Jaiashre Sridhar

Introduction: Migraine is a prevalent and disabling neurological disorder with subtypes mainly classified and managed based on monthly headache frequency. This study investigates a novel, data-driven clustering approach that uses comprehensive clinical features in addition to headache frequency to further understand migraine subtyping and management. Methods: This cross-sectional observational study included participants with a diagnosis of migraine based on self-reported surveys recruited from the Stanford Headache Clinic and surrounding community between 2012 and 2020. This study represents a secondary analysis of a previously collected cohort, incorporating newly added episodic migraine (EM) data alongside existing clinical and demographic variables. This approach extends prior work to refine data-driven migraine subtyping. Participants completed comprehensive surveys capturing clinical, demographic, and treatment-related information. A data-driven clustering approach was applied using hierarchical agglomerative clustering. To determine the optimal number of clusters, visual inspection of the dendrogram, scree plot, and principal component analysis was used. Descriptive statistics and logistic regression models were used to explore clinical characteristics and medication outcomes for each cluster. Results: 180 participants were included in the clustering analysis. Two distinct clusters were identified and compared. Cluster 1 ( n = 66; chronic migraine (CM) = 62; EM = 4) had a significantly ( p ≤ 0.001) higher migraine frequency, greater migraine-related disability, higher pain catastrophizing, and more comorbidities (including anxiety, depression, sleep disorders, post-traumatic stress disorder, and somatic symptoms). Cluster 2 ( n = 114; CM = 68; EM = 46) had significantly ( p ≤ 0.001) more years with headache and higher levels of pain self-efficacy. Treatment response differed significantly between clusters for acute medications, with Cluster 2 showing more favorable response rates to triptans and non-steroidal anti-inflammatory drugs ( p ≤ 0.001) and a higher proportion of individuals who indicated that acute medication helped to improve their migraine compared to Cluster 1. Conclusion: This study supports the potential use of a comprehensive phenotype-based clustering approach to determine migraine subtypes that correlate with acute treatment outcome.

Background: Menstrual-related migraine (MRM) is a subtype of migraine that significantly impacts women. Despite its high prevalence, MRM remains under-researched compared to other migraine types. This study investigates the phenotypic characteristics and treatment outcomes of MRM compared to non-menstrual migraine (non-MRM). Methods: This cross-sectional study included women aged 18 years or older with a diagnosis of migraine and an established menstruation pattern. Informed consent was properly obtained from all participants from the Stanford Headache Clinic and surrounding community between 2014 and 2024 prior to participation in the study. Data were collected from self-reported surveys, including demographic information, headache characteristics, physical and mental health status, and treatment outcomes from the Stanford Headache Clinic and surrounding community. Participants were then categorized into MRM and non-MRM groups. An exploratory analysis was performed to investigate phenotypic features, treatment outcomes, and brain volume through magnetic resonance imaging (MRI) study for each group. Statistical analyses included descriptive statistics, analysis of covariance (ANCOVA), and logistic regression. Results: A total of 84 subjects were included, with 38 in the MRM group and 46 in the non-MRM group. The mean age of participants was 34 (SD = 9) years. The MRM group (mean age 38 ± 9) showed significantly higher prevalences of recent change in migraine frequency, migraine attacks occurring in the same site, prodromal signs, speech aura, nausea, and vomiting associated with migraine, and a higher prevalence of reported family history of headache as compared with the non-MRM group (mean age 31 ± 10) (p < 0.05). MRM group also showed significantly higher rate of migraine improvement following the use of triptans and the overall acute and preventive medications (p < 0.05). No significant differences were found in brain volumes between the groups (p > 0.05). Conclusion: The MRM group was associated with significantly more bothersome symptoms associated with migraine, higher family history of headache, and more favorable response to overall acute medication especially triptan as compared with the non-MRM group.

