@article{pmid41603617,

title = {Clinical and Imaging Characteristics of Parkinson's Disease with Negative Alpha-Synuclein Seed Amplification Assay},

author = {Sarah M Brooker and Jacopo Pasquini and Seung Ho Choi and David-Erick Lafontant and Seyed-Mohammad Fereshtehnejad and Yashar Zeighami and Piergiorgio Grillo and Giulietta M Riboldi and Houman Azizi and Roqaie Moqadam and Un Jung Kang and Kelly N H Nudelman and Andrew Siderowf and Caroline M Tanner and Thomas F Tropea and Tatiana Foroud and Lana M Chahine and Brit Mollenhauer and Kalpana M Merchant and Douglas Galasko and Christopher S Coffey and Roseanne D Dobkin and Ethan G Brown and Roy N Alcalay and Daniel Weintraub and Kenneth Marek and Tanya Simuni and Paulina Gonzalez-Latapi and Nicola Pavese and Kathleen L Poston and },

doi = {10.1002/mds.70197},

issn = {1531-8257},

year  = {2026},

date = {2026-05-01},

journal = {Mov Disord},

volume = {41},

number = {5},

pages = {1114--1127},

abstract = {BACKGROUND: The cerebrospinal fluid alpha-synuclein seed amplification assay (CSFasynSAA) detects alpha-synuclein aggregation in over 90% of individuals with sporadic PD (sPD). However, the clinical characteristics of sPD with negative CSFasynSAA remain undefined.nnOBJECTIVES: Describe clinical and neuroimaging characteristics of CSFasynSAA-negative sPD individuals in the Parkinson's Progression Markers Initiative (PPMI).nnMETHODS: We identified sPD PPMI participants with a negative CSFasynSAA (SAA-, n = 80) or positive CSFasynSAA (SAA+, n = 856) result at baseline. For comparative analysis between groups, we used a reduced dataset (n = 79 SAA- and n = 237 SAA+) propensity-score matched on age, sex, and time since clinical diagnosis. Clinical parameters, dopamine transporter-single photon emission computed tomography (DAT-SPECT), and magnetic resonance imaging (MRI) brain volumetrics were analyzed.nnRESULTS: The SAA- and matched SAA+ groups had similar motor performance on the Movement Disorder Society Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III) and similar cognitive performance on the Montreal Cognitive Assessment (MoCA) at baseline. The proportion with severe hyposmia was 12% for SAA- versus 73% for SAA+ (P < 0.001). Per PPMI enrollment criteria all participants were classified as having an abnormal DAT-SPECT. There were no significant differences in median quantitative DAT-SPECT measures between groups. The SAA- group showed a higher degree of atrophy in subcortical brain regions including substantia nigra. Longitudinally, 14.3% of SAA- participants had a change in diagnosis versus 0.9% of SAA+ participants.nnCONCLUSIONS: At baseline, SAA- sPD PPMI participants have a substantially lower rate of hyposmia, but otherwise cannot be readily distinguished from SAA+ participants based on clinical characteristics. However, SAA- participants have a greater degree of subcortical brain atrophy, and approximately one out of seven SAA- participants received a change in diagnosis. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid42094133,

title = {Olfactory decline in aging: longitudinal trajectories and associations with cognitive decline and postmortem neuropathology},

author = {Cecilia Tremblay and Parichita Choudhury and Erika Driver-Dunckley and Zaki Alasmar and Geidy E Serrano and Holly A Shill and Shyamal Mehta and Andrew Ho and Seyed-Mohammad Fereshtehnejad and David Shprecher and Ileana Lorenzini and Christine M Belden and Alireza Atri and Charles H Adler and Thomas G Beach and Mahsa Dadar and Yashar Zeighami},

doi = {10.64898/2026.04.27.26351817},

year  = {2026},

date = {2026-04-01},

journal = {medRxiv},

abstract = {IMPORTANCE: Decline in olfactory function may be used as a predictor of cognitive decline, to enhance early detection models, improve risk stratification, and enable early intervention.nnOBJECTIVE: To assess the longitudinal association between olfactory decline, cognitive decline, and postmortem neuropathology.nnDESIGN SETTING AND PARTICIPANTS: Retrospective longitudinal analysis with clinicopathological correlations of a prospective population-based cohort study using data from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND) and its Brain and Body Donation Program. Participants included cognitively unimpaired individuals without parkinsonism that converted to mild cognitive impairment (MCI) and/or dementia or remained cognitively stable.nnMAIN OUTCOMES AND MEASURES: longitudinal change in olfaction, neuropsychiatric symptoms, motor function and memory, conversion to MCI/dementia, postmortem neuropathology.nnRESULTS: Over a mean follow-up period of 7.7 ± 5.4 years, out of 922 participants who were cognitively unimpaired at the first cognitive conference, 643 remained cognitively unimpaired, 279 converted to MCI, and 82 developed dementia. Of these, 633 individuals had at least 2 olfactory tests.Converters showed reduced olfactory function (t=-12.6, p <0.0001), faster progression in neuropsychiatric symptom burden (t=3.42, p < 0.001), and faster decline in memory (t= -7.33, p <0.0001) prior to conversion while no significant differences were observed in motor scores between converters and non-converters. Using ROC analysis, olfactory decline, increased neuropsychiatric symptom burden, as well as motor and memory decline predicted conversion to MCI with a consistent accuracy of ~ 70% up to 5 years before conversion, while UPSIT alone had an accuracy of ~ 60%. Longitudinal decline in olfaction was associated with a higher burden of a-synuclein (t= -8.21, p <0.0005), tau tangle (t= -2.66, p < 0.01) and amyloid plaque burden (t= -2.85, p < 0.005) and a faster decline over time was associated with a higher burden of tau (t=5.66, p<0.0001).nnCONCLUSIONS AND RELEVANCE: A reduction in olfactory identification ability is observed up to a decade prior to conversion to MCI and is associated with underlying burden of neuropathology markers, underscoring the value of incorporating olfactory testing in cognitively unimpaired individuals to identify those at-risk of future cognitive decline.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41532780,

