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Published in final edited form as: Addict Neurosci. 2026 Jan 22;19:100253. doi: 10.1016/j.addicn.2026.100253 Search in PMC Search in PubMed View in NLM Catalog Add to search Hippocampal Volume and Brain Tau Pathology in Opioid Use Disorder: Associations with Non-Fatal Opioid Overdose Delaney McKinstry Delaney McKinstry 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Delaney McKinstry 1, * , Zhenhao Shi Zhenhao Shi 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Zhenhao Shi 1, * , Astrid P Ramos-Rolón Astrid P Ramos-Rolón 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Astrid P Ramos-Rolón 1 , Jeffrey Phillips Jeffrey Phillips 3 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Jeffrey Phillips 3 , Sandhitsu Das Sandhitsu Das 2 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Sandhitsu Das 2 , Xinyi Li Xinyi Li 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Xinyi Li 1 , Nathan M Hager Nathan M Hager 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Nathan M Hager 1 , Timothy Pond Timothy Pond 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Timothy Pond 1 , Nora D Volkow Nora D Volkow 4 Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892 Find articles by Nora D Volkow 4 , Henry R Kranzler Henry R Kranzler 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 5 Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104 Find articles by Henry R Kranzler 1, 5 , Jacob G Dubroff Jacob G Dubroff 3 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Jacob G Dubroff 3 , Ilya M Nasrallah Ilya M Nasrallah 3 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Ilya M Nasrallah 3 , Corinde E Wiers Corinde E Wiers 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 3 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 Find articles by Corinde E Wiers 1, 3 Author information Article notes Copyright and License information 1 Center for Studies of Addiction, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 2 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 3 Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 4 Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892 5 Mental Illness Research, Education and Clinical Center, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104 * The first two authors contributed equally. Author contributions Delaney McKinstry : data curation (supportive), formal analysis (equal), investigation (supportive), validation (supportive), visualization (equal), original draft preparation (supportive), review & editing (supportive). Zhenhao Shi : data curation (supportive), formal analysis (equal), investigation (supportive), methodology (supportive), software (equal), validation (supportive), visualization (equal), original draft preparation (supportive), review & editing (supportive). Astrid P. Ramos-Rolón : data curation (supportive), methodology (supportive), software (equal), review & editing (supportive). Jeffrey Phillips : data curation (supportive), formal analysis (equal), methodology (supportive), resources (supportive), software (equal), validation (supportive), visualization (equal), review & editing (supportive). Sandhitsu Das : data curation (supportive), formal analysis (equal), methodology (supportive), resources (supportive), software (equal), visualization (equal), review & editing (supportive). Xinyi Li : data curation (supportive), review & editing (supportive). Nathan M. Hager : methodology (supportive), review & editing (supportive). Timothy Pond : data curation (supportive), project administration (supportive), review & editing (supportive). Nora D. Volkow : conceptualization (supportive), review & editing (supportive). Henry R. Kranzler : data curation (supportive), funding acquisition (equal), resources (supportive), review & editing (supportive). Jacob G. Dubroff : data curation (supportive), resources (supportive), review & editing (supportive). Ilya M. Nasrallah : data curation (supportive), formal analysis (supportive), resources (supportive), review & editing (supportive). Corinde E. Wiers : conceptualization (lead), data curation (lead), formal analysis (equal), funding acquisition (equal), investigation (lead), methodology (lead), project administration (lead), resources (lead), software (equal), supervision (lead), validation (equal), visualization (equal), original draft preparation (lead), review & editing (lead). ✉ Correspondence: Corinde E. Wiers, PhD, 3535 Market St Ste 500, Philadelphia, PA 19104, [email protected] Issue date 2026 Jun. PMC Copyright notice PMCID: PMC13052477 NIHMSID: NIHMS2141715 PMID: 41947888 The publisher's version of this article is available at Addict Neurosci Abstract Opioid use disorder (OUD) is associated with high rates of overdose (OD)-related morbidity and mortality. OD can cause hypoxic-ischemic injury to oxygen-sensitive brain regions such as the hippocampus. Post-mortem studies show Alzheimer’s disease-like hyperphosphorylated tau pathology in the brains of individuals with OUD. Neurocognitive impairments in individuals with OUD may reflect incipient dementia and contribute to poor clinical outcomes. Alternatively, OUD and OD could be independent risk factors for Alzheimer’s disease. To date, no study has evaluated the effects of non-fatal ODs or chronic OUD on hippocampal volume and tau deposition in the human brain in vivo . To fill this gap, we examined hippocampal volumes in OUD individuals (n=60) and healthy controls (HC, n=30) using T1-weighted magnetic resonance imaging (MRI). We found lower bilateral hippocampal volumes in OUD patients than HCs (p<0.001), but no differences between OUD individuals with a history of OD and those without (NOD) (p=0.92). We measured brain tau deposition using Positron Emission Tomography (PET) with [ 18 F]PI-2620 in n=4 HC, n=4 OUD-NOD, and n=4 OUD-OD individuals, and found no difference in brain tau between groups. Functional MRI assessment of episodic memory showed no differences in memory performance or hippocampal activity between groups, although OUD-OD individuals had poorer performance than HC with a medium effect size (d=0.56). In summary, we confirm prior findings of smaller hippocampal volumes in participants with OUD than in HC. However, with a limited sample size, our findings do not show evidence of brain tau deposition in OUD participants with or without OD histories. Keywords: Brain aging, Episodic memory, Fentanyl, Magnetic resonance imaging, Positron emission tomography 1. Introduction Opioid use disorder (OUD) is a chronic, relapsing condition that remains a major public health crisis in the United States, largely due to the alarming rates of opioid overdose (OD). In 2023, the Centers for Disease Control and Prevention reported 79,358 opioid-related OD deaths, primarily driven by illicit fentanyl. Opioid ODs are frequently fatal—with estimated mortality rates ranging from 4–18% [ 1 ], and OD-related deaths have become the leading cause of mortality among younger adults [ 2 ]. The acute respiratory depression induced by opioid OD leads to cerebral hypoxia and injury to brain regions vulnerable to ischemia, including the hippocampus and frontal cortex [ 3 – 5 ]. Beyond the immediate consequences of OD, chronic opioid use has been associated with structural brain abnormalities [ 6 ] and widespread neurocognitive deficits, particularly of episodic memory [ 7 , 8 ]. Despite these findings, the neurobiological mechanisms that underlie these cognitive impairments and brain changes in individuals with OUD are poorly understood, including whether these may differ from the changes caused by OD. Emerging evidence from postmortem studies suggests that tauopathy—abnormal accumulation of hyperphosphorylated tau protein, a hallmark of Alzheimer’s disease (AD)—may contribute to opioid-related brain pathology. Specifically, elevated levels of AD-like hyperphosphorylated tau with a mix of 3 and 4 microtubule binding region repeats (3R/4R tau) have been reported in the hippocampus, entorhinal cortex, and frontal cortex of individuals with a history of opioid use [ 9 – 12 ]. In addition to its central role in AD pathophysiology, hyperphosphorylated tau accumulation has also been observed in traumatic brain injury and aging-related neurodegeneration [ 13 ]. Positron emission tomography (PET) imaging with tau-specific radiotracers provides a powerful tool to assess tau deposition in vivo . Second-generation tracers such as [ 18 F]PI-2620 offer improved specificity and reduced off-target binding compared to earlier compounds [ 14 , 15 ]. These tracers reliably detect