bluuebunny/crossref_metadata_2025 huggingface dataset:
CU PET Aβ and Tau Positive Individuals Show Impairment in MoCA Delayed Recall
Authors: Felix Cynthia , Ferreira Pamela C.L. , Rodrigues Matheus Scarpatto , Snitz Beth E. , Abbas Sarah , Oliveira Markley , Bauer‐Negrini Guilherme , Povala Guilherme , Lemaire Peter , Kollasserry Felix Joy , Medeiros Marina Scop , Soares Carolina , Rebok George , Aguzzoli Cristiano Schaffer , Bellaver Bruna , Lussier Firoza Z , Amaral Livia , Pascual Belen , Gordon Brian A. , Lowe Val J. , Oh Hwamee , soleimani‐meigooni David N. , Tudorascu Dana , Jagust William J. , Klunk William E , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick A.
Published: January 2025

Background

Identification of cognitively unimpaired (CU) individuals who may progress to mild cognitive impairment (MCI), is a pressing issue in the Alzheimer’s disease (AD) field, since therapeutic interventions may be more effective in the absence of cognitive impairment and neurodegeneration. CU individuals positive for amyloid and tau PET are very likely in the AD pathway. In out‐patient cognitive screening, we use rapid and simple tests such as The Montreal Cognitive Assessment (MoCA) ‐ a composite of executive, visuospatial, naming, attention, language, abstraction, delayed recall, and orientation performances. We hypothesize that sub‐categorizing the CU group based on the presence of amyloid (A) and tau (T) pathologies, as measured by PET, can reveal subtle cognitive deficits, even in a clinical test with low sensitivity such as MoCA.

Method

We included 88 CU [defined as CDR global score of 0] older adults with available head‐to‐head MK6240 tau‐PET and Flortaucipir tracers from the ongoing HEAD multi‐site observational study. They also underwent Amyloid PET imaging using PiB or NAV4694. MoCA testing was done around the imaging time. To increase similarity with clinical practice, Amyloid and Tau PET positivity was defined using visual reads. Based on tracer uptake status, we categorized the individuals into A‐T‐, A‐T+, A+T‐ and A‐T‐ groups. Unpaired t‐test and one‐way ANOVA were used to test for significant differences between groups.

Result

2‐tailed p‐values were significant between the A‐T‐ group and the A+T+ group for MoCA total score [p: 0.0025 and p: 0.0025]. When we stratified MoCA in the different cognitive domains, we found that the results were driven by MoCA (memory) Delayed Recall score [p: 0.0007 and p: 0.0162]. A‐T+ vs A+T+ was also significant [p: 0.0099] for MoCA Delayed Recall. Both tau tracers (MK6240 and Flortaucipir) showed similar performance for determining T+ and identifying cognitive decline.

Conclusion

MoCA, a simple and commonly administered in‐office neuropsychological test, can detect subtle cognitive dysfunction in CU older adults who are Aß and tau positive (A+/T+). These CU A+/T+ individuals are likely on the path to developing AD dementia.
Associations Between Sex‐ and APOE‐Specific Transcriptomic Signatures in Alzheimer's Disease and Imaging‐Derived Phenotypes: An AZ‐ADRC‐Research Education Scholars Team Science Project
Authors: Raikes Adam C. , Vitali Francesca , Hernandez Gerson D , Yin Fei
Published: January 2025

Background

Age, female sex and the APOEe4 allele are among the top risk factors for developing late‐onset Alzheimer’s disease (LOAD). Precision medicine for AD drug development necessitates targeting specific biological pathways driving AD pathology. We previously identified LOAD‐associated transcriptomic signatures specific to both sex and APOE genotype. Here we extend these analyses to examine the association between these signatures and imaging‐derived phenotypes (IDPs).

Method

Brain RNA‐Seq datasets from ROSMAP (syn8456637) were obtained from the RNA‐Seq Harmonization Study on AMP‐AD, including 369 frontal cortex samples from APOEe3/e3 and APOEe3/e4 individuals. Differentially expressed genes (DEGs, p‐value<0.01) between LOAD and cognitively normal controls were identified for each sex‐genotype. IDPs were generated from 1155 individuals in ROSMAP, with 62 overlapping the RNA‐Seq Harmonization Study. T1‐weighted MRIs were processed using Fastsurfer (v.2.0.4), and cortical thickness (CT) and subcortical volumes (SV) were computed. After multi‐site harmonization and covariate adjustment, effect sizes (Cohen’s d) for controls vs. AD were computed for each sex‐genotype pairing. Correlations between these effect sizes and median regional DEG expression from the Allen Human Brain Atlas were examined.

