About

Ilya M Nasrallah, MD PhD, is an Associate Professor of Radiology at the Hospital of the University of Pennsylvania. He is an active radiologist within the Department of Radiology, practicing at multiple hospitals including Chester County Hospital, Pennsylvania Hospital, Penn Presbyterian Medical Center, and Phoenixville Hospital. His contact information is provided through the University of Pennsylvania's Department of Radiology. Dr. Nasrallah completed his undergraduate studies at Cornell University in 1998, earning a BA. He then obtained his PhD from the University of Pennsylvania in 2004 and his MD from the same institution in 2005. His professional focus involves radiology, with a particular emphasis on neuroimaging and brain-related research, as evidenced by his numerous publications in the field of neuroscience, neurodegeneration, and brain imaging.

Research topics

• Medicine
• Internal medicine
• Radiology
• Cardiology
• Psychology
• Genetics
• Biology
• Pathology
• Neuroscience
• Physical therapy
• Psychiatry
• Oncology
• Molecular biology
• Chemistry
• Cell biology

Selected publications

• Uncovering distinct spatiotemporal trajectories of T‐N mismatch subtypes with likely co‐pathology in Alzheimer's disease using event‐based modeling

  Alzheimer s & Dementia · 2026-05-01

  articleOpen access

  INTRODUCTION: In Alzheimer's disease (AD), tau-neurodegeneration (T-N) mismatch has been proposed to reflect non-AD processes such as transactive response DNA binding protein 43 kDa and vascular disease. We aimed to characterize the spatiotemporal trajectories of T-N mismatch that may reflect non-AD progression. METHODS: We performed T-N regression on 710 Alzheimer's Disease Neuroimaging Initiative participants using cortical thickness and 18F-flortaucipir uptake across 20 cortical regions. SuStaIn, a data-driven phenotype discovery and staging algorithm, was applied to standardized T-N residuals in canonical (N∼T) and vulnerable (N > T) cases. RESULTS: SuStaIn identified three vulnerable subtypes with distinct N > T progression patterns. The posterior and anterior subtypes displayed different, but progressively diffuse mismatch patterns, while the limbic subtype exhibited temporal-limbic progression. Subtypes and SuStaIn stages were associated with distinct clinical features. Their longitudinal trajectories aligned with SuStaIn inferred progression. DISCUSSION: Findings support that T-N mismatch progression captures specific co-pathological processes.

  PublisherDOI

• Temporal Modeling of Amyloid and Tau Trajectories in Alzheimer's Disease Using <scp>PET</scp> and Plasma Biomarkers

  Annals of Neurology · 2026-03-15 · 1 citations

  articleOpen access

  Objective This study aimed to compare positron emission tomography (PET) and plasma‐based temporal modeling of amyloid and tau biomarkers in Alzheimer's disease. Methods Longitudinal amyloid PET (n = 1,097, mean age ± SD = 72.5 ± 7.38 year, 51.4% male), 18 F‐flortaucipir tau‐PET (n = 230, 74.3 ± 7.18 year, 52.2% female), and Fujirebio Lumipulse plasma p‐tau 217 (n = 752, 72.8 ± 6.93 year, 51.3% male) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and University of Pennsylvania Alzheimer's Disease Research Center (Penn ADRC) were used to generate biomarker trajectory models using sampled‐iterative Local approximation (SILA). SILA models using plasma p‐tau 217 were compared to amyloid and tau PET‐based models to estimate amyloid and tau onset, and factors influencing tau onset and time from tau onset to dementia were evaluated for PET and plasma models. Results Plasma and PET models generated similar results for estimated amyloid and tau onset, with stronger model agreement for tau ( r = 0.88[0.86, 0.89], t = 57.4, p &lt; 0.001) than amyloid ( r = 0.75[0.72, 0.77], t = 37.4, p &lt; 0.001) onset. Accuracy of estimated onset compared to actual onset was high within modality (mean absolute error [MAE] ≤ 2.03) with slightly greater error (MAE 3.09–3.42) when comparing across modalities (ie, plasma to PET). For both plasma and PET, earlier tau onset was associated with younger amyloid onset, female sex, and ≥1 apolipoprotein (ApoE) ε4 allele. Earlier dementia onset after tau was associated with later tau onset for both plasma and PET, while male sex was associated with shorter tau to dementia gap in plasma models. Interpretation Temporal modeling of plasma biomarkers provides comparable information to PET‐based models, particularly for tau onset age, and can serve as a widely accessible tool for clinical assessment of biological disease severity. ANN NEUROL 2026