tau pathology in AD [ 13 , 16 , 17 ], other neurodegenerative diseases [ 14 , 15 ], and aging [ 18 ]. Tau PET binding correlates with cognitive performance [ 16 , 17 ], hippocampal activity during memory tasks [ 19 ], and postmortem measures of tau burden [ 20 , 21 ], reinforcing its utility as a biomarker of neurodegeneration. Here, we examined whether individuals with OUD, particularly those with a history of non-fatal OD, exhibit signs of AD-like brain pathology, i.e., hippocampal brain atrophy, tau pathology, and cognitive impairment. Using a multimodal neuroimaging approach that combined tau PET, structural and functional MRI, and a hippocampus-dependent episodic memory task, we evaluate the impact of non-fatal opioid OD and chronic opioid exposure on the brain. We hypothesized that individuals with OUD and a history of OD will show smaller hippocampal volumes, greater tau accumulation and functional abnormalities in memory-related brain regions than individuals with OUD without an OD history, and that both groups will differ from healthy controls. These findings could shed light on the long-term neurobiological consequences of opioid exposure and of non-fatal ODs. 2. Methods 2.1. Participants The [ 18 F]PI-2620 PET study was conducted in accordance with the Declaration of Helsinki, and all procedures were approved by the University of Pennsylvania’s Institutional Review Board. The study was registered on clinicaltrials.gov ( NCT05651516 ). English-speaking individuals aged 18–60 years gave informed consent to participate in the study and were grouped as follows: (1) healthy control participants (HCs), (2) individuals with OUD but no history of OD (OUD-NOD), and (3) individuals with OUD and at least one documented non-fatal, naloxone-reversed opioid OD within the past five years (OUD-OD). The 5-year time frame was selected to reduce heterogeneity from remote overdose events, as tau-related neuropathological changes following hypoxic or ischemic insults may diminish over time. HC participants had no lifetime history of substance use disorders (excluding cannabis or nicotine), reported no opioid use in the past 30 days, and tested negative for all drugs (except cannabis) via urine drug screens at the screening, MRI, and PET visits to ensure absence of substances that could confound psychiatric testing. Participants in the OUD groups met DSM-5 criteria for lifetime OUD and were enrolled in stable medication-assisted treatment. Exclusion criteria for all groups included a positive HIV test at screening, pregnancy or breastfeeding, weight >350lbs, self-reported claustrophobia or other contraindications to MRI (such as medical problems that could impact brain function, e.g., seizures, stroke and traumatic brain injury, head trauma with loss of consciousness), history of epilepsy or seizure disorder, current serious psychiatric disorder that could interfere with study participation, and self-reported heavy daily use of psychoactive substances. A total of 90 participants completed structural MRI as part of the P30 Penn PET Addiction Center of Excellence and were included in the analysis of hippocampal gray matter volumes [ 22 – 24 ]. Of the 90 participants, 30 were HCs, 30 were OUD-NOD, 21 were OUD-OD ( Table 1 ), and 9 OUD patients had no OD history assessment. The OUD patients were either enrolled in ongoing treatment with methadone or buprenorphine or had recently completed opioid detoxification in preparation for injectable naltrexone treatment (see Table 1 ). A subset of n=12 participants additionally underwent PET imaging with [ 18 F]PI-2620 (n=4 HC, n=4 OUD-NOD, n=4 OUD-OD) and n=14 an episodic memory functional MRI (fMRI) task (n=4 HC, n=4 OUD-NOD, n=6 OUD-OD) ( Table S1 ). Table 1. Participant characteristics of the structural magnetic resonance imaging sample. Variable HC (n=30) OUD-NOD (n=30) OUD-OD (n=21) p-value a Age (years) p=0.031 Mean (SD) 31.27 (11.37) 30.37 (8.91) 38.24 (12.83) Sex p=0.005 Female, No. (%) 17 (56.67%) 5 (16.67%) 9 (42.86%) Male, No. (%) 13 (43.33%) 25 (83.33%) 12 (57.14%) Race p=0.15 White, No. (%) 21 (70.00%) 26 (86.67%) 19 (90.48%) Black, No. (%) 4 (13.33%) 4 (13.33%) 2 (9.52%) Asian, No. (%) 2 (6.67%) 0 (0.00%) 0 (0.00%) Other, No. (%) 3 (10.00%) 0 (0.00%) 0 (0.00%) Ethnicity p=0.38 Hispanic, No. (%) 2 (6.67%) 0 (0.00%) 1 (4.76%) Non-Hispanic, No. (%) 28 (93.33%) 30 (100.00%) 20 (95.24%) Active MOUD p=0.053 Methadone, No. (%) – 3 (10.00%) 6 (28.57%) Buprenorphine, No. (%) – 3 (10.00%) 5 (23.81%) No medication, No. (%) – 24 (80.00%) 10 (47.62%) Open in a new tab Abbreviations: HC, healthy control; MOUD, medication for opioid use disorder; NOD, no history of overdose; OUD, opioid use disorder; OD, history of overdose; SD, standard deviation. a p-values were from χ 2 tests for categorical variables and one-way analyses of variance for numerical variables. 