Result

Controls showed anticipated CT and SV patterns, though male APOEe4s exhibited greater CT in more regions. Higher median expression of up (female APOEe3/e3, male APOEe4) and down (female APOEe3/e3, male APOEe3/e3, male APOEe4) regulated DEGs correlated with less CT in AD patients (r=0.21‐0.41, p < 0.046). In APOEe4 carriers, higher median up (male, female) and down (female) DEG expression correlated with greater SV in AD patients (r=0.48‐0.57, p < 0.043). Greater median downregulated DEG expression in APOEe3/e3s correlated with smaller SV (r=0.56, p=0.021). Higher median APOE expression was correlated with less CT in AD patients across sex‐genotype pairings (r=0.38‐0.86, p < 0.002), lower SV (females, all genotypes), and greater SV (male APOEe3/e3). No associations between median DEG expression and CT were observed in female APOEe4 carriers.

Conclusion

Our analyses reveal novel sex and APOE genotype‐specific transcriptomic signatures associations with imaging‐derived features in LOAD. Our findings transcriptome signature specific CT and SV profiles that may inform therapeutic targets. The present analyses provide support the identification and use of risk‐factor specific biomarkers of target engagement for preventative and therapeutic interventions.
The Association between Hepatic fat and Neuroinflammation in Midlife Obesity
Authors: Dolatshahi Mahsa , Rahmani Farzaneh , Commean Paul K. , Kasani Sara Hosseinzadeh , Naghashzadeh Mahshid , Lloyd LaKisha , Nguyen Caitlyn , McBee‐Kemper Abby , Weeks Jake , Ceriani Lael , Mittendorfer Bettina , Sirlin Claude , Song Sheng‐Kwei , Benzinger Tammie L.S. , Ippolito Joseph E. , Morris John C. , Raji Cyrus A.
Published: January 2025

Background

Obesity in midlife, body mass index (BMI) of 30 kg/m2 or higher, is recognized as a contributor to Alzheimer disease (AD) later in life. Adiposity in visceral tissues such as liver is associated with increased systemic inflammation and impaired cognition. In this study, we aimed to investigate the relationship between MRI‐derived Positron Density Fat Fraction (PDFF) and brain histology and neuroinflammation using Diffusion Basis Spectrum Imaging (DBSI) in cognitively normal midlife individuals.

Method

In total, 67 cognitively normal middle‐aged participants (Age: 50.02±6.00 years, female: 65.7%, obesity: 53.7%, BMI: 31.72±6.81 kg/m2) underwent brain and abdominal 3T MRI and metabolic assessment. Homeostatic Model Assessment for Insulin Resistance (HOMAIR) was used as for measuring insulin resistance. Using a trained U‐Net convolutional neural network (CNN) model, hepatic PDFF maps were calculated from conventional T1‐weighted images. The CNN included the Adam optimizer for training. A DBSI scheme with a total of 98 diffusion samplings was acquired. After eddy current and movement correction and removing non‐brain tissue, DBSI maps including fractional anisotropy (FA, overall integrity), axial diffusivity (AD, axonal injury), radial diffusivity (RD, myelin loss), restricted fraction (RF, inflammation cellularity), hindered fraction (HF, extracellular edema), and fiber fraction (FF, axonal density) maps were calculated using in‐house software scripted in MATLAB and Statistics Toolbox Release. DBSI‐derived maps were processed using a tract‐based spatial statistics (TBSS) pipeline to allow for whole‐brain white matter voxel‐wise analyses. Using the Randomize tool from FSL, the association of PDFF and HOMAIR with DBSI‐derived white matter skeleton, with age and sex as covariates, and a threshold of 0.05 for false‐discovery rate.

Result

There was a significant association between PDFF and HOMAIR (p<0.001), and obese individuals showed higher PDFF (p = 0.02) but no difference in HOMAIR (p = 0.12). We observed a significant positive association between PDFF and restricted fraction in widespread white matter tracts. There was no significant association between HOMAIR and DBSI measures.