title = {Gray matter microstructure from in vivo diffusion magnetic resonance imaging reflects post mortem neuropathology severity and clinical progression of Alzheimer's disease},

author = {Zaki Alasmar and Cécilia Tremblay and Roqaie Moqadam and Geidy E Serrano and Thomas G Beach and Alireza Atri and Yi Su and  and Yashar Zeighami and Mahsa Dadar},

doi = {10.1002/alz.71037},

issn = {1552-5279},

year  = {2026},

date = {2026-01-01},

journal = {Alzheimers Dement},

volume = {22},

number = {1},

pages = {e71037},

abstract = {INTRODUCTION: Diffusion-weighted imaging derived mean diffusivity (MD) correlates with Alzheimer's disease (AD) biomarkers, yet its neuropathological correlates remain unclear.nnMETHODS: Diffusion-weighted imaging, post mortem neuropathology, and cognitive performance data were obtained from the National Alzheimer's Coordinating Center (N = 97), Alzheimer's Disease Neuroimaging Initiative (N = 21), and Arizona Study of Aging and Neurodegenerative Disorders (N = 15). We examined MD associations with neuropathology, cognitive decline, and expression profiles of AD-implicated genes.nnRESULTS: Results revealed two latent variables-one linked to amyloid/tau, the other to vascular pathology-explaining between 70% and 16% of MD-pathology covariance, respectively. Higher MD correlated with worse cognitive performance, both cross-sectionally and up to 16 years prior to death. MD was regionally associated with Thal phase, neuritic plaque density, Braak stage (temporal/limbic), and infarcts (thalamus), and reflected gene expression patterns related to AD.nnDISCUSSION: In vivo MD captures distinct AD-related pathologies across brain regions and relates to cognitive trajectories and gene expression.nnHIGHLIGHTS: Diffusion-weighted imaging (DWI) can detect early gray matter microstructural differences in the Alzheimer's disease (AD) continuum. Mean diffusivity (MD) is associated with tau, amyloid and vascular neuropathologies. Thal amyloid phase and Braak tau score correlate with MD in temporal and limbic regions. Multivariate MD scores differentially relate to proteinopathies vs. vascular damage. MD can serve as a non-invasive biomarker to predict post mortem AD neuropathology.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41399254,

title = {Vascular risk factors mediate the relationship between education and white matter hyperintensities},

author = {Shima Raeesi and Yashar Zeighami and Roqaie Moqadam and Cassandra Morrison and Mahsa Dadar},

doi = {10.1002/alz.70972},

issn = {1552-5279},

year  = {2025},

date = {2025-12-01},

journal = {Alzheimers Dement},

volume = {21},

number = {12},

pages = {e70972},

abstract = {INTRODUCTION: Education can protect against cognitive decline and dementia through cognitive reserve and reduced vascular risk. This study examined whether vascular risk mediated the relationship between education and white matter hyperintensity (WMH) burden.nnMETHODS: Data from 1443 older adults from the National Alzheimer's Coordinating Center were analyzed. A composite vascular score was created using diabetes, hypertension, hypercholesterolemia, smoking, alcohol abuse, body mass index, and blood pressure. Linear regressions and mediation analyses examined associations and indirect effects between education, vascular risk, and WMHs, adjusting for age, sex, and diagnosis.nnRESULTS: Higher education was associated with lower vascular risk (p < 0.001) and WMH burden (p = 0.004). Mediation analysis showed an indirect effect of education on WMH via vascular risk (a*b = -0.02, p < 0.001), accounting for 27% of the total effect.nnDISCUSSION: Education influences cerebrovascular health by reducing vascular risk. Addressing vascular health may reduce WMH burden.nnHIGHLIGHTS: Education is associated with lower WMH burden in aging adults. Vascular risk factors mediate the education-WMH relationship. Higher education predicts better vascular profiles and less WMH accumulation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41457063,

title = {The impact of sex on clinical profiles of patients with behavioral variant frontotemporal dementia},

author = {Parsa Afrooz and Marie-Anne St-Georges and Thomas Carrier and Yashar Zeighami and Mahsa Dadar and Maxime Montembeault},