2.2. Assessments Before undergoing imaging, participants completed the Addiction Severity Index (ASI) [ 25 ], Pittsburgh Sleep Quality Index (PSQI) [ 26 ], Mini-Mental State Examination (MMSE) [ 27 ], and Shipley Institute of Living Scale [ 28 ] to assess drug use severity, sleep quality, cognitive function, and intelligence quotient (IQ), respectively. For the [ 18 F]PI-2620 PET study participants, history of OD (including absence of OD) was assessed using the in-house Drug Overdose Survey (see Supplementary Materials ) and confirmed by available medical records. The Survey also assessed the number of lifetime non-fatal ODs, which were defined as taking so high a dose of opioids that treatment with naloxone was required for opioid reversal, regardless of whether the dose may have been fatal. For the structural MRI-only OUD participants, history of OD (including absence of OD) was assessed by the ASI, medical history interview, and available medical records. ODs were defined as opioid ODs that required intervention by someone to recover, not simply sleeping it off, and included intentional suicide attempts. 2.3. MRI data acquisition For participants who underwent structural MRI only, the MRI was performed on a 3T Siemens TimTrio scanner at the University of Pennsylvania, and T1-weighted multi-echo magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images were acquired with repetition time (TR)/echo time (TE)=1510/3.71 ms, flip angle (FA)=9°, field of view (FOV)=256×192 mm 2 , voxel size=1×1×1 mm 3 , 160 slices. For those who underwent both structural and functional MRI, the MRI was performed on a 3T Siemens Prisma scanner, and we acquired MPRAGE images with TR/TE=2430/2.24 ms, FA=8°, FOV=256×240 mm 2 , voxel size=0.8×0.8×0.8 mm 3 , 208 slices. For fMRI, T2*-weighted blood oxygen level-dependent images were acquired on a 3T Siemens Prisma scanner using a single-shot gradient-echo EPI sequence with TR/TE=800/37 ms, FA=52°, FOV=208×208 mm 2 , voxel size=2×2×2 mm 3 , 72 slices parallel to the anterior-posterior commissural line, multi-band acceleration factor=8. 2.4. Episodic memory fMRI task During the acquisition of fMRI data, participants completed the episodic memory task adapted from [ 29 ]. The task was divided into two phases: the encoding phase was completed during MRI, and the retrieval phase was completed outside of the MRI scanner at 30 min after the completion of the encoding phase (see Figure S1 ). During the encoding phase, participants were presented with 176 pictures in a pseudorandom order. Each picture was displayed for 1500 ms. Of these, 88 pictures were preceded by a pre-stimulus cue in the form of the letter “O”. while the remaining 88 pictures were preceded by the cue “X”. The cue appeared for 750 ms. For pictures following the cue “O”, participants were asked to determine whether the object depicted was more likely to be found indoors or outdoors (the “location” judgment). For pictures following the cue “X”. participants were asked to determine whether the object depicted could fit into a shoebox (the “shoebox” judgment). Responses were made by pressing one of two keys on an MRI-compatible response device, using the index and middle fingers of the dominant hand. Interstimulus intervals between the cues and the pictures were sampled from an exponential distribution with a mean of 2500 ms. The encoding phase was divided into two MRI runs of equal length. Participants were asked to respond as quickly and accurately as possible. They were not informed about the retrieval phase of the task. During the retrieval phase, participants were presented with the 176 “old” pictures from the encoding phase and another 88 “new” pictures in a pseudorandom order. They were asked to indicate