Browsing by Author "Katsumi, Yuta"

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    Dissociable spatial topography of cortical atrophy in early‐onset and late‐onset Alzheimer's disease: A head‐to‐head comparison of the LEADS and ADNI cohorts
    (Wiley, 2025) Katsumi, Yuta; Touroutoglou, Alexandra; Brickhouse, Michael; Eloyan, Ani; Eckbo, Ryan; Zaitsev, Alexander; La Joie, Renaud; Lagarde, Julien; Schonhaut, Daniel; Thangarajah, Maryanne; Taurone, Alexander; Vemuri, Prashanthi; Jack, Clifford R., Jr.; Dage, Jeffrey L.; Nudelman, Kelly N. H.; Foroud, Tatiana; Hammers, Dustin B.; Ghetti, Bernardino; Murray, Melissa E.; Newell, Kathy L.; Polsinelli, Angelina J.; Aisen, Paul; Reman, Rema; Beckett, Laurel; Kramer, Joel H.; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian M.; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium for the Alzheimer's Disease Neuroimaging Initiative; Neurology, School of Medicine
    Introduction: Early-onset and late-onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head-to-head comparison of cortical atrophy in EOAD and LOAD, using two large and well-characterized cohorts (LEADS and ADNI). Methods: We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.e., early-stage AD), along with cognitively unimpaired participants. Results: After controlling for the level of cognitive impairment, we found a double dissociation between AD clinical phenotype and localization/magnitude of atrophy, characterized by predominant neocortical involvement in EOAD and more focal anterior medial temporal involvement in LOAD. Discussion: Our findings point to the clinical utility of MRI-based biomarkers of atrophy in differentiating between EOAD and LOAD, which may be useful for diagnosis, prognostication, and treatment. Highlights: Early-onset Alzheimer's disease (EOAD) and late-onset AD (LOAD) patients showed distinct and overlapping cortical atrophy patterns. EOAD patients showed prominent atrophy in widespread neocortical regions. LOAD patients showed prominent atrophy in the anterior medial temporal lobe. Regional atrophy was correlated with the severity of global cognitive impairment. Results were comparable when the sample was stratified for mild cognitive impairment (MCI) and dementia.
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    Dissociable spatial topography of neurodegeneration in Early‐onset and Late‐onset Alzheimer’s Disease: A head‐to‐head comparison of MRI‐derived atrophy measures between the LEADS and ADNI cohorts
    (Wiley, 2025-01-09) Katsumi, Yuta; Touroutoglou, Alexandra; Brickhouse, Michael; Eckbo, Ryan; La Joie, Renaud; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Dage, Jeffrey L.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium; Neurology, School of Medicine
    Background: Understanding how early‐onset Alzheimer’s disease (EOAD) differs from typical late‐onset AD (LOAD) is an important goal of AD research that may help increase the sensitivity of unique biomarkers for each phenotype. Building upon prior work based on small samples, here we leveraged two large, well‐characterized natural history study cohorts of AD patients (LEADS and ADNI3) to test the hypothesis that EOAD patients would show more prominent lateral and medial parietal and lateral temporal cortical atrophy sparing the medial temporal lobe (MTL), whereas LOAD patients would show prominent MTL atrophy. Method: We investigated differences in the spatial topography of cortical atrophy between EOAD and LOAD patients by analyzing structural MRI data collected from 211 patients with sporadic EOAD and 88 cognitively unimpaired (CU) participants from the LEADS cohort as well as 144 patients with LOAD and 365 CU participants from the ADNI3 cohort. MRI data were processed via FreeSurfer v6.0 to estimate cortical thickness for each participant. A direct comparison of cortical thickness was performed between EOAD and LOAD patients based on W‐scores (i.e., Z‐scores adjusted for age and sex relative to CU participants within each cohort) while controlling for MMSE total scores. All patients underwent amyloid PET with 18F‐Florbetaben or 18F‐Florbetapir and amyloid positivity was centrally determined by quantification‐supported visual read. Result: As expected, a direct comparison of cortical thickness between patients with EOAD and LOAD revealed a double dissociation between AD clinical phenotype and localization of cortical atrophy: EOAD patients showed greater atrophy in widespread cortical areas including the inferior parietal lobule (EOAD marginal mean W‐score ± SEM = ‐1.33±0.08 vs. LOAD = ‐0.52±0.09, p<.001, η2=.097), precuneus (‐1.66±0.09 vs. ‐0.59±0.10, p<.001, η2=.13), and caudal middle frontal gyrus (‐1.65±0.08 vs. ‐0.90±0.10, p<.001, η2=.074), whereas LOAD patients showed greater atrophy in the entorhinal/perirhinal cortex and temporal pole (‐1.00±0.09 vs. ‐1.41±0.11, p<.008, η2=.019). Conclusion: These findings demonstrate a clearly dissociable spatial pattern of neurodegeneration between EOAD and LOAD, supporting our previously developed LOAD and EOAD signatures of cortical atrophy, which underlies the distinct episodic memory and other cognitive characteristics of these AD clinical phenotypes.