Background: Integrating brain structure and function may help characterize neurobiological heterogeneity in migraine alongside symptom presentation. Aim: To apply a multimodal, exploratory, data-driven approach to identify migraine subgroups using structural and functional MRI, and to describe the clinical characteristics of the resulting subgroups. Methods: Resting-state functional connectivity (FC) across cortical and subcortical regions, along with structural measures including cortical thickness, cortical volume, and subcortical volumes, were extracted from 111 individuals with migraine (75 chronic, 36 episodic) classified according to ICHD-3 criteria. After dimensionality reduction using principal component analysis, hierarchical agglomerative clustering was applied to identify multimodal imaging-derived subgroups. For comparison, secondary unimodal clustering models were constructed using functional-only and structural-only feature sets. The optimal number of clusters was determined using silhouette coefficients, and clustering concordance across models was quantified using the Adjusted Rand Index (ARI). Group differences in clinical characteristics, FC, and cortical and subcortical structure were assessed using covariate-adjusted statistical models with false discovery rate (FDR) correction. Results: Multimodal clustering identified two subgroups with distinct clinical and imaging profiles, Migraine Cluster 1 (M1 _{f + s} ) and Migraine Cluster 2 (M2 _{f + s} ). M2 _{f + s} showed older age, longer disease duration, greater migraine disability, widespread increases in cortical-subcortical FC (including Dorsal Attention, Somatomotor, and Visual networks), and reduced cortical volumes across frontal, parietal, temporal, and insular regions compared with M1 _{f + s} . This subgroup also exhibited increased connectivity relative to controls. In contrast, M1 _{f + s} showed preserved cortical structure and stronger Control-network–subcortical connectivity compared to M2 _{f + s} , and no significant functional or structural deviations from controls. Unimodal analyses revealed that Functional-only clustering aligned moderately with the multimodal cluster solution (ARI = 0.427), showing that FC was a primary determinant of the multimodal cluster structure, whereas structural-only clustering showed negligible overlap (ARI = 0.001), reflecting an orthogonal dimension of heterogeneity captured by structural variation. Conclusion: Data-driven multimodal neuroimaging-based clustering in migraine identified two subgroups with distinct clinical and imaging patterns, highlighting heterogeneity and providing a framework for further investigation of imaging-informed characterization.

Background: Agitation is a behavioral syndrome involving increased motor activity, restlessness, aggressiveness and emotional distress. Its has a 30% prevalence across multiple types of dementia and is associated with negative outcomes, including reduced quality of life, caregiver distress, and mortality. Pharmacological treatment risks serious side effects, including mortality. Understanding brain network topology could provide insights into novel treatment methods.Method: Participants comprised 600 subjects from 3 existing databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD) and the Mesulam Center for Cognitive Neurology and Alzheimer’s Disease at Northwestern University.Participants were clinically diagnosed with either behavioral variant frontotemporal dementia (bvFTD), mild cognitive impairment (MCI), dementia of the Alzeheimer type (DAT) or cognitively normal (CN). The Neuropsychiatric Inventory Questionnaire (NPI‐Q) caregiver rating was used to determine whether individuals were agitated or not.Morphometric similarity networks (MSNs) were generated from Freesurfer statistics calculated from T1‐weighted MRIs. 7 surface‐based cortical metrics (e.g. gray matter volume, surface area) were calculated for each of 360 parcels. Pairwise inter‐parcel Pearson correlations of feature vectors were calculated to produce a morphometric similarity matrix for each individual. We calculated sub‐matrices for salience (SN) cognitive control (CCN) and default mode networks (DMN). Brain Connectivity Toolbox calculated transitivity and global efficiency of each network. Metrics were calculated at different thresholds to ensure the results were not threshold‐dependent.We calculated 2 (Agitation present/absent) x 4 (Diagnosis) repeated measures ANCOVAS for transitivity and global efficiency for each network. Covariates were age, sex, race, database, education, ICV, CDR‐SB and days MRI‐NPI‐Q.Result: For the SN, people with Agitation had significantly lower global efficiency than people without Agitation (Figure 1). There were no significant effects of diagnosis or interaction.For the CCN, people with agitation had significantly lower transitivity than people without Agitation (Figure 2). There were no significant effect of diagnosis or interaction.Conclusion: Across different forms of dementia, Agitation is associated with reduced integration (global efficiency) of the salience network, and reduced segregation (transitivity) of the cognitive control network. Interventions that alter these topological network features may be effective in reducing agitation in dementia, regardless of clinical diagnosis.