whether each picture was old or new on a 5-point scale: “sure old”, “maybe old”, “don’t know”, “maybe new”, and “sure new”. For each picture identified as “sure old” or “maybe old”, participants were asked to indicate the type of judgment (location/shoebox) during encoding: “sure location”, “maybe location”, “don’t know”, “maybe shoebox”, and “sure shoebox”. Our analysis focused on participants’ performance on the new-vs-old question. For each participant, a receiver operating characteristic (ROC) curve was constructed by computing the true positive and false positive rates across a range of thresholds. Episodic memory performance was indexed by the area under the ROC curve (ROC AUC). 2.5. MRI data analysis For structural MRI, hippocampal volumes were segmented from the T1-weighted MRI scans using the Automatic Segmentation of Hippocampal Subfields (ASHS) pipeline, performed in ITK-SNAP. Segmentation protocols followed established methods developed for in vivo structural MRI data [ 30 ]. Total intracranial volume was estimated using ASHS-HarP 1.0.2. Functional MRI images were analyzed using SPM12 (Wellcome Trust Centre for Neuroimaging, London, UK) and underwent standard preprocessing steps including slice time correction, motion correction, coregistration with the structural MRI images, normalization to the Montreal Neurological Institute (MNI) space, and spatial smoothing by an 8-mm full width at half maximum Gaussian filter. We used a head motion threshold of absolute displacement <1 voxel and framewise displacement <0.5 mm. Participants had mean absolute and framewise displacements of 0.63 mm (range=0.14–2.50) and 0.09 mm (range=0.04–0.21), respectively. Therefore, we did not exclude any participants from subsequent analysis. Pictures presented during the encoding phase that were later correctly identified as either “sure old” or “maybe old” were categorized as “remembered”, and those that were later identified as “don’t know”, “maybe new”, or “sure new” were categorized as “forgotten”. Task conditions, including the remembered and forgotten pictures and their respective cues, were convolved by the canonical hemodynamic response function. The convolved regressors and the six rigid-body motion parameters and run-specific constant terms were entered in individual-level general linear models. Neural activity related to episodic memory encoding was evaluated by the “remembered vs. forgotten” contrast. The contrast value of the hippocampus, defined by the Neuromorphometrics atlas ( www.neuromorphometrics.com ), was extracted for subsequent analysis. 2.6. PET data acquisition All [ 18 F]PI-2620 PET scans were conducted on the PennPET Explorer [ 31 , 32 ]. Participants received an IV bolus injection of [ 18 F]PI-2620 under the direct supervision of a Nuclear Medicine Authorized User (mean injection dose = 5.44 ± 0.38 mCi; no group differences, see Table 1 ). Participants underwent a 30-min brain PET/CT scan performed starting at 45 min post injection of [ 18 F]PI-2620. A low-dose CT scan was acquired according to standard PET/CT imaging procedures for attenuation correction. All images were reconstructed using standard reconstruction techniques. 2.7. PET data analysis All PET/CT images were processed and analyzed using PMOD software (version 4.2, PMOD Technologies Ltd., Zurich, Switzerland). PET images were aligned to each participant’s T1-weighted MRI using Advanced Normalization Tools (ANTs). Standardized Uptake Value Ratio (SUVr) maps were created by dividing each voxel’s intensity by the mean signal within a cerebellar gray matter reference region. Regions of interest included the hippocampus, the whole cortical gray matter, and the subcortical gray matter. 2.8. Statistical analysis Statistical analysis was conducted in R (version 4.2.2). One-way ANOVAs and χ 2 tests examined group differences in numerical and categorical variables, respectively. For the group comparisons on hippocampal volume, age, sex, and intracranial volume were entered as covariates of no interest, and their main effects were included in the model. A separate model examined the interaction between sex and group by adding the interaction term in the model. A significant main effect of group was followed by planned comparisons between the HC and OUD individuals (i.e., OUD-NOD and OUD-OD combined), and between OUD-NOD and OUD-OD individuals, using the Sidak adjustment for multiple comparisons. Exploratory pairwise comparisons and the corresponding uncorrected p-values and effect sizes are summarized in Table S2 . Exploratory separate analyses of the left and right hippocampus are summarized in Table S3 . 