Conclusion

Our results indicate the role of hepatic fat in increased inflammation‐related cellularity in the brain in cognitively normal midlife individuals. These findings suggest that excess hepatic fat can potentially increase the risk for Alzheimer disease and cognitive impairment at least partly through promoting neuroinflammation.
Optimal Transport‐Based Transcriptomic Mapping Revealed Atypical Disease Progression Subtypes in Living Alzheimer’s Disease Patients
Authors: Huang Xiaoqing , Zhang Jie , Huang Kun , Reeves Cristian A Lasagna , Jury Nur
Published: January 2025

Background

Alzheimer’s disease (AD) exhibits substantial heterogeneity in its disease trajectory. A subset of AD patients with unmatched cognitive decline/tauopathy severity has not been well studied. We identified such atypical subgroups in post‐mortem AD brain studies. However, such atypical subtypes may not be easily identified in living patients, as obtaining brain samples are unfeasible, and NFT measurement is not accurate. In this study, we utilize the matched transcriptomic data from both brain and blood of ROSMAP cohort to identify such atypical AD groups in the blood transcriptomic data of live patients in other cohorts using transfer learning‐based approach, to uncover distinct molecular signatures and biomarkers for earlier and more accurate disease subtyping and prognosis in living AD patients.

Method

Three subgroups were defined from ROSMAP cohort with the blood and brain RNA‐seq data based on the clinical information of their tauopathies and disease progression, namely, Asymptomatic AD, Low‐NFT AD, Typical AD, plus normal Control, which serves as our training dataset for a supervised transfer learning. Then, the labels were transferred to the blood RNA‐seq samples from two new cohorts, ADNI and ANMerge using optimal transport. Next, we identify the genes consistently expressed in three independent cohorts for that specific AD subtype. Lastly, the diffusion pseudo‐time analysis infers the temporal order of the gene expression patterns within each subgroups. Dominant genes with a consistent expression pattern across cohorts are considered as the signature for each subgroup, and their relevance to AD pathology is analyzed.

Result

We identified distinctive genes with consistent expression patterns across cohorts for each AD subgroup. Remarkably, our analysis also reveals the temporal gene expression dynamics differs for sex, age (late/early onset), and onset pattern (sudden/gradual) across the cohorts.

Conclusion

Through a deep transfer learning‐based approach on the blood and brain transcriptomic data, we successfully identified the atypical disease progression subgroups among live AD patient cohorts in ADNI and ANMerge with promising biomarkers/gene signatures. The molecular signatures identified in this study not only enhance our comprehension of the underlying pathophysiological mechanisms but also hold promise for developing early prognosis and effective personalized treatments for AD and related tauopathies.
White matter integrity is lower in non‐amnestic than amnestic Alzheimer's disease and differs by clinical syndrome
Authors: Phillips Jeffrey S , Adluru Nagesh , Chung Moo K , Radhakrishnan Hamsi , Olm Christopher A , Cook Philip A. , Gee James C. , Arezoumandan Sanaz , Cousins Katheryn A Q , Wolk David A , McMillan Corey T , Irwin David J
Published: January 2025

Background

Amnestic mild cognitive impairment and Alzheimer’s disease (aAD) exhibit degeneration of white matter (WM) tracts preceding overt cognitive decline. However, WM changes in non‐amnestic AD (naAD) are understudied. We hypothesized patterns of WM degeneration would differ between aAD and naAD.

Methods

We compared WM degeneration, assessed by 30‐direction diffusion‐weighted imaging (DWI), in 41 individuals with aAD; 67 with naAD diagnoses including logopenic‐variant primary progressive aphasia (lvPPA), posterior cortical atrophy (PCA), behavioral variant AD (bvAD), and corticobasal syndrome (CBS); and 45 with normal cognition (Table 1). We performed deterministic tractography between 148 cortical/subcortical regions; connectivity was quantified by generalized fractional anisotropy (GFA). Tractwise GFA and regional grey matter (GM) volumes were converted to W‐scores adjusting for age and sex. We contrasted GFA between groups using regression models covarying for MMSE score. A mixed effects model was used to assess associations between tractwise GFA and volume of GM endpoints. Additionally, we computed the number of connected components in each person’s brain graph (i.e., number of subgraphs created by progressively stricter thresholds) across 100 GFA values. We contrasted this topological metric between groups and assessed its associations with MMSE and disease duration.

Results

GM volume was lower in naAD and aAD than controls (both p<0.0001) but did not differ between patient groups (p=0.81). aAD and naAD patients had shared WM degeneration in corpus callosum, cingulum, and inferior/superior longitudinal fasciculi; however, WM degeneration was more severe in naAD (Fig. 1). In lvPPA, WM degeneration was left‐lateralized; in PCA, GFA was reduced in bilateral parietal, occipital, and temporal areas (Fig. 2). Across groups, GM and WM degeneration were significantly associated [F(1,20442)=326.0, p<0.0001]. Topological analysis indicated naAD but not aAD patients had lower global connectivity than controls. A higher component number (indicating reduced connectivity) was associated with longer disease duration and lower MMSE.