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    Heterogeneous clinical phenotypes of sporadic early-onset Alzheimer's disease: a neuropsychological data-driven approach
    (Springer Nature, 2025-02-06) Putcha, Deepti; Katsumi, Yuta; Touroutoglou, Alexandra; Eloyan, Ani; Taurone, Alexander; Thangarajah, Maryanne; Aisen, Paul; Dage, Jeffrey L.; Foroud, Tatiana; Jack, Clifford R., Jr.; Kramer, Joel H.; Nudelman, Kelly N. H.; Raman, Rema; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Grant, Ian M.; Honig, Lawrence S.; Johnson, Erik C. B.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Dickerson, Bradford C.; Apostolova, Liana G.; Hammers, Dustin B.; LEADS Consortium; Neurology, School of Medicine
    Background: The clinical presentations of early-onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease are distinct, with EOAD having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) described EOAD as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the phenotypic range of EOAD presentation, we applied a neuropsychological data-driven method to subtype the LEADS cohort. Methods: Neuropsychological test performance from 169 amyloid-positive EOAD participants were analyzed. Education-corrected normative comparisons were made using a sample of 98 cognitively normal participants. Comparing the relative levels of impairment between each cognitive domain, we applied a cut-off of 1 SD below all other domain scores to indicate a phenotype of "predominant" impairment in a given cognitive domain. Individuals were otherwise considered to have multidomain impairment. Whole-cortex general linear modeling of cortical atrophy was applied as an MRI-based validation of these distinct clinical phenotypes. Results: We identified 6 phenotypic subtypes of EOAD: Dysexecutive Predominant (22% of sample), Amnestic Predominant (11%), Language Predominant (11%), Visuospatial Predominant (15%), Mixed Amnestic/Dysexecutive Predominant (11%), and Multidomain (30%). These phenotypes did not differ by age, sex, or years of education. The APOE ɛ4 genotype was enriched in the Amnestic Predominant group, who were also rated as least impaired. Cortical thickness analysis validated these clinical phenotypes with dissociations in atrophy patterns observed between the Dysexecutive and Amnestic Predominant groups. In contrast to the heterogeneity observed from our neuropsychological data-driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic-predominant, 9% were non-amnestic, 4% met criteria for Posterior Cortical Atrophy, and 5% met criteria for Primary Progressive Aphasia. Conclusion: A neuropsychological data-driven method to phenotype EOAD individuals uncovered a more detailed understanding of the presenting heterogeneity in this atypical AD sample compared to clinical judgment alone. Clinicians and patients may over-report memory dysfunction at the expense of non-memory symptoms. These findings have important implications for diagnostic accuracy and treatment considerations.