3. Results 3.1. Hippocampal volume We found significant differences in hippocampal volume across groups (F(2,75)=12.76, p<0.001, partial η 2 =0.25; HC, 6753 mm 3 , 95% CI=[6539,6967]; OUD-NOD, 6008 mm 3 , 95% CI=[5764,6252]; OUD-OD, 6073 mm 3 , 95% CI=[5810,6336]). Post hoc comparisons showed significantly smaller hippocampal volumes in OUD individuals compared to HC (t(75)=5.01, p<0.001, Cohen’s d=1.22; difference=713 mm 3 , 95% CI=[338,1037]), but no difference between OUD-OD and OUD-NOD subgroups (t(75)=−0.36, p=0.92, Cohen’s d=−0.11; difference=−65 mm 3 , 95% CI=[−477,346]) (see Figure 1 ). Figure 1. Open in a new tab Hippocampal volumes across groups (adjusted for sex, age, and total intracranial volume), measured by structural magnetic resonance imaging. Abbreviations: HC, healthy control; OUD, opioid use disorder; NOD, no history of overdose; OD, history of overdose. ***: p<0.001. Additionally, there was a significant sex-by-group interaction (F(2,73)=3.17, p=0.048, partial η 2 =0.08). Post hoc comparisons showed a larger group effect among women (F(2,73)=13.28, p<0.001, η 2 =0.27; HC, 6688 mm 3 , 95% CI=[6392,6985]; OUD-NOD, 5647 mm 3 , 95% CI=[5108,6186]; OUD-OD, 5608 mm 3 , 95% CI=[5215,6001]) than men (F(2,73)=3.31, p=0.042, η 2 =0.08; HC, 6767 mm 3 , 95% CI=[6437,7096]; OUD-NOD, 6261 mm 3 , 95% CI=[6026,6496]; OUD-OD, 6482 mm 3 , 95% CI=[6128,6837]). Among women, there were smaller hippocampal volumes in OUD individuals compared to HC (t(73)=−5.00, p<0.001, Cohen’s d=−1.87, difference=−1060 mm 3 , 95% CI=[−1545,−576]), but no difference between OUD-OD and OUD-NOD subgroups (t(73)=−0.12, p=0.99, Cohen’s d=−0.07, difference=−38 mm 3 , 95% CI=[−769,691]). Among men, there was a trend for smaller hippocampal volumes in OUD individuals compared to HC (t(73)=−2.11, p=0.076, Cohen’s d=−0.70, difference=−395 mm 3 , 95% CI=[−824,33]), but no difference between OUD-OD and OUD-NOD subgroups (t(73)=1.05, p=0.51, Cohen’s d=0.39, difference=221 mm 3 , 95% CI=[−262,704]). The difference in hippocampal volume between OUD and HC individuals remained significant after including the 9 OUD patients whose OD history was not assessed (total n=90) (F(1,85)=27.09, p<0.001, partial η 2 =0.24; HC, 6750 mm 3 , 95% CI=[6541,6960]; OUD, 6039 mm 3 , 95% CI=[5874,6205]; difference=711 mm 3 , 95% CI=[439,983]). The difference also remained significant after controlling for scanner type (F(1,84)=11.59, p=0.001), while the effect of scanner type was not significant (F(1,84)=0.65, p=0.42). Similarly, there was a significant sex-by-group interaction (F(1,84)=4.77, p=0.032, partial η 2 =0.05; female HC, 6708 mm 3 , 95% CI=[6418,6998]; female OUD, 5671 mm 3 , 95% CI=[5360,5982]; male HC, 6749 mm 3 , 95% CI=[6427,7072]; male OUD, 6295 mm 3 , 95% CI=[6121,6468]]). Post hoc comparisons showed a larger difference between OUD and HC among females (t(84)=5.18, p<0.001, Cohen’s d=1.90, difference=1037 mm 3 , 95% CI=[580,1493]) than males (t(84)=2.56, p=0.025, Cohen’s d=0.81, difference=455 mm 3 , 95% CI=[50,860]). The sex-by-group interaction remained significant after controlling for scanner type (F(1,83)=4.13, p=0.045), while the effect of scanner type was not significant (F(1,83)=0.088 p=0.77). 3.2. Tau deposition Hippocampal tau deposition did not significantly differ across groups (F(2,9)=0.85, p=0.46, η 2 =0.16; HC, 1.05, 95% CI=[0.95,1.15]; OUD-NOD, 0.97, 95% CI=[0.87,1.07]; OUD-OD, 1.03, 95% CI=[0.93,1.13]) (see Figure 2 , Figure 3 , Figure S2 ). Similarly, tau deposition did not differ significantly across groups in the cortical gray matter (F(2,9)=0.41, p=0.68, η 2 =0.08; HC, 1.04, 95% CI=[0.95,1.12]; OUD-NOD, 1.04, 95% CI=[0.95,1.12]; OUD-OD, 1.08, 95% CI=[0.99,1.16]) (see Figure S3 left) or the subcortical gray matter (F(2,9)=2.37, p=0.15, η 2 =0.35; HC, 0.98, 95% CI=[0.89,1.08]; OUD-NOD, 0.89, 95% CI=[0.79,0.98]; OUD-OD, 1.01, 95% CI=[0.92,1.10]) (see Figure S3 right). Figure 2. Open in a new tab Hippocampal tau deposition level across