Conclusion

Associations between GM volume and GFA suggest WM degeneration in naAD is related to AD pathologic change and not an independent process. Greater WM disease in naAD than aAD was not attributable to GM atrophy or symptom severity. Finally, topological metrics may serve as objective markers of global disease progression.
Voxel‐wise comparison of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of individuals across the Alzheimer’s disease spectrum – the HEAD study
Authors: Bellaver Bruna , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Amaral Livia , Ferreira Pamela C.L. , Lowe Val J. , soleimani‐meigooni David N. , Oh Hwamee , Tudorascu Dana , Jagust William J. , Klunk William E , Pascual Belen , Gordon Brian A. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali
Published: January 2025

Background

In vivo studies using the tau PET tracers have shown high performance for the diagnosis of Alzheimer’s disease dementia and patterns of tracer uptake that resemble those observed in post‐mortem studies. However, tau tracers present distinct patterns of binding that might influence their performance in detecting AD pathology. In a head‐to‐head study, we investigated the performance of [18F]MK6240 and [18F]Flortaucipir for the diagnosis of AD.

Method

We assessed 132 individuals from the HEAD study (58 CU Aß‐, 15 CU Aß+, 14 MCI Aß‐, 32 MCI Aß+ and 13 AD dementia) with Aß‐PET, [18F]MK6240 and [18F]Flortaucipir. Voxel‐wise receiver operating characteristic curves (ROC) of the two tau tracers were used to contrast groups provided the area under the curve (AUC) for disease diagnosis or biomarkers positivity.

Result

The brain maps showed numerically higher and more spread AUC for [18F]Flortaucipir to discriminate CU Aß‐ from CU Aß+ (Fig.1A). The difference between tracers’ AUC was greater in Braaks IV and V (Fig.2A), both regions that are not expected to have tau accumulation in CU Aß+ individuals, reflecting a potential off‐target binding for [18F]Flortaucipir. To differentiate CU Aß‐ from MCI Aß+ individuals, [18F]MK6240 presented a numerically higher AUC than [18F]Flortaucipir in Braak I and II and similar AUC in other Braak regions (Fig.1B, Fig. 2B). We observed a high performance of [18F]MK6240 and [18F]Flortaucipir in differentiating AD dementia from CU Aß‐ individuals. However, [18F]MK6240 exhibits a higher AUC than [18F]Flortaucipir in all Braak regions, especially Braak V‐VI (Fig.1C, Fig.2C). Finally, [18F]MK6240 presented higher AUC in all Braak regions to discriminate MCI Aß‐ from MCI Aß+ individuals (Fig.1D, Fig.2D).

Conclusion

Our results indicate that [18F]MK6240 and [18F]Flortaucipir present high accuracy to discriminate AD from CU Aß‐ individuals. However, [18F]MK6240 presents higher AUC to discriminate AD and MCI Aß+ from CU Aß‐ individuals than [18F]Flortaucipir. Together, our head‐to‐head study sheds light on the distinct patterns of binding for Tau‐PET tracers.
Multicentre validation of the dementia with Lewy bodies‐related metabolic brain pattern
Authors: Perovnik Matej , Simončič Urban , Kos Hana , Jamšek Jan , Kramberger Milica G , Brendel Matthias , Camacho Valle , Vandenberghe Rik , Garibotto Valentina , Ochoa‐Figueroa Miguel A , Padovani Alessandro , Morbelli Silvia , Ferreira Daniel , Trošt Maja
Published: January 2025

Background

Dementia with Lewy bodies (DLB) is a an a‐synucleinopathy characterized by dementia and a combination of parkinsonism, visual hallucinations, fluctuating cognition or REM sleep behaviour disorder. Specific biomarkers for DLB are lacking. DLB‐related pattern (DLBRP) is a metabolic network imaging biomarker which expression can be quantified on a single patient basis. DLBRP has been identified in few different cohorts but lacks a multicentric validation.

Methods

FDG PET scans from 259 DLB patients and 176 normal controls (NC) were obtained from eight centres from European DLB Consortium (EDLB, 180 patients), Alzheimer’s disease Neuroimaging Initiative (ADNI, 135 NC) and local dataset from Ljubljana (LJU, 79 patients and 41 NC). FDG PET scans were pre‐processed and we used topographic profile rating to calculate the DLBRP expression (i.e. subject scores). Subject scores were compared between groups using t tests and ANOVA and correlated with clinical parameters using Pearson’s correlation coefficient.