Background: The amount of white matter hyperintensity (WMH) found on T₂‐weighted imaging has shown a strong relationship with age and is thought to be a marker of ischemia‐associated demyelination or secondary axonal degeneration associated with neurodegenerative disease. Previous healthy older aging studies have shown increased WMH are associated with poorer episodic memory performance and decreased volume of medial temporal lobe regions. Here, we examine if lower WMH are associated with a phenotype of successful cognitive aging, SuperAging. SuperAgers are participants over the age 80 with episodic memory at least as good as that of average middle‐aged adults.Method: We quantified the WMH volume from T₂‐FLAIR imaging with a pre‐trained Bayesian 3D U‐Net convolutional neural network in SuperAgers and cognitively healthy elderly controls (EC) of a similar age and education. All participants underwent the ADNI‐3 FLAIR on the same 3T Prisma MRI. WMH volume was square root transformed to account for the skewed distribution. A 2‐tail independent samples t‐test evaluated the difference in WMH volume between SuperAgers and EC. Additionally, we examined if there were differences within SA and EC groups by sex.Result: 46 SuperAgers (average age 86.3 ± 1.86; average years of education 16.9 ± 1.9) and 25 EC (average age 87.1 ± 5.17; average years of education 17.1 ± 3.1) met criteria and were included in the study. There was no significant difference in the volume of WMH between SuperAgers and ECs (t = 1.01, p = 0.32). Further, there was no significant difference in WMH by sex within the SuperAging group (t = 0.10, p = 0.92) or EC group (t = 0.82, p = 0.42).Conclusion: Resistance to the development of WMH, a cross‐sectional marker of cerebrovascular disease and demyelination, does not appear to be a strong discriminating factor in 80+ year‐olds with superior memory function compared to cognitively average agers. Future analyses will include longitudinal changes in WMH and examining regional WMH. Cognitive aging is associated with several underlying brain‐based etiologies, and SuperAging may be better explained by a more sophisticated multi‐modal approach.

Background: Memory decline in late life is a hallmark of aging, yet SuperAgers are individuals age 80 or older with episodic memory performance at least as good as cognitively average 50‐to‐60‐year‐olds. Neuroimaging analyses have found SuperAgers to have thicker cortices and slower atrophy rates compared to similarly aged controls (Controls). This project extended the anatomical findings by examining the functional integrity of seven intrinsic networks spanning the entire cortex and two memory‐specific systems identified from task‐based and resting state studies.Methods: Within‐network functional connectivity (FC) of seven large‐scale networks and two memory systems were compared between 25 SuperAgers and 16 Controls using resting state functional MRI (rs‐fMRI) and T₁‐weighted structural imaging from a single visit. Classification of SuperAgers/Controls was determined based on measures of episodic memory, executive functioning, verbal fluency, and picture naming (Harrison et al., 2012). Inclusion criteria required stable cognitive status across two visits. In the first analysis, we included all seven networks from a common resting‐state atlas (Schaefer et al., 2018). Within‐network FCs for all networks were defined as the average FCs between all within‐network regions (Figure 1A). In the second analysis, we investigated two memory‐specific networks: the anterior temporal (AT) and posterior medial (PM) systems. AT and PM cortical maps (Figure 1B) were identified from prior research (de Flores et al., 2022). Within‐network FC of the AT and PM systems were defined as the FC between AT‐to‐anterior‐hippocampus and PM‐to‐posterior‐hippocampus, respectively. For both analyses, FCs were compared across groups using two‐sample independent t‐tests and corrected for multiple comparisons (Benjamini‐Hochberg false discovery rate).Results: We found no significant between‐group differences in demographic characteristics including age, sex, and education. Performance on neuropsychological measures was significantly different for episodic memory, verbal fluency, and picture naming. Group differences in within‐network FCs for large‐scale networks and memory systems were nonsignificant.Conclusion: At the group‐level, within‐network FC of large‐scale networks and memory‐specific systems were not a primary differentiator between cognitively average aging and SuperAging phenotypes. Person‐specific functional connectome approaches may be important for understanding network organizational features of SuperAgers.