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    Heterogeneous clinical phenotypes of sporadic Early‐onset Alzheimer’s disease: A data‐driven approach
    (Wiley, 2025-01-03) Putcha, Deepti; Katsumi, Yuta; Touroutoglou, Alexandra; Dage, Jeffrey L.; Eloyan, Ani; Nudelman, Kelly N.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; Hammers, Dustin B.; LEADS Consortium; Neurology, School of Medicine
    Background: Early‐onset Alzheimer’s disease (EOAD) manifests prior to the age of 65. Clinical presentation of EOAD is distinct from that of late‐onset Alzheimer’s disease, and is characterized as having a more aggressive disease course with greater heterogeneity. Recent publications from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS) described their sample as predominantly amnestic, though this phenotypic description was based solely on clinical judgment. To better understand the range of EOAD presentation, we applied a neuropsychological data‐driven method to phenotypic subtyping within the LEADS cohort. Method: Data from 169 amyloid‐positive EOAD participants with composite data in all cognitive domains (Episodic Memory, Executive Functioning, Speed/Attention, Language, and Visuospatial) were analyzed. Our approach consisted of comparing the relative levels of baseline impairment in each cognitive domain. Education‐corrected normative comparisons were made using a sample of 98 aged‐matched cognitively normal participants. A cut‐off of 1 SD below all other composite domain scores was applied to indicate a phenotype of “predominant” impairment in a given cognitive domain. Individuals were otherwise considered to have a phenotype best characterized by multidomain impairment. Result: We identified 6 phenotypic subtypes of EOAD (Table 1): Dysexecutive‐predominant (22% of sample), Amnestic‐predominant (11%), Language‐predominant (11%), Visuospatial‐predominant (15%), Mixed Amnestic/Dysexecutive‐predominant (11%), and Multidomain (30%). These subtypes did not differ on age, age‐at‐symptom‐onset, sex, or overall clinical severity (p>0.05). Groups differed on global cognitive functioning (MMSE) such that the Amnestic‐predominant group performed better than other domain‐predominant subtypes of EOAD (p>0.05). In contrast to the heterogeneity observed from our data‐driven approach, diagnostic classifications for this same sample based solely on clinical judgment indicated that 82% of individuals were amnestic‐predominant, 9% were non‐amnestic, 4% were visuospatial‐predominant, and 5% were language‐predominant. Conclusion: Applying a neuropsychological data‐driven method of phenotyping EOAD individuals uncovered a more detailed understanding of the diversity of presenting heterogeneity in this atypical AD group compared to clinical judgment alone. These results suggest that clinicians and patients may over‐prioritize memory dysfunction during subjective reporting at the expense of non‐memory symptoms, which has important implications for diagnostic accuracy and treatment considerations. We plan to investigate the patterns of cortical atrophy and network dysfunction subserving this heterogeneity.
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    Identifying anatomical subtypes of sporadic EOAD in LEADS via unsupervised clustering of MRI‐based regional atrophy patterns
    (Wiley, 2025-01-09) McGinnis, Scott M.; Katsumi, Yuta; Eckbo, Ryan; Brickhouse, Michael; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Dage, Jeffrey L.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Touroutoglou, Alexandra; Dickerson, Bradford C.; Neurology, School of Medicine
    Background: Neurodegeneration in sporadic early‐onset Alzheimer disease (EOAD) is topographically heterogeneous, as suggested by variability in syndromic presentation. We performed an unsupervised clustering analysis of structural MRI data to identify anatomical subtypes of EOAD. We hypothesized that distinct clusters will be present but will: (1) share areas of overlap focused around posterior regions of our newly developed EOAD signature of cortical atrophy (Touroutoglou et al., 2023), including the posterior default mode (DMN) and frontoparietal control networks (FPN) of the cerebral cortex; and (2) show non‐overlapping topography inclusive of nodes of other networks including dorsal attention (DAN) and visual association (VIS) networks. Methods: We analyzed structural MRI data from 183 individuals with EOAD and 88 cognitively unimpaired (CU) participants from the Longitudinal Early‐Onset Alzheimer's Disease Study (LEADS). MRI data were processed using FreeSurfer v6.0 to estimate vertex‐wise cortical thickness, which was converted to W‐scores (i.e., Z‐scores relative to CU participants adjusted for age and sex). We then performed an agglomerative hierarchical clustering analysis on a between‐patients similarity matrix computed from rank‐ordered whole‐cortex W‐scores. Results: Analysis yielded 2 major clusters, with subordinate clustering failing to delineate additional unique topographies. One cluster (n=54) exhibited prominent atrophy in the anterior DMN (medial prefrontal cortex, anterior lateral temporal cortex) and rostral FPN (rostral middle and superior frontal gyri). The other cluster (n=129) showed prominent atrophy in the DAN (superior parietal lobule, caudal superior frontal gyrus, posterior temporal cortex) and VIS (posterior inferior temporal/occipital cortex, posterior parietal cortex). Both clusters showed atrophy in the posterior DMN (posterior cingulate cortex, precuneus, posterior inferior parietal lobule, mid lateral temporal cortex) and the FPN (middle and superior frontal gyri, anterior inferior parietal lobule, mid inferior temporal cortex). The clusters did not differ with respect to age, sex, education, APOE status, or clinical measures of disease severity. Conclusions: Our sample of sporadic EOAD patients comprised 2 principal anatomical subtypes, commonly overlapping with the posterior DMN and FPN that constitute the EOAD signature, one subtype uniquely overlapped with the anterior DMN/rostral FPN and the other with the DAN/VIS network. Anatomical differences between the subtypes likely correspond to aspects of phenotypic heterogeneity.