Results

Patients from different centres differed in MMSE (p < 0.001), proportion of patients with parkinsonism (p < 0.001) and visual hallucinations (p < 0.001), but not in age (p = 0.15) or sex distribution (p = 0.13). DLB patients from all centres had significantly higher DLBRP expression than NCs (all p < 0.001). We could accurately distinguish between DLB and NC (AUC = 0.983) based on DLBRP expression. We observed a negative correlation between DLBRP expression and MMSE (r = –0.30, p < 0.001). The expression of DLBRP differed among centres, post hoc analysis showed that only patients from Barcelona had significantly lower DLBRP subject scores in comparison to other groups but they were also less cognitively impaired. Patients with visual hallucinations (p < 0.001) had significantly higher DLBRP expression than those without hallucinations and patients with abnormal Datscan™ (p = 0.02) had higher DLBRP expression than those with normal Datscan™. Other core features, levodopa or antidementives did not affect the DLBRP expression.

Conclusions

We showed that DLBRP can accurately distinguish between DLB and NC from multiple centres and different scanners. The DLBRP expression correlated with measurements of cognitive impairment. This validation study confirms that the DLBRP is a robust metabolic imaging biomarker of DLB.
Locus coeruleus metabolism predicts the accumulation of Alzheimer’s disease pathology in amyloid‐positive symptomatic individuals
Authors: Koops Elouise A. , Dutta Joyita , Becker Alex , Hanseeuw Bernard J , Sperling Reisa A , Johnson Keith A , Jacobs Heidi I.L.
Published: January 2025

Background

Higher cerebrospinal fluid noradrenergic metabolic turnover has been associated with higher levels of Alzheimer’s disease pathology in cognitively impaired individuals. It remains unclear whether there is a specific anatomic vulnerability to metabolic alterations within the locus coeruleus (LC) and whether this hypermetabolism relates to steeper rates of pathology accumulation. Here, we overcome a spatial limitation of Positron Emission Tomography (PET) imaging in small nuclei with a dedicated Magnetic Resonance Imaging (MRI)‐guided framework to recover PET resolution. This method allowed us to investigate the [18F]Fluorodeoxyglucose (FDG)‐PET signal, reflecting energetic metabolic demand of the LC and its association with serial beta‐amyloid and tau‐PET.

Methods

We included 78 amyloid‐positive, cognitively impaired participants from the ADNI‐3 cohort (Table 1). The LC was identified using a dilated Keren template registered to each T1 (Fig 1A). The LC mask was subdivided into a rostral, middle, and caudal part to investigate region‐specific associations (Fig 1B). We used an MR joint‐entropy‐penalty algorithm to quantify PET signal via deconvolution based on spatially‐variant blur kernels. The deconvolution step was stabilized using an anatomical‐based joint‐entropy prior (Fig 1C). The FDG‐PET signal was referenced to the pons, excluding the LC (Fig 1D). Amyloid positivity was determined as a centiloid score = 21, reflecting moderate neuritic plaques, on florbetaben and florbetapir scans. Linear mixed effects models investigated relationships between LC‐FDG, global beta‐amyloid and meta‐temporal tau burden (flortaucipir) over time. Age and sex were included as covariates.

Results

Higher LC FDG‐PET signal was associated with higher meta‐temporal tau‐PET at baseline (T = 2.87, p = 0.00463) and accumulation over time (T = 2.257, p = 0.0273; Fig 2A&C). Higher LC FDG‐PET signal was associated with higher global amyloid at baseline (T = 2.066, p = 0.041) and faster amyloid accumulation over time in the dementia group (T = ‐2.74, p = 0.00904; Fig 2B&D), following a rostro‐caudal gradient.

Conclusion

In a cohort with cognitive impairment, we observed steeper accumulation of Alzheimer’s disease pathology in individuals with higher LC FDG‐PET signal, with a specific amyloid‐related vulnerability for the rostral parts of the LC. Future steps will be to include cognitively unimpaired individuals to investigate the relationship between LC FDG‐PET signal and pathology accumulation in early stages of the disease process.
Impact of TDP‐43 co‐pathology on limbic cortical microstructure in Alzheimer's disease: evidence from an autopsy‐confirmed cohort with in vivo diffusion MRI
Authors: Torso Mario , Ridgway Gerard R , Hardwidge Jamie , Hardingham Ian , Chance Steven A ,   National Alzheimer’s Coordinating Center,   Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Published: January 2025

Background

TDP‐43 (TAR DNA‐binding protein 43) is one of the most frequently observed co‐pathologies in Alzheimer's disease (AD). Recognizing the diversity of pathological features in individuals with AD, including the presence of TDP‐43, may lead to more personalized and effective treatment approaches. We investigate ante‐mortem cortical microstructural changes in MRI with subsequent autopsy confirmation of Alzheimer’s disease neuropathological changes (ADNC) with and without TDP‐43 comorbidity.