doi = {10.1002/alz.70996},

issn = {1552-5279},

year  = {2025},

date = {2025-12-01},

journal = {Alzheimers Dement},

volume = {21},

number = {12},

pages = {e70996},

abstract = {INTRODUCTION: Sex differences in behavioral variant frontotemporal dementia (bvFTD) remain understudied, especially when controlling for sex differences already existing in the general population.nnMETHODS: Clinical features were analyzed in 620 bvFTD participants. Sex-diagnosis interactions were examined in 1029 bvFTD and 1029 healthy control (HC) participants for neuropsychiatric symptoms, and in 1109 bvFTD and 1109 HC participants for cognitive, behavioral, and language measures.nnRESULTS: Males with bvFTD showed greater-than-expected loss of empathy and nighttime behavioral symptoms relative to females, based on sex-by-diagnosis interactions. They also exhibited higher-than-expected punishment sensitivity. In contrast, females with bvFTD showed greater-than-expected impairments in semantic fluency and picture naming relative to HC females.nnDISCUSSION: Findings reveal that males with bvFTD present with more prominent behavioral disturbances, while females with bvFTD experience greater language impairments. This work is an important step toward integrating social determinants of health, such as sex, into the diagnostic and care paradigms for bvFTD.nnHIGHLIGHTS: Males with behavioral variant frontotemporal dementia (bvFTD) show more empathy loss and nighttime behavioral symptoms Females with bvFTD have greater deficits in naming and semantic fluency Findings support including sex in diagnostic and care models for bvFTD.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41282725,

title = {Quantifying brain atrophy in Frontotemporal Dementia: a head-to-head comparison of neuroimaging techniques},

author = {Amelie Metz and Roqaie Moqadam and Yashar Zeighami and D Louis Collins and Sylvia Villeneuve and Mahsa Dadar},

doi = {10.1101/2025.10.28.25339007},

year  = {2025},

date = {2025-11-01},

journal = {medRxiv},

abstract = {Frontotemporal Dementia (FTD) is a neurodegenerative disorder characterized by extensive atrophy in the frontal and temporal lobes of the brain as well as high cerebrovascular burden. While anatomical Magnetic Resonance Imaging (MRI) is well established for quantifying brain atrophy in FTD, the variability in (pre-)processing methods limits the generalizability and comparability of findings. This study systematically compared the robustness and sensitivity of multiple widely used neuroimaging metrics, namely Deformation-Based Morphometry (DBM), Voxel-Based Morphometry (VBM), Cortical Thickness (CT), and segmentation-based grey matter Volumes, in detecting atrophy across FTD subtypes. We processed 732 T1-weighted MRI scans from 156 participants with FTD and 139 healthy controls from the Frontotemporal Lobar Degeneration Neuroimaging Initiative using our in-house pipeline PELICAN (Dadar et al., 2025) for volumetric measures and FreeSurfer version 7 (Fischl, 2012) for CT and grey matter segmentations. Visual quality control using consistent quality control images at each step of the pipelines revealed significantly higher failure rates for CT (38.52%) and FreeSurfer segmentations (23.63%) relative to PELICAN's volumetric measures (2.04% DBM, 3.05% VBM). Failure rates differed between FTD subtypes and were related to pathological burden. Particularly for FreeSurfer, errors occurred predominantly in regions with high prevalence of atrophy and White Matter Hyperintensities. In PELICAN, the addition of a FTD-specific template as an intermediate step during nonlinear registration decreased the failure rates in this step in the FTD population. We then applied linear regression models to assess each metric's sensitivity in detecting cross-sectional differences between FTD groups controls as well as linear mixed-effects models to determine which method is most sensitive to longitudinal anatomical changes. While CT yielded effect sizes comparable to VBM and DBM when analyzing the same subset of successfully processed scans, VBM and DBM demonstrated enhanced power to detect effects due to lower failure rates and higher participant retention in the full sample. Overall, we demonstrate that image processing methodology and pipeline selection profoundly influences effect sizes and statistical power to detect meaningful between-group differences or longitudinal changes. Volumetric measures (DBM and VBM) yielded sufficiently robust pipeline outcomes to maintain adequate statistical power for capturing atrophy patterns after quality control procedures.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41059583,

title = {Sex differences in vulnerability to tau pathology: Impact on cognitive decline},

author = {Yashar Zeighami and Cecilia Tremblay and Mahsa Dadar},

doi = {10.1002/alz.70634},

issn = {1552-5279},

year  = {2025},

date = {2025-10-01},

journal = {Alzheimers Dement},

volume = {21},

number = {10},

pages = {e70634},

abstract = {INTRODUCTION: Although the link between the presence of amyloid and tau pathologies, neurodegeneration, and cognitive decline in aging individuals is established, it is less clear whether there are sex differences in vulnerability to these pathologies.nnMETHODS: A total of 1464 participants (7168 longitudinal assessments, 4.77 ± 3.78 years of follow-up) were included from the National Alzheimer's Coordinating Center (NACC) database. Longitudinal mixed effects and mediation models examined the sex differences across cognitive decline trajectories of amyloid (A), tau (T), and neurodegeneration (N) groups.nnRESULTS: AT males showed faster cognitive decline compared to AT females (p < 0.005), whereas AT females showed steeper cognitive decline compared to AT males (p < 0.0001). In addition, sex marginally moderated the mediating effect of tau on the relationship between amyloid and cognitive decline (p = 0.046).nnDISCUSSION: Sex differences in vulnerability to tau pathology in the presence of amyloid can shape cognitive decline trajectories.nnHIGHLIGHTS: AT males showed faster cognitive decline compared to AT females. AT females showed faster cognitive decline compared to AT males. Tau status significantly mediated the relationship between amyloid status and cognitive decline. Sex marginally moderated the mediating relationship between amyloid, tau, and cognitive decline. The findings point to sex differences in the impact of tau pathology on cognition.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41040262,