Background: Agitation is a behavioral syndrome involving increased motor activity, restlessness, aggressiveness and emotional distress. Its has a 30% prevalence across multiple types of dementia and is associated with negative outcomes, including reduced quality of life, caregiver distress, and mortality. Pharmacological treatment risks serious side effects, including mortality. Understanding brain network topology could provide insights into novel treatment methods.Method: Participants comprised 600 subjects from 3 existing databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD) and the Mesulam Center for Cognitive Neurology and Alzheimer’s Disease at Northwestern University. Participants were clinically diagnosed with either behavioral variant frontotemporal dementia (bvFTD), mild cognitive impairment (MCI), dementia of the Alzeheimer type (DAT) or cognitively normal (CN). The Neuropsychiatric Inventory Questionnaire (NPI‐Q) caregiver rating was used to determine whether individuals were agitated or not. Morphometric similarity networks (MSNs) were generated from Freesurfer statistics calculated from T1‐weighted MRIs. 7 surface‐based cortical metrics (e.g. gray matter volume, surface area) were calculated for each of 360 parcels. Pairwise inter‐parcel Pearson correlations of feature vectors were calculated to produce a morphometric similarity matrix for each individual. We calculated sub‐matrices for salience (SN) cognitive control (CCN) and default mode networks (DMN). Brain Connectivity Toolbox calculated transitivity and global efficiency of each network. Metrics were calculated at different thresholds to ensure the results were not threshold‐dependent. We calculated 2 (Agitation present/absent) x 4 (Diagnosis) repeated measures ANCOVAS for transitivity and global efficiency for each network. Covariates were age, sex, race, database, education, ICV, CDR‐SB and days MRI‐NPI‐Q.Result: For the SN, people with Agitation had significantly lower global efficiency than people without Agitation (Figure 1). There were no significant effects of diagnosis or interaction. For the CCN, people with agitation had significantly lower transitivity than people without Agitation (Figure 2). There were no significant effect of diagnosis or interaction.Conclusion: Across different forms of dementia, Agitation is associated with reduced integration (global efficiency) of the salience network, and reduced segregation (transitivity) of the cognitive control network. Interventions that alter these topological network features may be effective in reducing agitation in dementia, regardless of clinical diagnosis.

Background: The amount of white matter hyperintensity (WMH) found on T2‐weighted imaging has shown a strong relationship with age and is thought to be a marker of ischemia‐associated demyelination or secondary axonal degeneration associated with neurodegenerative disease. Previous healthy older aging studies have shown increased WMH are associated with poorer episodic memory performance and decreased volume of medial temporal lobe regions. Here, we examine if lower WMH are associated with a phenotype of successful cognitive aging, SuperAging. SuperAgers are participants over the age 80 with episodic memory at least as good as that of average middle‐aged adults.Method: We quantified the WMH volume from T2‐FLAIR imaging with a pre‐trained Bayesian 3D U‐Net convolutional neural network in SuperAgers and cognitively healthy elderly controls (EC) of a similar age and education. All participants underwent the ADNI‐3 FLAIR on the same 3T Prisma MRI. WMH volume was square root transformed to account for the skewed distribution. A 2‐tail independent samples t‐test evaluated the difference in WMH volume between SuperAgers and EC. Additionally, we examined if there were differences within SA and EC groups by sex.Result: 46 SuperAgers (average age 86.3 ± 1.86; average years of education 16.9 ± 1.9) and 25 EC (average age 87.1 ± 5.17; average years of education 17.1 ± 3.1) met criteria and were included in the study. There was no significant difference in the volume of WMH between SuperAgers and ECs (t = 1.01, p = 0.32). Further, there was no significant difference in WMH by sex within the SuperAging group (t = 0.10, p = 0.92) or EC group (t = 0.82, p = 0.42).Conclusion: Resistance to the development of WMH, a cross‐sectional marker of cerebrovascular disease and demyelination, does not appear to be a strong discriminating factor in 80+ year‐olds with superior memory function compared to cognitively average agers. Future analyses will include longitudinal changes in WMH and examining regional WMH. Cognitive aging is associated with several underlying brain‐based etiologies, and SuperAging may be better explained by a more sophisticated multi‐modal approach.

Background: Memory complaints are widespread among the elderly and aging is a major risk factor for Alzheimer’s disease (AD), leading to the impression that gradual loss of memory ability is a nearly universal consequence of getting old. Our longitudinal studies of SuperAgers, 80+ year‐olds with episodic memory performance that remains in the range that is at least normal for 50‐60 year‐olds suggests an alternative aging trajectory is possible. This session will highlight some of the emerging biologic features of the SuperAgers.Method: Participants include SuperAgers and cognitively average 80+ year‐old cognitively average normal controls. Data from detailed neuropsychological assessments, quantitative neuroimaging measurements (MR and amyloid PET), genetic features and neuropathologic findings will be reported.Result: Initial evidence suggest SuperAgers tend to show mismatch between chronologic and biologic age, including maintenance of cortical but not necessarily hippocampal volume, a tendency to resist significant amyloid PET retention, an abundance of anterior cingulate Von Economo neurons, and some with resistance to cortical Alzheimer’s pathology.Conclusion: These studies contribute to our understanding mechanisms of resilience and resistance in cognitive aging and may help isolate factors that are potentially important for promoting successful cognitive aging and avoiding age‐related brain diseases such as AD.