Method

Participants with ADNC and ante‐mortem MRI were obtained from the National Alzheimer's Coordinating Center (NACC; n=46) and the Alzheimer's Disease Neuroimaging Initiative (ADNI; n=8). Participants were grouped based on TDP‐43 presence (36 AD “pure” and 18 AD with TDP‐43). Thal phase, CERAD neuritic plaque assessment, Braak NFT, and combined ABC score, were used as AD neuropathological hallmarks. Cases with TDP‐43 = Stage 2 were considered AD with TDP‐43. Structural and diffusion MRI were used to calculate cortical diffusivity measures in five functional macroregions: Primary Sensory, Unimodal, Limbic/Proisocortex, Heteromodal and Primary Motor (Ffytche & Wible 2014, PMID:24936089; Mesulam 1998, PMID:9648540). Cortical mean diffusivity and three minicolumn‐inspired cortical diffusivity measures were calculated: the angle between the radial minicolumnar direction and the principal diffusion direction (AngleR); the principal diffusion component parallel with the minicolumns (ParlPD), and the diffusion components perpendicular to the minicolumns (PerpPD+) (Torso et al. 2022, PMID:36281682). Groups were characterized using clinical, demographic, and neuropathological data (see table). Differences in diffusion metrics were tested adjusting for interval between MRI scan date and autopsy date, scanner manufacturer, diffusion b‐value, age and sex, with FDR correction (pFDR<0.05).

Result

The Limbic/Proisocortex macroregion exhibited significantly higher AngleR (F1,36= 7.978; pFDR=0.04) in AD with TDP‐43 (AngleR mean 1.135) compared to the AD pure group (1.055).

Conclusion

Diffusion MRI can detect differences in cortical microstructure of AD with TDP‐43 co‐pathology, suggesting more extensive cortical damage in AD with TDP‐43 comorbidity that involves Limbic cortical areas (cingulate, entorhinal, parahippocampal, and temporal pole). Consistent with previous findings regarding early cortical changes associated with amyloid deposition and neuroinflammation (Torso et al. 2022, PMID:36281682 Torso et al. 2323PMID:37794477), the diffusion‐derived metric AngleR appears particularly sensitive to subtle effects, such as the impact of TDP‐43 in addition to ADNC.
Association between brain metabolism and anxiety in long COVID in an underrepresented cohort
Authors: de Souza Débora Guerini , Silveira Maiele Dornelles , Borelli Wyllians Vendramini , Senger Joana Emilia , Machado Luiza Santos , Ferrari‐Souza João Pedro , de Bastiani Marco Antônio , Povala Guilherme , da Silva Ana Paula Bornes , de Mello Guilherme Bastos , da Ros João Pedro Uglione , Jotz Arthur Viana , Kadan Matheus Fakhri , Radaelli Graciane , Faria Daniele de Paula de Paula , Coutinho Artur Martins , Lourenco Mychael V , Pascoal Tharick A. , Rosa‐Neto Pedro , Matushita Cristina Sebastião , Soder Ricardo Benardi , Schumacher‐Schuh Artur Francisco , Souza Diogo O. , da Costa Jaderson Costa , Zimmer Eduardo R.
Published: January 2025

Background

Long COVID is an under‐characterized disorder that affects a wide range of individuals after COVID‐19 resolution. Long COVID individuals report persistent neurological manifestations, such as anxiety. Understanding its effects in the brain might help uncover the actual burden imposed by the pandemic sequelae and either define or discard long COVID as a risk factor for neurodegenerative diseases. Here, we aim to identify whether there is an association between brain metabolism and anxiety in an underrepresented population.

Method

Community‐dwelling individuals, above 50 years old, from Porto Alegre, Brazil, were divided into long COVID (n = 39) and control groups (n = 10) were evaluated with a battery of neuropsychological testing, including the GAD‐7 scale of anxiety. Then, they underwent a brain [18F]FDG‐PET scan (images normalized by the pons). We conducted a voxel‐wise linear regression testing the association between [18F]FDG metabolism and GAD‐7, and corrected for education, sex, and age. The analysis was corrected for multiple comparisons using the cluster‐wise random field theory method (significant t←3.34 and t>3.34, p<0.001, df = 35).