groups, measured by positron emission tomography. Abbreviations: HC, healthy control; OUD, opioid use disorder; NOD, no history of overdose; OD, history of overdose; SUVr, standardized update value ratio. Figure 3. Open in a new tab Tau SUVr maps of individual participants, shown at: MNI space coordinates x=−28 (A), encompassing the left entorhinal cortex and hippocampus, and x=0 (B), representing the midline section. Each image includes participant age (in years) and sex. Abbreviations: HC, healthy control individuals; NOD, individuals with opioid use disorder but no history of opioid overdose, OD, individuals with opioid use disorder and a history of opioid overdose; SUVr, standardized update value ratio; F, female; M, male; MNI, Montreal Neurological Institute. 3.3. Episodic memory Performance on the fMRI episodic memory task, indexed by the ROC AUC, did not differ significantly across groups (F(2,11)=0.40, p=0.68, η 2 =0.07; HC, 0.83, 95% CI=[0.68,0.98]; OUD-NOD, 0.80, 95% CI=[0.65,0.95]; OUD-OD, 0.75, 95% CI=[0.63,0.87]) (see Figure S4 ). Exploratory post hoc analysis revealed a medium effect (Cohen’s d=0.56), with a smaller ROC AUC in the OUD-OD than the HC group. Hippocampal neural response to remembered vs. forgotten items did not differ significantly across groups (F(2,11)=0.90, p=0.43, η 2 =0.14; HC, −0.06, 95% CI=[−0.59,0.47]; OUD-NOD, 0.28, 95% CI=[−0.25,0.80]; OUD-OD, −0.13, 95% CI=[−0.56,0.30]) (see Figure S5 ). 4. Discussion We examined the neurobiological correlates of chronic opioid use and non-fatal opioid OD with hippocampal volume, in vivo tau deposition, and memory function using multimodal neuroimaging and cognitive assessments. We found that individuals with OUD exhibited significantly smaller hippocampal volumes than healthy controls, consistent with prior reports of opioid-related neurotoxicity and structural brain alterations [ 4 , 5 , 33 , 34 ]. However, in contrast to our previous report of smaller hippocampal volumes in OUD individuals with a history of OD vs. NOD [ 35 , 36 ], we were unable to replicate this finding in the current, larger sample. This suggests that hippocampal atrophy may be associated with OUD more broadly, rather than discrete OD-related effects. Notably, whereas our prior work primarily included individuals using either prescription opioids or heroin, the present sample was collected during a period in which fentanyl had become the predominant opioid in the drug supply. Given the substantially greater potency of fentanyl, it is possible that recurrent hypoxic or near-OD events occur without being reliably recognized or recalled, potentially reducing the distinction between OD and NOD groups. As such, the absence of OD-specific hippocampal differences in the current study may reflect challenges in classifying OD exposure in fentanyl-dominant patient populations rather than the absence of OD-related neurobiological effects. Importantly, we also investigated tau pathology in vivo using the PET tracer [ 18 F]PI-2620 in a subsample of 4 HC and 8 OUD participants. Contrary to our hypothesis and prior postmortem findings of tau hyperphosphorylation in the brains of individuals with OUD [ 9 – 12 ], we found no evidence of significant tau accumulation in OUD-OD or OUD-NOD individuals relative to HC. This lack of corroboration may reflect the limited statistical power from our small sample size as well as differences between in vivo and postmortem methodologies, disease stage, or the sensitivity of tau PET to detect diffuse or early-stage pathology [ 37 , 38 ]. Postmortem studies suggest that opioid-related tau pathology may occur in a focal or patchy distribution and involve 3R or 4R tau species that may not be optimally detected by current PET ligands, which are most sensitive to 3R/4R tau [ 9 , 10 , 12 ]. Alternatively, tau accumulation may develop later in the disease course, or only in the presence of comorbid factors (e.g., traumatic brain injury, aging, or genetic vulnerability). It is also possible that OD-related hypoxia triggers transient biochemical tau alterations without leading to persistent fibrillary tau aggregates that are detectable by PET [ 14 ]. Behaviorally, OUD-OD participants demonstrated impaired performance on a visual episodic memory task relative to healthy controls, with a