan, health insurance coverage allows only a single amyloid PET or CSF test for Aβ pathology per a patient, limiting simultaneous CSF biomarker validation. Research budgetary constraints also precluded concurrent PET and CSF evaluations. In the ATN framework, brain perfusion SPECT primarily reflects the neurodegeneration (N) marker, which may partly explain the weak correlations with amyloid (A) and tau (T) marker [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Without concurrent amyloid or tau PET [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], the correlation between SPECT SUVRs and CSF biomarker levels remains indirect and modest.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, brain perfusion SPECT revealed AD-typical perfusion patterns and modest correlations between rCBF and CSF biomarker levels, particularly CSF Aβ42. Although SPECT cannot completely substitute amyloid/tau PET or CSF biomarker measurements, brain perfusion SPECT may serve as a pre-screening tool for triaging patients for definitive testing. Further studies are needed to improve the predictive potential of CSF biomarker derived from brain perfusion SPECT in AD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD, Alzheimer’s disease; CBF, cerebral blood flow; rCBF, regional relative cerebral blood flow; DLB, dementia with Lewy bodies; CSF, cerebrospinal fluid; Aβ, amyloid-β; SPECT, single photon emission computed tomography; MMSE, Mini-Mental State Examination; BA, Brodmann area; PET, positron emission tomography; VOI, voxel of interest; SUVR, the standardized uptake value ratio; MRI, magnetic resonance imaging; MCI, mild cognitive impairment; GM, gray matter; SPM12, Statical Parametric Mapping 12 Software; DARTEL, Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra; iNPH, idiopathic normal pressure hydrocephalus; CBS, corticobasal syndrome; PCA, posterior cortical atrophy, REM, rapid eye movement; RBD, rapid eye movement sleep behavior disorder; PD, Parkinson’s disease; CLEIA, chemiluminescent enzyme immunoassay; PC, posterior cingulate; Par, parietal cortex; [\u003csup\u003e123\u003c/sup\u003eI]-IMP,\u0026nbsp;\u003cem\u003eN\u003c/em\u003e-isopropyl-\u003cem\u003ep\u003c/em\u003e-[\u003csup\u003e123\u003c/sup\u003eI] iodoamphetamine; [\u003csup\u003e99m\u003c/sup\u003eTc]-ECD, [\u003csup\u003e99m\u003c/sup\u003eTc]-Technetium-99-ethyl cysteinate dimer; [\u003csup\u003e99m\u003c/sup\u003eTc]-HMPAO, [\u003csup\u003e99m\u003c/sup\u003eTc]-Technetium-99-hexamethylpropyleneamine oxime; [\u003csup\u003e18\u003c/sup\u003eF]-FDG, 2-deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]-fluoro-D- glucose; ATN, amyloid-tau-neurodegeneration\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u0026nbsp; Conceptual idea for the article was by TN and KI. TN performed the literature search. TN, KS, HO, and AT were involved in the clinical neurological and neuropsychological data acquisition. YS, YK, RK, and KI were involved in the imaging data acquisition. TN, TY and KI performed the data analysis. The first draft of the manuscript was written by TN and was reviewed by KI. All authors provided critical revisions on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis manuscript received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committee of Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center). The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest:\u003c/strong\u003e\u0026nbsp; All authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u0026nbsp; All investigations were carried out according to the Declaration of Helsinki. We confirmed that we have read the Journal’s position on issues involved in ethical publication and affirm that this report is consistent with those guidelines. All co-authors have read and approved the submission. This study was examined by the Ethics Committee of the Hyogo Prefectural Harima-Himeji General Medical Center, and informed consent was made by opt-out information disclosure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflicts of interest were disclosed.