title = {RAVEN: Robust, generalizable, multi-resolution structural MRI upsampling using Autoencoders},

author = {Walter Adame Gonzalez and Roqaie Moqadam and Yashar Zeighami and Mahsa Dadar},

doi = {10.1101/2025.09.22.677945},

issn = {2692-8205},

year  = {2025},

date = {2025-09-01},

journal = {bioRxiv},

abstract = {Due to their high inter-tissue contrast, Magnetic resonance images (MRIs) can reflect neuroanatomical changes related to healthy aging and pathological processes. However, standard brain MRI acquisition resolutions hinder the ability to measure the more subtle changes that occur in early disease stages. Increasing the resolution during acquisition poses multiple challenges, including increased noise, higher acquisition times and cost, and discomfort of the scanned individual. In this work, we propose a robust, generalizable single-image super-resolution network for brain MRIs named Resolution Augmentation with Variational auto-Encoder Networks (RAVEN) with generative adversarial networks (GANs). We show RAVEN is capable of upsampling in-vivo and ex-vivo MRIs of diverse modalities (e.g. T1-weighted, T2-weighted, and T2*) and varying field strengths (3T to 7T) to target voxel sizes as small as 0.5mm isotropic using arbitrary upsampling factors. RAVEN achieved state-of-the-art performance against deep learning and non-deep learning methods, best preserving true anatomical information. We have also made RAVEN open access, with the source code as well as training and evaluation scripts available and ready to use at: https://github.com/waadgo/raven.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39923816,

title = {Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis},

author = {Samuel R Knight and Leyla Abbasova and Yashar Zeighami and Justine Y Hansen and Daniel Martins and Fernando Zelaya and Ottavia Dipasquale and Thomas Liu and David Shin and Matthijs Bossong and Matilda Azis and Mathilde Antoniades and Oliver D Howes and Ilaria Bonoldi and Alice Egerton and Paul Allen and Owen O'Daly and Philip McGuire and Gemma Modinos},

doi = {10.1016/j.biopsych.2025.01.028},

issn = {1873-2402},

year  = {2025},

date = {2025-07-01},

journal = {Biol Psychiatry},

volume = {98},

number = {2},

pages = {144--155},

abstract = {BACKGROUND: The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear.nnMETHODS: Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers.nnRESULTS: We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D, D, dopamine transporter), acetylcholine (VAChT, M), gamma-aminobutyric acid A (GABA), and glutamate (NMDA) receptors as key predictors for SSD (R = 0.58, false discovery rate [FDR]-corrected p < .05) and CHR-P (R = 0.6, p < .05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis.nnCONCLUSIONS: Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40502602,

title = {Vascular risk factors mediate the relationship between education and white matter hyperintensities},

author = {Shima Raeesi and Yashar Zeighami and Roqaie Moqadam and Cassandra Morrison and Mahsa Dadar},

doi = {10.1101/2025.06.03.25328899},

year  = {2025},

date = {2025-06-01},

journal = {medRxiv},

abstract = {INTRODUCTION: Education can protect against cognitive decline and dementia through cognitive reserve and reduced vascular risk. This study examined whether vascular risk mediates the relationship between education and white matter hyperintensity (WMH) burden.nnMETHODS: Data from 1089 older adults from the National Alzheimer's Coordinating Center were analyzed. A composite vascular score was created using diabetes, hypertension, hypercholesterolemia, smoking, alcohol abuse, body mass index, and blood pressure. Linear regressions and mediation analyses examined associations and indirect effects between education, vascular risk, and WMH, adjusting for age, sex, and diagnosis.nnRESULTS: Higher education was associated with lower vascular risk ( < .001) and WMH burden ( = .01). Mediation analysis showed an indirect effect of education on WMH via vascular risk (a*b = -0.02,  = .004), accounting for 23% of the total effect.nnDISCUSSION: Education influences cerebrovascular health via reducing vascular risk. Addressing vascular health may reduce WMH burden.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40088050,

title = {Brain Imaging Phenotypes Associated with Polygenic Risk for Essential Tremor},

author = {Miranda Medeiros and Alexandre Pastor-Bernier and Houman Azizi and Zoe Schmilovich and Charles-Etienne Castonguay and Peter Savadjiev and Jean-Baptiste Poline and Etienne St-Onge and Fan Zhang and Lauren J O'Donnell and Ofer Pasternak and Yashar Zeighami and Patrick A Dion and Alain Dagher and Guy A Rouleau},