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    Longitudinal neurodegeneration in Early‐Onset Alzheimer’s Disease: A summary of MRI‐derived atrophy in LEADS
    (Wiley, 2025-01-09) Touroutoglou, Alexandra; Katsumi, Yuta; Eckbo, Ryan; Brickhouse, Michael; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Dage, Jeffrey L.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium; Neurology, School of Medicine
    Background: Prior work has advanced our understanding of cortical atrophy in early‐onset Alzheimer’s disease (EOAD), but longitudinal data are sparse. Current longitudinal MRI studies point to progressive atrophy in cerebral cortex exhibiting a posterior‐to‐anterior gradient, but these studies include small samples with mostly amnestic EOAD. Here, we analyzed a large sample of sporadic EOAD patients from the Longitudinal Early‐Onset Alzheimer's Disease Study (LEADS) to test the central hypothesis that areas in our recently described EOAD signature (Touroutoglou et al., 2023) affected at baseline in the posterior lateral temporal cortex, inferior parietal lobule, and PCC/precuneus will continue to degenerate and additional longitudinal atrophy will be found in the medial temporal lobe and frontal regions as cognitive decline progresses over time in multiple domains. Method: We investigated longitudinal changes in cortical thickness by analyzing structural MRI data collected from 367 patients with EOAD and 99 cognitively unimpaired (CN) older adults, totaling 839 MRI scans across the cohorts with up to 4 years of follow‐up. MRI data were longitudinally processed in FreeSurfer 6.0. Linear mixed effects models were constructed to estimate the rate of cortical atrophy with random intercepts and slopes for individual participants while controlling for baseline age and sex. Result: EOAD patients exhibited cortical atrophy at a faster rate than controls in widespread areas of the cerebral cortex. As expected, the regions exhibiting accelerated longitudinal atrophy included not only the EOAD signature regions as a whole (EOAD: ‐0.052±0.002 mm/year vs. CN: 0.0001±0.002 mm/year; Dslopes = ‐0.052, p<.001), but also those that were minimally atrophied at baseline, such as superior frontal gyrus (EOAD: ‐0.052+/‐0.004 vs. CN: ‐0.001+/‐0.004, Dslopes = ‐ 0.051, p<.001) and medial temporal lobe (EOAD: ‐0.083±0.005 mm/year vs. CN: 0.001±0.006 mm/year; Dslopes = ‐0.082, p<.001). We observed no difference in the rate of atrophy in the calcarine fissure (a control region not expected to change; Dslopes = ‐0.002, p£.69). Conclusion: Our findings show that neurodegeneration in EOAD accelerates over time in the EOAD signature regions and spreads to additional areas within large‐scale brain networks (consistent with those observed in late‐onset AD) contributing to the worsening of symptoms over time.