\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to all hospital staff at the Hyogo Prefectural Harima-Himeji General Medical Center (formerly called Hyogo Brain and Heart Center) for providing us with the chance to perform this research. Special thanks to all patient and staff in the memory clinic in the Neurocognitive Disorders Medical Center at the Hyogo Prefectural Harima-Himeji General Medical Center.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMcKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer\u0026apos;s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer\u0026apos;s Disease. Neurology. 1984;34(7):939-44.\u003c/li\u003e\n \u003cli\u003eMcKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Jr., Kawas CH, et al. The diagnosis of dementia due to Alzheimer\u0026apos;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026apos;s Association workgroups on diagnostic guidelines for Alzheimer\u0026apos;s disease. Alzheimers Dement. 2011;7(3):263-9.\u003c/li\u003e\n \u003cli\u003eHardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer\u0026apos;s disease: progress and problems on the road to therapeutics. Science. 2002;297(5580):353-6.\u003c/li\u003e\n \u003cli\u003eBraak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239-59.\u003c/li\u003e\n \u003cli\u003eJack CR, Jr., Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, et al. Revised criteria for diagnosis and staging of Alzheimer\u0026apos;s disease: Alzheimer\u0026apos;s Association Workgroup. Alzheimers Dement. 2024;20(8):5143-69.\u003c/li\u003e\n \u003cli\u003eJack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer\u0026apos;s disease. Alzheimers Dement. 2018;14(4):535-62.\u003c/li\u003e\n \u003cli\u003eJack CR, Jr., Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539-47.\u003c/li\u003e\n \u003cli\u003eYadollahikhales G, Rojas JC. Anti-Amyloid Immunotherapies for Alzheimer\u0026apos;s Disease: A 2023 Clinical Update. Neurotherapeutics. 2023;20(4):914-31.\u003c/li\u003e\n \u003cli\u003evan Dyck CH, Swanson CJ, Aisen P, Bateman RJ, Chen C, Gee M, et al. Lecanemab in Early Alzheimer\u0026apos;s Disease. N Engl J Med. 2023;388(1):9-21.\u003c/li\u003e\n \u003cli\u003eSims JR, Zimmer JA, Evans CD, Lu M, Ardayfio P, Sparks J, et al. Donanemab in Early Symptomatic Alzheimer Disease: The TRAILBLAZER-ALZ 2 Randomized Clinical Trial. Jama. 2023;330(6):512-27.\u003c/li\u003e\n \u003cli\u003eDubois B, von Arnim CAF, Burnie N, Bozeat S, Cummings J. Biomarkers in Alzheimer\u0026apos;s disease: role in early and differential diagnosis and recognition of atypical variants. Alzheimers Res Ther. 2023;15(1):175.\u003c/li\u003e\n \u003cli\u003eWitbracht MG, Bernstein OM, Lin V, Salazar CR, Sajjadi SA, Hoang D, et al. Education and Message Framing Increase Willingness to Undergo Research Lumbar Puncture: A Randomized Controlled Trial. Front Med (Lausanne). 2020;7:493.\u003c/li\u003e\n \u003cli\u003eDay GS, Rappai T, Sathyan S, Morris JC. Deciphering the factors that influence participation in studies requiring serial lumbar punctures. Alzheimers Dement (Amst). 2020;12(1):e12003.\u003c/li\u003e\n \u003cli\u003eCosterus JM, Brouwer MC, van de Beek D. Technological advances and changing indications for lumbar puncture in neurological disorders. Lancet Neurol. 