Result

We found that GAD‐7 score presented a widespread negative association with [18F]FDG metabolism in multiple gray and white matter regions (Figure 1). Specifically, hippocampus (tmax = ‐3.34, p = 0.002), amygdala (tmax = ‐3.82, p = 0.0005), cerebellum (tmax = ‐4.28, p = 0.0001), and lateral occipitotemporal gyrus (tmax = ‐5.26, p = 0.0001) had the most relevant associated clusters in gray matter, while temporal lobe (tmax = ‐3.9, p = 0.0004) and frontal lobe (tmax = ‐4.33, p = 0.0001) presented the most relevant associated clusters in white matter.

Conclusion

Anxiety symptoms are a highly self‐reported symptom in long COVID. Here we show that anxiety is widely associated with reduced brain glucose metabolism in crucial areas for the limbic system and cognition, such as the hippocampus and amygdala. The peculiar associations between anxiety and FDG metabolism in white matter may suggest inflammatory responses triggered by long COVID. These data provide new insights into the mechanisms underlying long COVID symptoms in the brain.
Biological aging is associated with atypical neurodegenerative patterns in Alzheimer's disease
Authors: Sreepada Lasya P , Brown Christopher , Das Sandhitsu R. , Yushkevich Paul A. , Wolk David A , McMillan Corey T
Published: January 2025

Background

Atypical presentations of Alzheimer’s disease (AD), which demonstrate more cortical involvement relative to medial temporal lobe (MTL), are generally associated with younger age of onset. Age, defined chronologically, is a primary driver of AD pathology and neurodegeneration. However, some young onset cases are typical, amnestic presentations and some older onset cases present more cortical atrophy. We hypothesize that a biological definition of age, defined using epigenetic clock measures, may be even more tightly linked to atypicality than chronological age.

Method

Subjects from ADNI with blood DNA methylation (DNAm) samples and T1 MRI (N=646, 55.7% female, 75.5 +/‐ 7.4 years, Table 1) were selected. Participants were defined as “young” (< 65 years) or “old” (> 80 years) amyloid‐negative controls or amyloid‐positive MCI/AD. DNAm was assayed on Illumina EPIC arrays. Epigenetic age was computed by applying the Shireby cortical epigenetic clock. We then defined epigenetic age acceleration (EAA) by regressing epigenetic age against chronological age and extracting the residual. Subjects were classified as appearing “biologically younger” (EAA < 1 SD below the mean), “neutral” (EAA within 1 SD), or “biologically older” (EAA > 1 SD above the mean). Regional cortical thickness measures were derived from MRI using FreeSurfer 5.1 and harmonized for scanner differences using longitudinal ComBat. We computed composite bilateral thickness z‐scores, adjusted for age and sex relative to controls, in the cortical AD signature anatomical ROIs (Figure 1) and the MTL (entorhinal and parahippocampal cortices). Finally, we defined an AD “mismatch” score as the signed difference of the composite cortical and MTL thickness z‐scores.

Result

Cortical thickness z‐scores were lower in MCI/AD relative to controls. Lower mismatch scores, reflecting greater cortical neurodegeneration relative to MTL, were observed in young‐onset relative to older‐onset MCI/AD. Similarly, mismatch scores were significantly lower in biologically younger cases relative to biologically older cases (t=2.5589; p = 0.01; Figure 2).

Conclusion

Our results support the notion that biological age, as measured by epigenetic modifiers, modulates the degree of atypicality in AD patients, akin to chronological age. These findings suggest that biological aspects of aging may influence the topography of AD‐related pathology and spread.
Detection of presymptomatic cerebral amyloid angiopathy, CAA, using diffusion MRI analysis of cortical grey matter in hereditary mutation carriers
Authors: Chance Steven A , Hardingham Ian , Valotti Michele , van Rooden Sanneke , van der Grond Jeroen
Published: January 2025

Background

HCHWA‐D mutation carriers experience highly heritable early onset of cerebral amyloid angiopathy (CAA). Diffusion MRI appears to be sensitive to CAA and detects punctate cortical lesions in CAA cases post‐mortem. DTI of white matter tracts was found to be sensitive to alterations in symptomatic HCHWA‐D carriers but did not show alterations in presymptomatic carriers (Schouten et al. https://doi.org/10.1161/JAHA.118.011288.). The diffusion signal from cortical grey matter microstructure may yet detect presymptomatic cases.