  PublisherDOI

• Medial temporal lobe Tau-Neurodegeneration mismatch from structural imaging and plasma biomarkers

  Brain · 2026-04-17

  articleOpen access

  While tau pathology is closely associated with neurodegeneration in Alzheimer's disease (AD), our prior work using multi-modality imaging revealed that mismatch between tau (T) and neurodegeneration (N) may reflect contributions from non-AD processes. The medial temporal lobe (MTL), an early site of AD pathology, is also a common target of co-pathologies such as limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), often following an anterior-posterior atrophy gradient. Given the susceptibility of MTL to co-pathologies, here we explored T-N mismatch specifically within MTL using plasma ptau217 and MTL morphometry for identifying vulnerabilities and resilience in cognitively impaired or unimpaired AD patients. We parcellated the MTL into 100 spatially contiguous segments and calculated their T-N mismatch using plasma ptau217 as a measure for T and thickness as a marker of N. Based on these mismatch profiles, we clustered 447 amyloid-positive individuals from ADNI cohort into data-driven T-N phenotypes. We characterized the T-N phenotypes by examining their cross-sectional and longitudinal atrophy both within the MTL and across the whole brain, as well as cognitive trajectories. This framework was replicated in an independent cohort and finally translated to a real-world clinical sample of 50 patients undergoing anti-amyloid therapy. Clustering identified three T-N phenotypes with different MTL T-N mismatch profiles, atrophy patterns, and cognitive outcomes, despite comparable AD severity. The "canonical" group, characterized by low T-N residuals (N ∼ T), showed AD-like neurodegeneration patterns. The "vulnerable" group, characterized by disproportionately greater neurodegeneration than tau (N > T), showed atrophy primarily in the anterior MTL that extended into temporal-limbic regions, both in cross-sectional and longitudinal analyses. This group also exhibited neurodegeneration that preceded estimated tau onset and experienced faster cognitive decline across multiple domains, aligning with the typical characteristics of mixed LATE-NC with AD. In contrast, the "resilient" group (N < T) showed minimal atrophy and preserved cognitive function. These phenotypes were reproducible in an independent research cohort. Importantly, in a feasibility study applying the model developed from ADNI to a clinical cohort of patients receiving lecanemab, we identified vulnerable individuals with LATE-like atrophy patterns. This highlights its potential utility for identifying individuals with co-pathology in clinical settings. Our findings demonstrate that T-N mismatch within MTL using MRI and plasma biomarkers can reveal AD groups with varying vulnerability/resilience, with the vulnerable group displaying structural and cognitive outcomes suggestive of LATE-NC. This approach offers a cost-effective strategy for clinical trial stratification and precision medicine for AD therapeutics.

  PublisherDOI

• Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals

  Nature Communications · 2026-04-25

  articleOpen access

  Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease progression due to reliance on cross-sectional data. We introduce Coupled Cross-sectional and Longitudinal Non-negative Matrix Factorization (CCL-NMF) to capture dominant brain aging patterns by simultaneously leveraging cross-sectional and longitudinal neuroimaging data. CCL-NMF allows individuals to co-express multiple patterns, capturing mixed neuropathologic processes. Applied to neuroimaging data from 48,949 individuals from the harmonized iSTAGING study, CCL-NMF identifies seven distinct, reproducible, and biologically relevant neuroanatomical patterns. Subject-specific loading coefficients quantifying the individual expression of these patterns show distinct associations with cognition, genetic, and lifestyle factors. To support broader application, a regression-based tool was developed to estimate loadings in external cohorts without rerunning the full framework. By enabling individualized estimation of distinct brain aging patterns, these findings may improve risk assessment and therapeutic evaluation in neurodegenerative diseases. Although demonstrated using structural MRI, this framework is generalizable to other imaging modalities and biomarker types.