2018;17(3):268-78.\u003c/li\u003e\n \u003cli\u003eMoulder KL, Besser LM, Beekly D, Blennow K, Kukull W, Morris JC. Factors Influencing Successful Lumbar Puncture in Alzheimer Research. Alzheimer Dis Assoc Disord. 2017;31(4):287-94.\u003c/li\u003e\n \u003cli\u003eDubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer\u0026apos;s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement. 2016;12(3):292-323.\u003c/li\u003e\n \u003cli\u003eEdwards C, Leira EC, Gonzalez-Alegre P. Residency training: a failed lumbar puncture is more about obesity than lack of ability. Neurology. 2015;84(10):e69-72.\u003c/li\u003e\n \u003cli\u003eWright BL, Lai JT, Sinclair AJ. Cerebrospinal fluid and lumbar puncture: a practical review. J Neurol. 2012;259(8):1530-45.\u003c/li\u003e\n \u003cli\u003eYamada T, Kimura Y, Watanabe S, Watanabe A, Honda M, Nagaoka T, et al. Evaluation of amyloid PET positivity using machine learning on (18)F-FDG PET images. Jpn J Radiol. 2025;43(9):1541-9.\u003c/li\u003e\n \u003cli\u003eRuan D, Sun L. Amyloid-beta PET in Alzheimer\u0026apos;s disease: A systematic review and Bayesian meta-analysis. Brain Behav. 2023;13(1):e2850.\u003c/li\u003e\n \u003cli\u003eFantoni ER, Chalkidou A, JT OB, Farrar G, Hammers A. A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer\u0026apos;s Disease. J Alzheimers Dis. 2018;63(2):783-96.\u003c/li\u003e\n \u003cli\u003eFerrando R, Damian A. Brain SPECT as a Biomarker of Neurodegeneration in Dementia in the Era of Molecular Imaging: Still a Valid Option? Front Neurol. 2021;12:629442.\u003c/li\u003e\n \u003cli\u003eNakata T, Shimada K, Iba A, Oda H, Terashima A, Koide Y, et al. Differential diagnosis of MCI with Lewy bodies and MCI due to Alzheimer\u0026apos;s disease by visual assessment of occipital hypoperfusion on SPECT images. Jpn J Radiol. 2024;42(3):308-18.\u003c/li\u003e\n \u003cli\u003eNakata T, Shimada K, Iba A, Oda H, Terashima A, Koide Y, et al. Correlation between noise pareidolia test scores for visual hallucinations and regional cerebral blood flow in dementia with Lewy bodies. Ann Nucl Med. 2022;36(4):384-92.\u003c/li\u003e\n \u003cli\u003eMatsuda H. Role of neuroimaging in Alzheimer\u0026apos;s disease, with emphasis on brain perfusion SPECT. J Nucl Med. 2007;48(8):1289-300.\u003c/li\u003e\n \u003cli\u003eGraff-Radford J, Yong KXX, Apostolova LG, Bouwman FH, Carrillo M, Dickerson BC, et al. New insights into atypical Alzheimer\u0026apos;s disease in the era of biomarkers. Lancet Neurol. 2021;20(3):222-34.\u003c/li\u003e\n \u003cli\u003eOssenkoppele R, Jansen WJ, Rabinovici GD, Knol DL, van der Flier WM, van Berckel BN, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. Jama. 2015;313(19):1939-49.\u003c/li\u003e\n \u003cli\u003eDubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer\u0026apos;s disease: the IWG-2 criteria. Lancet Neurol. 2014;13(6):614-29.\u003c/li\u003e\n \u003cli\u003eKurihara M, Kondo S, Ohse K, Nojima H, Kikkawa-Saito E, Iwata A. Relationship Between Cerebrospinal Fluid Alzheimer\u0026apos;s Disease Biomarker Values Measured via Lumipulse Assays and Conventional ELISA: Single-Center Experience and Systematic Review. J Alzheimers Dis. 2024;99(3):1077-92.\u003c/li\u003e\n \u003cli\u003eHisashi Nojima, Satoshi Ito, Akira Kushida, Aki Abe, Wataru Motsuchi, David Verbel, et al. Clinical utility of cerebrospinal fluid biomarkers measured by LUMIPULSE system. Annals of Clinical and Translational Neurology. 2022;9(12):1898-909.\u003c/li\u003e\n \u003cli\u003eShoji M. CEREBROSPINAL FLUID A\u0026beta;40 AND A\u0026beta;42: NATURAL COURSE AND CLINICAL USEFULNESS. Frontiers in Bioscience. 