doi = {10.1002/mds.30167},

issn = {1531-8257},

year  = {2025},

date = {2025-06-01},

journal = {Mov Disord},

volume = {40},

number = {6},

pages = {1160--1171},

abstract = {Essential tremor (ET) is a common movement disorder with a strong genetic basis. Magnetic resonance imaging (MRI), particularly diffusion-weighted MRI (dMRI) and T1 MRI, have been used to identify brain abnormalities of ET patients. However, the mechanisms by which genetic risk affects the brain to render individuals vulnerable to ET remain unknown. We aimed to understand how ET manifests by identifying presymptomatic brain vulnerabilities driven by ET genetic risk. We probed the vulnerability of healthy people towards ET by investigating the association of morphometry, and white and grey matter dMRI with ET in polygenic risk scores (PRS) in roughly 30,000 individuals from the UK Biobank (UKB). Our results indicate significant effects of ET-PRS with mean diffusivity, fractional anisotropy, free water, radial diffusivity, and axial diffusivity in white matter tracts implicated in movement control. We found significant associations between ET-PRS and grey matter tissue microstructure, including the red nucleus, caudate, putamen, and motor thalamus. ET-PRS was associated with reduced grey matter volumes in several cortical and subcortical areas including the cerebellum. Identified anomalies included networks connected to surgical sites effective in ET treatment. Finally, in a secondary analysis, low PRS individuals compared with a small number of patients with ET (N = 49) in the UKB revealed many structural differences. Brain structural vulnerabilities in healthy people at risk of developing ET correspond to areas known to be involved in the pathology of ET. High genetic risk of ET seems to disrupt ET brain networks even in the absence of overt symptoms of ET. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40375414,

title = {Unveiling diverse clinical symptom patterns and neural activity profiles in major depressive disorder subtypes},

author = {Xiang Wang and Yingying Su and Qian Liu and Muzi Li and Yashar Zeighami and Jie Fan and G Camelia Adams and Changlian Tan and Xiongzhao Zhu and Xiangfei Meng},

doi = {10.1016/j.ebiom.2025.105756},

issn = {2352-3964},

year  = {2025},

date = {2025-06-01},

journal = {EBioMedicine},

volume = {116},

pages = {105756},

abstract = {BACKGROUND: The heterogeneity of major depressive disorder (MDD) significantly hinders its effective and optimal clinical outcomes. This study aimed to identify MDD subtypes by adopting a data-driven approach and assessing validity based on symptomatology and neuroimaging.nnMETHODS: A total of 259 patients with MDD and 92 healthy controls were enrolled in this cross-sectional study. Latent profile analysis (LPA) was used to identify MDD subtypes based on validated clinical symptoms. To examine whether there were differences between these identified MDD subtypes, network analysis was used to test any differences in symptom patterns between these subtypes. We also compared neural activity between these identified MDD subtypes and tested whether certain neural activities were related to individual subtypes. This MDD subtyping was further tested in an independent dataset that contains 86 patients with MDD.nnFINDINGS: Five MDD subtypes with distinct depressive symptom patterns were identified using the LPA model, with the 5-class model selected as the optimal classification solution based on its superior fit indices (AIC = 6656.296, aBIC = 6681.030, entropy = 0.917, LMR p = 0.3267, BLRT p < 0.001). The identified subtypes include atypical-like depression, two melancholic depression (moderate and severe) subtypes with distinct patterns on feeling anxious, and two anhedonic depression subtypes (moderate and severe) with different manifestations on weight/appetite loss. The reproducibility of the classification was also confirmed. Significant differences in symptom structures between melancholic and two anhedonic subtypes, and between anhedonic and atypical subtypes were observed (all p < 0.05). Furthermore, these identified subtypes had differential neural activities in both regional spontaneous neural activity (pFWE < 0.005) and functional connectivity between different brain regions (pFDR < 0.005), linked to different clinical symptoms (FDR q < 0.05).nnINTERPRETATION: The network analysis and neuroimaging tests support the existence and validity of the identified MDD subtypes, each exhibiting unique clinical manifestations and neural activity patterns. The categorisation of these subtypes sheds light on the heterogeneity of depression and suggest that personalised treatment and management strategies tailored to specific subtypes may enhance intervention strategies in clinical settings.nnFUNDING: National Natural Science Foundation of China (NSFC) and China Scholarship Council (CSC).},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40502575,

title = {Gray matter microstructure from in-vivo diffusion MRI reflects post-mortem neuropathology severity and clinical progression of Alzheimer's disease},

author = {Zaki Alasmar and Cécilia Tremblay and Roqaie Moqadam and Geidy E Serrano and Thomas G Beach and Alizera Atri and Yi Su and Yashar Zeighami and Mahsa Dadar},