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    The Sporadic Early-onset Alzheimer's Disease Signature Of Atrophy: Preliminary Findings From The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) Cohort
    (Wiley, 2023) Touroutoglou, Alexandra; Katsumi, Yuta; Brickhouse, Michael; Zaitsev, Alexander; Eckbo, Ryan; Aisen, Paul; Beckett, Laurel; Dage, Jeffrey L.; Eloyan, Ani; Foroud, Tatiana; Ghetti, Bernardino; Griffin, Percy; Hammers, Dustin; Jack, Clifford R., Jr.; Kramer, Joel H.; Iaccarino, Leonardo; La Joie, Renaud; Mundada, Nidhi S.; Koeppe, Robert; Kukull, Walter A.; Murray, Melissa E.; Nudelman, Kelly; Polsinelli, Angelina J.; Rumbaugh, Malia; Soleimani-Meigooni, David N.; Toga, Arthur; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph C.; Mendez, Mario F.; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Stephen; Sha, Sharon; Turner, R. Scott; Wingo, Thomas S.; Wolk, David A.; Womack, Kyle; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Dickerson, Bradford C.; LEADS Consortium; Neurology, School of Medicine
    Introduction: Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early-onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. Methods: We analyzed MRI scans to define the sporadic EOAD-signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early-onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. Results: The EOAD-signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD-signature atrophy was associated with the severity of cognitive impairment. Discussion: The EOAD-signature atrophy is a reliable and clinically valid biomarker of AD-related neurodegeneration that could be used in clinical trials for EOAD. Highlights: We developed an early-onset Alzheimer's disease (EOAD)-signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD-signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.
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    Utility of CSF biomarkers in assessing neurodegeneration in Early‐Onset Alzheimer’s disease
    (Wiley, 2025-01-09) Eldaief, Mark C.; Touroutoglou, Alexandra; Brickhouse, Michael; Katsumi, Yuta; Eloyan, Ani; Nudelman, Kelly N.; Foroud, Tatiana M.; Carrillo, Maria C.; Rabinovici, Gil D.; Apostolova, Liana G.; Schindler, Suzanne E.; Herries, Elizabeth M.; Henson, Rachel L.; Dage, Jeffrey L.; Dickerson, Bradford C.; Medical and Molecular Genetics, School of Medicine
    Background: There is a significant need for biomarkers of neurodegenerative burden in Early‐onset Alzheimer’s disease (EOAD). Evidence suggests that levels of specific CSF biomarkers (e.g., Neurofilament light (NfL), Synaptosomal‐Associated Protein (SNAP‐25), neurogranin, Visinin‐like protein 1 (VILIP‐1), Aß 42/40, phospho‐tau and total tau) index the extent of neurodegeneration in dementing illnesses. However, it remains unclear whether these biomarkers correlate to cortical atrophy patterns in EOAD. Based on prior work demonstrating correlations between NfL, SNAP‐25, neurogranin and Aß 42/40 CSF levels and cognitive impairment in EOAD (Dage et al. 2023), we hypothesized that these biomarkers (and not VILIP‐1, phospho‐tau or total tau) would variably predict cortical atrophy within our recently described EOAD signature (Touroutoglou et al. 2023). Method: We recruited 92 EOAD patients. In each patient, atrophy within the EOAD cortical signature were calculated as W‐scores (i.e., Z‐scores adjusted for age and sex relative to a sample of healthy controls). We first ran a simple regression analysis of each of the 7 CSF biomarkers and W‐scores in the EOAD signature across EOAD patients. We then entered the biomarkers with significant correlations to the EOAD signature into a stepwise regression analysis with backward elimination to ascertain the most parsimonious model predicting atrophy in the EOAD signature. As a control region not expected to be related to these biomarkers, we assessed correlations to calcarine fissure cortical atrophy. Result: As predicted, we observed a significant correlation between CSF levels of NfL, SNAP‐25, neurogranin and Aß 42/40 and atrophy within the EOAD signature. After entering these four biomarkers into the stepwise regression analysis, the most parsimonious model identified complementary contributions of NfL, SNAP‐25, and Aß 42/40, in predicting atrophy in the EOAD signature. There were no correlations between any biomarker and calcarine atrophy. Conclusion: Selected CSF biomarkers in EOAD patients predict the degree of atrophy within the EOAD cortical signature. Ongoing work includes correlating these biomarkers with topographical atrophy patterns on the whole‐brain voxel‐wise level. Our results suggest that certain CSF biomarkers could assess neurodegenerative burden within EOAD individuals. This would provide valuable information regarding disease progression for clinical care and clinical trials involving disease‐modifying therapies.