2002;7:d997-1006.\u003c/li\u003e\n \u003cli\u003eShoji M, Kanai M. Cerebrospinal fluid A\u0026beta;40 and A\u0026beta;42: Natural course and clinical usefulness. J Alzheimers Dis. 2001;3(3):313-21.\u003c/li\u003e\n \u003cli\u003eIshii K, Yamada T, Hanaoka K, Kaida H, Miyazaki K, Ueda M, et al. Regional gray matter-dedicated SUVR with 3D-MRI detects positive amyloid deposits in equivocal amyloid PET images. Ann Nucl Med. 2020;34(11):856-63.\u003c/li\u003e\n \u003cli\u003eAshburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95-113.\u003c/li\u003e\n \u003cli\u003eIshii K. Clinical application of positron emission tomography for diagnosis of dementia. Annals of Nuclear Medicine. 2002;16(8):515-25.\u003c/li\u003e\n \u003cli\u003eWoyk K, Hansen N, Wiltfang J, Lange C, Bouter C. The relationship of (18)F-FDG-PET to other common biomarkers of dementia in a clinical cohort with memory deficits. J Alzheimers Dis Rep. 2025;9:25424823251314392.\u003c/li\u003e\n \u003cli\u003eAsada T, Kakuma T, Tanaka M, Araki W, Lebowitz AJ, Momose T, Matsuda H. A statistical method for predicting amyloid-\u0026beta; deposits from severity, extend, and ratio indices of the (99m)Tc-ECD SPECT. J Alzheimers Dis. 2025:13872877251324222.\u003c/li\u003e\n \u003cli\u003eAndriuta D, Moullart V, Schraen S, Devendeville A, Meyer ME, Godefroy O. Inferior Parietal Cortex Hypoperfusion is the Most Specific Imaging Marker for AD Patients With Positive CSF Biomarker Assays in a Memory Clinic in France. Alzheimer Dis Assoc Disord. 2018;32(2):89-93.\u003c/li\u003e\n \u003cli\u003eHabert MO, de Souza LC, Lamari F, Daragon N, Desarnaud S, Jardel C, et al. Brain perfusion SPECT correlates with CSF biomarkers in Alzheimer\u0026apos;s disease. Eur J Nucl Med Mol Imaging. 2010;37(3):589-93.\u003c/li\u003e\n \u003cli\u003eTapiola T, Alafuzoff I, Herukka SK, Parkkinen L, Hartikainen P, Soininen H, Pirttil\u0026auml; T. Cerebrospinal fluid {beta}-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain. Arch Neurol. 2009;66(3):382-9.\u003c/li\u003e\n \u003cli\u003eJagust WJ, Landau SM, Shaw LM, Trojanowski JQ, Koeppe RA, Reiman EM, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73(15):1193-9.\u003c/li\u003e\n \u003cli\u003eYamakuni R, Abe M, Ukon N, Matsuda H, Takano H, Sawamoto N, et al. Comparison and cutoff values of two amyloid PET scaling methods: centiloid scale and amyloid-\u0026beta; load. Ann Nucl Med. 2025;39(8):799-812.\u003c/li\u003e\n \u003cli\u003eTakenaka A, Nihashi T, Sakurai K, Notomi K, Ono H, Inui Y, et al. Interrater agreement and variability in visual reading of [18F] flutemetamol PET images. Ann Nucl Med. 2025;39(1):68-76.\u003c/li\u003e\n \u003cli\u003eShang C, Sakurai K, Nihashi T, Arahata Y, Takeda A, Ishii K, et al. Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods. Ann Nucl Med. 2024;38(6):460-7.\u003c/li\u003e\n \u003cli\u003eBouter Y, Glasnek RM, Wenzel JM, Bouter C. (18)F-FDG-PET and Multimodal Biomarker Integration: A Powerful Tool for Alzheimer\u0026apos;s Disease Diagnosis. Nucl Med Mol Imaging. 2025;59(6):453-71.\u003c/li\u003e\n \u003cli\u003eAlbert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer\u0026apos;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026apos;s Association workgroups on diagnostic guidelines for Alzheimer\u0026apos;s disease. Alzheimers Dement. 2011;7(3):270-9.\u003c/li\u003e\n \u003cli\u003eMatsuda H, Hanyu H, Kaneko C, Ogura M, Yamao T. Highly specific amyloid and tau PET ligands for ATN classification in suspected Alzheimer\u0026apos;s disease patients. Ann Nucl Med. 2025;39(5):458-65.