doi = {10.1101/2025.05.30.25328630},

year  = {2025},

date = {2025-06-01},

journal = {medRxiv},

abstract = {INTRODUCTION: Diffusion-weighted imaging derived mean diffusivity (MD) correlates with Alzheimer's disease biomarkers, yet its neuropathological correlates remain unclear.nnMETHODS: Diffusion-weighted imaging, postmortem neuropathology, and cognitive performance data were obtained from the National Alzheimer's Coordinating Center (N=97), Alzheimer's Disease Neuroimaging Initiative (N=21), and Arizona Study of Aging and Neurodegenerative Disorders (N=15). We examined MD associations with neuropathology, cognitive decline, and expression profiles of AD-implicated genes.nnRESULTS: Results revealed two latent variables-one linked to amyloid/tau, the other to vascular pathology-explaining 70% and 16% of MD-pathology covariance, respectively. Higher MD correlated with worse cognitive performance, both cross-sectionally and up to 14 years prior to death. MD was regionally associated with Thal phase, neuritic plaque density, Braak stage (temporal/limbic), and infarcts (thalamus), and reflected gene expression patterns related to AD.nnDISCUSSION: In vivo MD captures distinct AD-related pathologies across brain regions and relates to cognitive trajectories and gene expression.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40415649,

title = {Distinct Longitudinal Clinical-Neuroanatomical Trajectories in Parkinson's Disease Clinical Subtypes: Insight toward Precision Medicine},

author = {Seyed-Mohammad Fereshtehnejad and Roqaie Moqadam and Houman Azizi and Ronald B Postuma and Mahsa Dadar and Anthony E Lang and Connie Marras and Yashar Zeighami},

doi = {10.1002/mds.30229},

issn = {1531-8257},

year  = {2025},

date = {2025-05-01},

journal = {Mov Disord},

abstract = {BACKGROUND: Parkinson's disease (PD) varies widely across individuals in clinical manifestations and course of progression. Identification of distinct biological subtypes could explain this heterogeneity, identify its pathophysiology, and predict disease progression.nnOBJECTIVES: Our aim was to compare longitudinal clinical trajectories and brain atrophy patterns between clinical subtypes defined at the baseline de novo PD.nnMETHODS: We analyzed data from 421 PD patients (mean follow-up: 8.2 years) in the Parkinson's Progression Markers Initiative (PPMI). Using multi-domain motor and non-motor criteria, de novo patients were classified into "mild motor-predominant" (n = 223), "intermediate" (n = 146), and "diffuse-malignant" (n = 52) subtypes. Deformation-based morphometry was performed on T1-weighted magnetic resonance imaging (MRIs) from 128 PD patients with at least two MRIs (71 mild motor-predominant, 42 intermediate, and 15 diffuse-malignant) and 60 controls, with an average MRI follow-up duration of 3.4 ± 1.1 in the PD cohort. Mixed-effects models compared clinical progression and longitudinal pattern of regional atrophy across subtypes.nnRESULTS: The diffuse-malignant subtype exhibited faster worsening of motor severity (P = 0.007), cognition (P < 0.0001), and activities of daily living (P < 0.0001) compared to mild motor-predominant subtype over 8 years. These findings remained statistically significant after an age-matched subgroup analysis and adjustment for the levodopa treatment. Accelerated atrophy was observed in the precuneus, temporal and fusiform gyri, cerebellum, and other regions (corrected-P < 0.05).nnCONCLUSIONS: Longitudinal analysis revealed distinct patterns of clinical progression and regional atrophy in PD subtypes, with the diffuse-malignant subtype showing more severe neurodegeneration and clinical deterioration suggesting existence of diverse pathophysiological mechanisms in PD. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39757311,

title = {Disentangling the effect of sex from brain size on brain organization and cognitive functioning},

author = {Aliza Brzezinski-Rittner and Roqaie Moqadam and Yasser Iturria-Medina and M Mallar Chakravarty and Mahsa Dadar and Yashar Zeighami},

doi = {10.1007/s11357-024-01486-5},

issn = {2509-2723},

year  = {2025},

date = {2025-02-01},

journal = {Geroscience},

volume = {47},

number = {1},

pages = {247--262},

abstract = {Neuroanatomical sex differences estimated in neuroimaging studies are confounded by total intracranial volume (TIV) as a major biological factor. Employing a matching approach widely used for causal modeling, we disentangled the effect of TIV from sex to study sex-differentiated brain aging trajectories, their relation to functional networks and cytoarchitectonic classes, brain allometry, and cognition. Using data from the UK Biobank, we created subsamples that removed, maintained, or exaggerated the TIV differences in the original sample. We compared regional and vertex-level sex estimates across subsamples. The overall sex-related differences diminished in head size-matched subsamples, suggesting that most of the observed variability results from TIV differences. Furthermore, bidirectional sex differences in brain neuroanatomy emerged that were previously masked by the effect of TIV. Allometry remained fairly consistent across lifespan and was not sex-differentiated. Finally, the matching process changed the direction of the estimated sex differences in "verbal and numerical reasoning" and "working memory", suggesting that behavioral sex difference investigations can benefit from additional biological analysis to uncover the underlying factors contributing to cognition. Taken together, we provide new evidence disentangling sex differences from TIV as a relevant biological confound.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39952947,

title = {Patient-centered brain transcriptomic and multimodal imaging determinants of clinical progression, physical activity, and treatment needs in Parkinson's disease},

author = {Quadri Adewale and Ahmed Faraz Khan and Sue-Jin Lin and Tobias R Baumeister and Yashar Zeighami and Felix Carbonell and Daniel Ferreira and Yasser Iturria-Medina},