  PublisherOA PDFDOI

• Hippocampal volume and brain tau pathology in opioid use disorder: Associations with non-fatal opioid overdose

  Addiction Neuroscience · 2026-01-18 · 1 citations

  articleOpen access

  • 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. 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.

  PublisherDOI

• Discordance of Dopaminergic Dysfunction and Subcortical Atrophy by α‐Synuclein Status in Sporadic and Genetic Parkinson's Disease

  Movement Disorders · 2026-01-28 · 1 citations

  articleOpen accessSenior authorCorresponding

  Abstract Background Parkinson's disease (PD) is characterized by predominantly neuronal α‐synuclein pathology and dopaminergic dysfunction. Cerebrospinal fluid (CSF) seeding amplification assays (SAA) detect α‐synuclein aggregates in vivo, but not all patients with PD have a positive SAA. This pathological heterogeneity among patients may not be entirely captured by binary results from α‐synuclein SAA positivity (S+) versus negativity (S–). To further dissect this biological variability, we explored spatial neuroimaging differences in S+ versus S– patients. Objective The study aim was to investigate how SAA status influences imaging measures of dopamine denervation and atrophy. Methods We compare SAA status with CSF proteinopathy markers, 123 I‐Ioflupane dopamine transporter (DAT), and magnetic resonance imaging (MRI) in participants with sporadic (n = 490), LRRK2 ‐associated (n = 158), and GBA ‐associated (n = 80) PD from the Parkinson's Progression Markers Initiative (PPMI). Results Between 64% and 95% of participants in these groups have S+ status. For all groups, S+ participants have decreased putamen DAT neurotransmission compared to S– participants, whereas S– participants have reduced MRI volume in basal ganglia structures relative to S+ participants. With striatal DAT/MRI ratios, S+ participants have disproportionately lower putamen DAT uptake relative to atrophy. In exploratory analyses, participants with cognitive impairment or hyposmia are associated with worse DAT/MRI discordance. By CSF markers, S– participants with sporadic PD have higher CSF pTau 181 /amyloid‐β 42 ratio, suggesting Alzheimer's copathology. Conclusions S+ patients exhibit more dopaminergic deficit, whereas S– patients have more subcortical atrophy across sporadic and genetic PD. Together, our findings reveal structure/function and DAT/MRI discordance, providing insight into biomarkers and pathophysiology of synucleinopathy and PD. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

  PublisherOA PDFDOI

• Relationships between heritable dementia risk factors, cardiovascular risk factors in young adulthood, and midlife neuropsychological outcomes

  Journal of Alzheimer s Disease · 2026-01-01

  articleOpen access

  Background Selected cardiovascular factors, APOE4 carriership, and family history (FH) are robust risk factors for Alzheimer's disease and dementia. While cardiovascular risk tends to affect cognition from midlife, it remains unclear whether heritable risk predicts cardiovascular health in young adulthood and midlife, and whether young-adult cardiovascular health predicts midlife cognition. Objective We sought to examine how heritable dementia risk relates to cardiovascular health and how these cardiovascular risk factors in young adulthood predict midlife brain volumes and cognition. Methods We used data from the CARDIA study, which followed 5115 individuals aged 18–30 at baseline over 30 years. Analyses focused on 2808 participants (Mean age = 60, SD = 3.58) who attended the 30-year visit. We examined associations between APOE4 and FH with baseline and 30-year follow-up measures of cardiovascular risk factors (LDL-C, HDL-C, glucose, blood pressure, body mass index (BMI), smoking), cognition, and brain volumes. Results APOE4 carriers with FH had higher LDL-C and lower HDL-C levels as early as young adulthood, persisting into midlife. BMI and smoking were the only cardiovascular risk factors from young adulthood that predicted midlife cognition. There was no association between young adult cardiovascular risk factors and midlife brain volumes, but those with heritable dementia risk had larger brain volumes in regions vulnerable to midlife atrophy. Conclusions APOE4 carriership was associated with an unfavorable lipid profile that started in early adulthood and persisted to later life. Early cardiovascular risk was also associated with midlife cognition, which is earlier than studies typically focusing on later-life cognition.