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the supplementary files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease (AD), cerebrospinal fluid (CSF) biomarkers, single photon emission computed tomography (SPECT), regional relative cerebral blood flow (rCBF), voxel-wise analysis","lastPublishedDoi":"10.21203/rs.3.rs-8804350/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8804350/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) involves amyloid-β (Aβ) and phosphorylated tau (p-tau) pathology. Cerebrospinal fluid (CSF) biomarkers reflect these changes but require invasive lumbar puncture, whereas amyloid positron emission tomography (PET) is less invasive but expensive and typically limited to a single reimbursed scan. 2-Deoxy-2-[\u003csup\u003e18\u003c/sup\u003eF]-fluoro-D-glucose (FDG)-PET is less invasive than lumbar puncture; however, it is not covered by health insurance in Japan. In contrast, brain perfusion single photon emission computed tomography (SPECT) is widely available, repeatable, and cost-effective compared to FDG-PET. We investigated the correlation between regional relative cerebral blood flow (rCBF) measured by SPECT and CSF biomarkers, and evaluated whether rCBF reductions could predict CSF biomarker levels.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 88 patients underwent Mini-Mental State Examination (MMSE), MRI, \u003cem\u003eN\u003c/em\u003e-isopropyl-\u003cem\u003ep\u003c/em\u003e-[\u003csup\u003e123\u003c/sup\u003eI] iodoamphetamine (IMP)-SPECT, and CSF biomarker assessments. SPECT data were normalized to cerebellar counts and co-registered to MRI. Voxel-wise analyses identified the regions where decreased rCBF correlated with CSF biomarkers. Simple regression evaluated correlations between the standard uptake value ratios (SUVRs) of posterior cingulate (PC), bilateral parietal cortices, MMSE, age, sex and biomarker levels. Multiple regression models incorporated the three SUVRs, MMSE, and age. Predicting validity was assessed using correlation coefficient (r), coefficient of determination (r\u0026sup2;), and Bland\u0026ndash;Altman analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVoxel-wise analysis revealed positive correlations between CSF Aβ42 level and CBF in PC and angular gyrus, while CSF t-tau and p-tau correlated negatively with parietal hypoperfusion. CSF Aβ40 showed no significant correlations. Simple regression demonstrated weak correlations, such as CSF Aβ42 with right parietal cortex (r\u0026thinsp;=\u0026thinsp;0.47). Multiple regression yielded moderate predictability for CSF Aβ42 (r\u0026sup2; = 0.42), whereas other biomarkers were poorly predicted.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSPECT revealed AD-typical hypoperfusion patterns and demonstrated modest potential to estimate CSF Aβ42 levels, but not CSF Aβ40, t-tau, or p-tau. Although SPECT cannot substitute CSF biomarker measurements or amyloid/tau PET, SPECT may serve as a pre-screening tool to identify patients requiring definitive biomarker testing.\u003c/p\u003e","manuscriptTitle":"Estimating cerebrospinal fluid biomarkers using brain perfusion SPECT in Alzheimer’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:07:10","doi":"10.21203/rs.3.rs-8804350/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"196bb075-2f20-4d5b-98c5-1c87f0e7cb68","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T01:24:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 10:07:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8804350","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8804350","identity":"rs-8804350","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}