doi = {10.1038/s41531-025-00878-4},

issn = {2373-8057},

year  = {2025},

date = {2025-02-01},

journal = {NPJ Parkinsons Dis},

volume = {11},

number = {1},

pages = {29},

abstract = {We continue to lack a clear understanding on how the biological and clinical complexity of Parkinson's disease emerges from molecular to macroscopic brain interactions. Here, we use personalized multiscale spatiotemporal computational brain models to characterize for the first time the synergistic links between genes, several multimodal neuroimaging-derived biological factors, clinical profiles, and therapeutic needs in PD. We identified genes modulating PD-caused brain reorganization in dopamine transporter level, neuronal activity integrity, microstructure, dendrite density and tissue atrophy. Inter-individual heterogeneity in the identified gene-mediated biological mechanisms was associated with five distinct configurations of PD motor and non-motor symptoms. Notably, the protein-protein interaction networks underlying both brain phenotypic and symptom configurations in PD revealed distinct hub genes including MYC, CCNA2, CCDK1, SRC, STAT3 and PSMD4. We also studied the biological mechanisms associated with physical activities performance, observing that leisure and work activities are strongly related to neurotypical cholesterol homeostasis and inflammatory response processes, respectively. Finally, patient-tailored in silico gene perturbations revealed a set of putative disease-modifying drugs with potential to effectively treat PD across different biological levels, most of which are associated with dopamine reuptake and anti-inflammation. Our study constitutes the first self-contained multiscale spatiotemporal computational approach providing comprehensive insights into the complex multifactorial pathogenesis of PD, unraveling key biological modulators of physical and clinical deterioration, and serving as a blueprint for optimum drug selection at personalized level.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39990273,

title = {Frontotemporal dementia subtyping using machine learning, multivariate statistics and neuroimaging},

author = {Amelie Metz and Yashar Zeighami and Simon Ducharme and Sylvia Villeneuve and Mahsa Dadar},

doi = {10.1093/braincomms/fcaf065},

issn = {2632-1297},

year  = {2025},

date = {2025-01-01},

journal = {Brain Commun},

volume = {7},

number = {1},

pages = {fcaf065},

abstract = {Frontotemporal dementia (FTD) is a prevalent form of early-onset dementia characterized by progressive neurodegeneration and encompasses a group of heterogeneous disorders. Due to overlapping symptoms, diagnosis of FTD and its subtypes still poses a challenge. Magnetic resonance imaging (MRI) is commonly used to support the diagnosis of FTD. Using machine learning and multivariate statistics, we tested whether brain atrophy patterns are associated with severity of cognitive impairment, whether this relationship differs between the phenotypic subtypes and whether we could use these brain patterns to classify patients according to their FTD variant. A total of 136 patients (70 behavioural variant FTD, 36 semantic variant primary progressive aphasia and 30 non-fluent variant primary progressive aphasia) from the frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) database underwent brain MRI and clinical and neuropsychological examination. Deformation-based morphometry, which offers increased sensitivity to subtle local differences in structural image contrasts, was used to estimate regional cortical and subcortical atrophy. Atlas-based associations between atrophy values and performance across different cognitive tests were assessed using partial least squares. We then applied linear regression models to discern the group differences regarding the relationship between atrophy and cognitive decline in the three FTD phenotypes. Lastly, we assessed whether the combination of atrophy and cognition patterns in the latent variables identified in the partial least squares analysis could be used as features in a machine learning model to predict FTD subtypes in patients. Results revealed four significant latent variables that combined accounted for 86% of the shared covariance between cognitive and brain atrophy measures. Partial least squares-based atrophy and cognitive patterns predicted the FTD phenotypes with a cross-validated accuracy of 89.12%, with high specificity (91.46-97.15%) and sensitivity (84.19-93.56%). When using only MRI measures and two behavioural tests in the partial least squares and classification algorithms, ensuring clinical feasibility, our model was equally precise in the same participant sample (87.18%, specificity 76.14-92.00%, sensitivity 86.93-98.26%). Here, including only atrophy or behaviour patterns in the analysis led to prediction accuracies of 69.76% and 76.54%, respectively, highlighting the increased value of combining MRI and clinical measures in subtype classification. We demonstrate that the combination of brain atrophy and clinical characteristics and multivariate statistical methods can serve as a biomarker for disease phenotyping in FTD, whereby the inclusion of deformation-based morphometry measures adds to the classification accuracy in the absence of extensive clinical testing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41063969,

title = {Apathy progression is associated with brain atrophy and white matter damage in Parkinson's disease},

author = {Roqaie Moqadam and Houman Azizi and Aliza Brzezinski-Rittner and Lucas Alexis Ronat and Shima Raeesi and Alexandru Hanganu and Yashar Zeighami and Mahsa Dadar},