  PublisherDOI

• Associations of ¹⁸ F‐flortaucipir tau PET with quantitative tau histopathology in the medial temporal lobe

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  Abstract Background 18 F‐Flortaucipir is widely used for positron emission tomography (PET) imaging of Alzheimer’s disease (AD)‐type tau, but its sensitivity in early Braak stages has been questioned, and hippocampal uptake is at least partially confounded by off‐target binding. We investigated associations between antemortem PET uptake and digitally‐quantified tau and TDP‐43 neuropathology. Methods Participants ( n = 14, Figure 1) included 5 people with no/low AD neuropathologic change (ADNC) at autopsy, 2 intermediate, and 7 high. Clinical diagnoses included normal cognition ( n = 2), AD ( n = 5), dementia with Lewy bodies ( n = 2), Parkinson’s disease dementia ( n = 1), corticobasal syndrome ( n = 2), and posterior cortical atrophy ( n = 2). We used the Automated Segmentation of Hippocampal Subfields T1 MRI pipeline to segment anterior and posterior hippocampus, Brodmann’s areas (BA) 35 (transentorhinal cortex) &amp; 36, and entorhinal cortex. 18 F‐Flortaucipir standardized uptake value ratios (SUVRs) were computed relative to inferior cerebellar grey matter and averaged across hemispheres. Postmortem sampling comprised hippocampal subiculum, CA1, CA2, CA3, and dentate gyrus; BA35 and BA36; and entorhinal cortex. FFPE‐brain tissue was immunostained using PHF1 and phospho‐specific TDP‐43 antibodies and digitally imaged. Two different weakly supervised learning algorithms, Wildcat, were trained to identify tau tangles and threads; or somatic and neuritic phosphorylated TDP‐43 (pTDP‐43) inclusions. Each pathology type was quantified by summary statistics on Wildcat heatmaps, averaged over hippocampal subfields; and over BA35/entorhinal cortex (Denning et al., 2024). We computed non‐parametric correlations between SUVRs and pathology measures at a=0.05 with false discovery rate correction. Results Tangles were associated with SUVRs in BA35/entorhinal cortex (Spearman’s r=0.59, p = 0.029; Figure 2A). The mean hippocampal tangle measure was associated with SUVRs in both anterior (r=0.77, p = 0.002) and posterior (r=0.77, p = 0.002) hippocampus (Figure 2B‐C). Associations between SUVR and tau threads were marginally significant (Figure 2D‐F). In BA35/entorhinal cortex, 9/9 intermediate‐high ADNC cases had SUVRs above an established positivity cutoff of 1.23 (Figure 3). PET SUVRs were not associated with somatic or neuritic pTDP‐43 measures. Conclusion Results suggest 18 F‐flortaucipir is sensitive to tau burden in early Braak‐stage regions and primarily reflects neurofibrillary tangles rather than thread‐like tau inclusions.

  PublisherOA PDFDOI

• Medial temporal lobe Tau‐Neurodegeneration <i>mismatch</i> from MRI and plasma biomarkers identifies vulnerable and resilient phenotypes with AD