medium effect size. Episodic memory deficits are well documented in OUD [ 7 , 8 ], and hippocampal dysfunction is likely a contributing factor. While our imaging data do not implicate tau as a driving mechanism in this cohort, other neuropathological changes, such as synaptic loss, neuroinflammation, or white matter injury, may underlie these cognitive deficits. Moreover, the role of hypoxia-induced injury following OD events, particularly in the hippocampus and medial temporal lobes, warrants further exploration [ 3 – 5 ]. This study represents a first step toward understanding how opioid OD may impact neurodegenerative pathways. However, several limitations must be acknowledged. The sample size for the patients who completed PET and fMRI was very small, limiting the power to detect subtle or regionally specific differences. Consequently, it is difficult to determine whether the null findings reflect a true absence of group differences or type II error. Future studies with larger samples and more carefully defined inclusion criteria will be essential to validate these preliminary findings. For example, inclusion criteria could be refined by enrolling patients with a history of OD reversed by naloxone in an emergency room setting, or by limiting inclusion to those patients with OD-related amnesic syndrome; although a recent PET case study in a patient with opioid-associated amnestic syndrome demonstrated a similar negative PET tau finding [ 39 ]. Moreover, future research should include longitudinal designs to determine whether hippocampal volume loss or subtle tau changes progress over time, particularly in those with multiple validated OD events. Biofluid assays from CSF or plasma are increasingly sensitive to a variety of pathways, including but also beyond typical AD-related markers like p-tau, and neurofilament light chain. Together with multi-modal neuroimaging, such investigations will be critical for delineating the neuropathological cascade underlying cognitive decline in OUD. Additionally, examining how factors such as duration of opioid use, type of opioid (prescription opioids vs. heroin vs. fentanyl), naloxone reversal, or comorbid mental health factors (e.g., trauma, mood disorders, anxiety disorders, etc.) or other substance use histories (e.g., nicotine, cannabis, alcohol, stimulants, etc.) influence neurodegeneration may reveal subgroups at higher risk for dementia-like progression. In conclusion, our results suggest that chronic opioid use is associated with reduced hippocampal volume, consistent with past findings of opioid-related neurotoxicity. However, we did not find evidence of increased tau deposition in individuals with OUD or a history of non-fatal OD using tau PET imaging. Memory impairment in the OUD-OD group suggests a possible link between OD-related injury and hippocampal function, though tauopathy does not appear to be a primary mechanism in this early-stage cohort. Larger, longitudinal studies are necessary to determine whether opioid exposure contributes to neurodegenerative disease risk through tau-independent mechanisms or delayed tau accumulation. Supplementary Material 1 NIHMS2141715-supplement-1.pdf (4.9MB, pdf) Highlights: Individuals with OUD had lower hippocampal volume but no evidence of brain tau deposition, compared to healthy controls. History of opioid OD did not affect hippocampal volume or brain tau deposition. Individuals with a history of opioid OD had poorer memory performance than healthy controls. Funding The work was supported by the following National Institutes of Health grants: P30DA046345–05W1 (CEW, IMN, SD, HRK, TP, JGD), K01DA051709 (ZS), T32DA028874 (XL, NMH), T32AG076411 (APRR). Footnotes Declaration of competing interests Dr. Kranzler is a member of advisory boards for Altimmune and Clearmind Medicine; a consultant to Sobrera Pharmaceuticals, Altimmune, Lilly, and Ribocure; the recipient of research funding and medication supplies for an investigator-initiated study from Alkermes and a company-initiated study by Altimmune. The other authors have nothing to disclose. Ethics approval The study was approved by the University of Pennsylvania Institutional Review Board (#852095). Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. 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