doi = {10.1093/braincomms/fcaf355},

issn = {2632-1297},

year  = {2025},

date = {2025-01-01},

journal = {Brain Commun},

volume = {7},

number = {5},

pages = {fcaf355},

abstract = {Apathy is a prevalent non-motor symptom that significantly impacts the quality of life in Parkinson's disease (PD) patients. Although previous studies have investigated the neural correlates of apathy in PD, the longitudinal relationships between regional brain atrophy, white matter hyperintensities (WMHs), and apathy progression remain underexplored. Using longitudinal, multisite data of  PD patients from the Parkinson's Progression Markers Initiative (PPMI), the present study aims to investigate these relationships. We used T1-weighted magnetic resonance imaging (MRI) and clinical data from 445 participants. Apathy was assessed as part of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part I. We applied deformation-based morphometry (DBM) to quantify grey matter atrophy and used the Brain tISsue segmentatiON (BISON) algorithm to segment WMHs from T1-weighted images. Using linear regression models, we performed cross-sectional analyses to identify the associations between baseline brain measurements (DBM and WMH) and apathy severity. Longitudinal analyses utilized linear mixed-effects models to investigate whether baseline brain measurements were associated with future apathy progression over time, accounting for covariates such as age, sex, motion artefacts, Hoehn and Yahr stage, levodopa-equivalent daily dose (LEDD), Total Intracranial Volume (TIV) and baseline apathy. Hypothesis-based and exploratory analyses were conducted to confirm the results previously reported in the literature and explore potential new associations. No cross-sectional regional associations survived multiple comparison corrections. Longitudinal hypothesis-based models confirmed that baseline atrophy in regions such as the bilateral nucleus accumbens area, superior parietal, putamen, insula, left precuneus, right precentral and cerebellum grey matter was significantly associated with future apathy progression. Exploratory longitudinal analyses identified additional regions, including the bilateral lingual, parahippocampal, basal forebrain, ventral diencephalon, isthmus cingulate, thalamus, hippocampus, left middle temporal, right inferior temporal, pericalcarine, medial orbitofrontal, cuneus, where baseline atrophy was correlated with progression of apathy severity. Moreover, greater WMH burden, particularly in the frontal lobe, was associated with worsening apathy. These results highlight the influence of both grey matter atrophy and WMHs on apathy progression in PD.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38512130,

title = {Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions},

author = {Veronika Pak and Quadri Adewale and Danilo Bzdok and Mahsa Dadar and Yashar Zeighami and Yasser Iturria-Medina},

doi = {10.7554/eLife.89368},

issn = {2050-084X},

year  = {2024},

date = {2024-03-01},

journal = {Elife},

volume = {12},

abstract = {For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid39403074,

title = {Sustained improvements in brain health and metabolic markers 24 months following bariatric surgery},

author = {Marianne Legault and Mélissa Pelletier and Amélie Lachance and Marie-Ève Lachance and Yashar Zeighami and Marie-Frédérique Gauthier and Sylvain Iceta and Laurent Biertho and Stephanie Fulton and Denis Richard and Alain Dagher and André Tchernof and Mahsa Dadar and Andréanne Michaud},

doi = {10.1093/braincomms/fcae336},

issn = {2632-1297},

year  = {2024},

date = {2024-01-01},

journal = {Brain Commun},

volume = {6},

number = {5},

pages = {fcae336},

abstract = {Obesity and its metabolic complications are associated with lower grey matter and white matter densities, whereas weight loss after bariatric surgery leads to an increase in both measures. These increases in grey and white matter density are significantly associated with post-operative weight loss and improvement of the metabolic/inflammatory profiles. While our recent studies demonstrated widespread increases in white matter density 4 and 12 months after bariatric surgery, it is not clear if these changes persist over time. The underlying mechanisms also remain unknown. In this regard, numerous studies demonstrate that the enlargement or hypertrophy of mature adipocytes, particularly in the visceral fat compartment, is an important marker of adipose tissue dysfunction and obesity-related cardiometabolic abnormalities. We aimed (i) to assess whether the increases in grey and white matter densities previously observed at 12 months are maintained 24 months after bariatric surgery; (ii) to examine the association between these structural brain changes and adiposity and metabolic markers 24 months after bariatric surgery; and (iii) to examine the association between abdominal adipocyte diameter at the time of surgery and post-surgery grey and white matter densities changes. Thirty-three participants undergoing bariatric surgery were recruited. Grey and white matter densities were assessed from T1-weighted magnetic resonance imaging scans acquired prior to and 4, 12 and 24 months post-surgery using voxel-based morphometry. Omental and subcutaneous adipose tissue samples were collected during the surgical procedure. Omental and subcutaneous adipocyte diameters were measured by microscopy of fixed adipose tissue samples. Linear mixed-effects models were performed controlling for age, sex, surgery type, initial body mass index, and initial diabetic status. The average weight loss at 24 months was 33.6 ± 7.6%. A widespread increase in white matter density was observed 24 months post-surgery mainly in the cerebellum, brainstem and corpus callosum ( < 0.05, false discovery rate) as well as some regions in grey matter density. Greater omental adipocyte diameter at the time of surgery was associated with greater changes in total white matter density at 24 months ( = 0.008). A positive trend was observed between subcutaneous adipocyte diameter at the time of surgery and changes in total white matter density at 24 months ( = 0.05). Our results show prolonged increases in grey and white matter densities up to 24 months post-bariatric surgery. Greater preoperative omental adipocyte diameter is associated with greater increases in white matter density at 24 months, suggesting that individuals with excess visceral adiposity might benefit the most from surgery.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}