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: The heterogeneity of Alzheimer's disease (AD) and lack of well-validated markers of non-AD factors (e.g. TDP-43) present a substantial challenge for therapeutics. Our prior work showed discordance between tau (T) and neurodegeneration (N) identified non-AD factors in AD through multi-modality imaging. Here we tried a simplified approach using plasma ptau217 and medial temporal lobe (MTL) morphometry, given this region's common association with co-pathologies, particularly LATE-NC. METHOD: We included 349 ADNI participants (188 cognitively normal, 161 MCI/dementia) with paired T1-MRI and plasma ptau217. The MTL was segmented into subregions and further parcellated into 100 bilateral super-points within regional boundaries. T1-MRI-derived thickness and amygdala volume represented N, and plasma p-Tau217 represented T. T-N residuals, calculated through regression across super-points and amygdala, were used for weighted clustering. RESULT: P-Tau217 showed strong association with MTL atrophy (Figure 1A). Three distinct data-driven T-N groups were identified based on mismatch patterns (Figure 1B), including a canonical group (N∼T), a vulnerable group (N>T) with negative residuals primarily in anterior hippocampal and extrahippocampal areas, and a resilient group (N<T) with positive residuals. After clustering, group comparisons were restricted to the AD continuum (i.e. A+). While groups differed in regional volumes (e.g., amygdala), tau severity did not vary (Table 1), suggesting these patterns were not driven by AD pathology. The vulnerable group, displayed greater anterior MTL atrophy aligning with their T-N residual patterns, while the resilient group had less atrophy in anterior extrahippocampal area (Figure 2A). Outside the MTL, the vulnerable group showed greater anterior limbic atrophy whereas the resilient group showed less (Figure 2B). The T-N groups differed in Clinical Dementia Rating (CDR) with the vulnerable group having the worst ratings and the resilient group the best (Table 1). Notably, the vulnerable group demonstrated greater baseline memory impairment. Longitudinally, the vulnerable group also declined more severely across multiple cognitive domains while resilient group remained most stable (Figure 2C). CONCLUSION: T-N mismatch within MTL using MRI and plasma biomarkers revealed groups with varying vulnerability/resilience, with the vulnerable group displaying patterns of atrophy and cognition suggestive of LATE-NC. It offers a less invasive, cost-effective method for stratifying individuals for therapeutic interventions.

  PublisherOA PDFDOI

• Investigating the combined effects of smoking and amyloid on brain structure in cognitively unimpaired adults using a machine learning‐based MRI marker

  Alzheimer s & Dementia · 2025-12-01

  articleOpen access

  BACKGROUND: Smoking is a well-established risk factor for cardiovascular disease, and its association with neurodegeneration and cognitive decline is an area of ongoing research. Critically, the interplay between smoking, Alzheimer's disease (AD) pathology, and cognitive impairment remains incompletely understood. This study investigated the relationship between smoking, AD pathology as indexed by amyloid-beta (Aβ) deposition, and cognitive performance using SPARE-SM, a novel machine learning-based marker that quantifies smoking-related spatial patterns of abnormalities on individual structural magnetic resonance images (sMRI). METHODS: SPARE-Smoking, derived from N = 37,098 cognitively unimpaired individuals from diverse cohorts, was evaluated in N = 222 individuals who had amyloid (Aβ) status available within +/- 1 year of the MRI scan in a subset of the training cohort. Amyloid deposition was determined using study-specific cut-offs for CSF and PET SUVR measures, categorizing participants as Aβ-/Aβ+. Multivariable regression models were used to assess interactions between Aβ status, smoking history, and age on SPARE-SM scores. Multivariable linear regression models, adjusted for age, sex, and years of education, examined associations between SPARE-SM and cognitive performance. RESULTS: While the proportion of smokers was similar between Aβ+ and Aβ- participants (Table 1), SPARE-SM showed a nuanced relationship with both Aβ and smoking status (Figure 1A). Specifically, SPARE-SM was higher than SM+Aβ- individuals in SM+ Aβ+ individuals (p <0.05) but lower in SM- Aβ+ individuals (p <0.05). Importantly, higher SPARE-SM was associated with worse cognitive performance, whereas simply classifying individuals as smokers or non-smokers showed no associations with cognitive outcomes (Figure 1B). CONCLUSION: These findings suggest a complex relationship between smoking, amyloid pathology, and cognition. The observation that SPARE-SM differed by Aβ in smoking individuals highlights their potential synergistic effects on neurodegeneration. SPARE-SM demonstrated associations with cognitive decline, even when clinical smoking status did not, emphasizing its potential for early risk identification. Further research is needed to disentangle the mechanisms linking smoking, brain changes, amyloid, and dementia.

  PublisherOA PDFDOI

Frequent coauthors

• David A. Wolk

  California University of Pennsylvania

  111 shared

• Christos Davatzikos

  University of Pennsylvania

  108 shared

• Güray Erus

  106 shared

• Lenore J. Launer

  National Institute on Aging

  90 shared

• Mohamad Habes

  89 shared

• R. Nick Bryan

  University of Pennsylvania

  74 shared

• Haochang Shou

  University of Pennsylvania

  68 shared

• Marilyn S. Albert

  Johns Hopkins University

  50 shared