Method

CDM (cortical disarray measurement) quantifies DTI parameters related to the vertical minicolumnar microstructure of cortical grey matter that is disrupted in neurodegeneration. AngleR is the angle between the principal diffusion direction and the cortical minicolumn axis. PerpPD+ and ParlPD are the components parallel and perendicular to the minicolumn axis. Participants from an existing dataset (Schouten et al.), included 12 symptomatic and 11 asymptomatic HCHWA‐D mutation carriers and 24 non‐carrier controls (based on: genotype, clinical status, and age). Diffusion MRI had been acquired at a single time point from a Philips Achieva 3T scanner at Leiden University Medical Center. Statistical GLM used multivariate ANOVA, classifying patients by Mutation status (carrier vs non‐carrier) and Clinical status (non‐carrier control, asymptomatic carrier, symptomatic carrier), covarying for age.

Result

For the whole brain cortical average PerpPD+ was significantly increased in mutation carriers (p<0.05) driven by a significantly increased PerpPD+ in symptomatic carriers compared to both asymptomatic groups (controls and carriers) (p<0.05). At the regional level some measures in prespecified regions of interest were significantly altered in symptomatic carriers and, notably, significantly different in mutation carriers (p<0.05), both symptomatic and asymptomatic groups, compared to controls.

Conclusion

Diffusion MRI analysis of cerebral cortex detects microstructural differences related to clinical symptoms of CAA in HCHWA‐D mutation carriers and particular measures of the microstructure organisation differentiate the CAA group, including presymptomatic, from controls.
Psoas Muscle Sarcopenia Predicts Brain Volume Loss on MRI in 7,149 Individuals
Authors: Raji Cyrus A. , Meysami Somayeh , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul
Published: January 2025

Background

Sarcopenia has been linked to brain atrophy and there is lack of information on specific muscle groups that may contribute to this link. The psoas muscles are sensitive to sarcopenia and thus may sensitively relate to brain aging and Alzheimer disease risk.

Method

This study utilized 7,149 healthy individuals across four sites (Mean age 53.06 ± 12.95 years, age range 18‐97 years; 48% women; 52% men; 38% non‐white) were scanned on 1.5T MR machines with a whole‐body protocol. Whole body sequences included coronal T1 for segmentation of psoas muscle volumes. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 segmented 96 brain regions. Partial correlation analysis was done on psoas muscle volumes and brain regions controlling for sex and total intracranial volume to determine if lower psoas muscle volumes correlated to brain atrophy. Multiple comparisons were accounted for using the Bonferroni Method.

Result

Lower psoas muscle volumes demonstrated a statistically partial correlation to lower gray matter (rp = ‐0.164, p = 2.66×10‐43) and white matter volumes (rp = ‐0.171, p = 4.39×10‐47). The frontal lobe exhibited a negative correlation (rp = ‐0.172, p = 1.30×10‐47), identical to the temporal lobe (rp = ‐0.172, p = 1.21×10‐47). Negative correlations were also noted in the parietal lobe (rp = ‐0.119, p = 8.72×10‐23), and occipital lobe (rp = ‐0.139; p = 2.89×10‐31). The hippocampus also showed a statistically significant negative correlation (rp = ‐0.145, p = 7.65×10‐34), as did the posterior cingulate (rp = ‐0.162, p = 5.20×10‐42) and the precuneus (rp = ‐0.101, p = 1.34×10‐16). The cerebral ventricles showed a non‐significant negative correlation (rp = ‐0.025, p = 0.345).

Conclusion

We demonstrate a significant correlation between lower psoas muscle volume and brain volume loss, including AD risk regions such as the hippocampus and precuneus. These MRI findings underscore the importance of muscle health in relation to brain integrity. The volume of the psoas, which is involved in basic leg flexion activities such as walking, could be a crucial indicator of brain health and Alzheimer risk.
Large‐scale Normative Modeling of Brain Microstructure over the Human Lifespan
Authors: Villalon‐Reina Julio E , Zhu Alyssa H , Benavidez Sebastian M , Moreau Clara , Feng Yixue , Laltoo Emily , Chattopadhyay Tamoghna , Nourollahimoghadam Elnaz , Thomopoulos Sophia I , Nabulsi Leila , Lawrence Katherine E , Weiner Michael W , Jack Clifford R. , Nir Talia M , Jahanshad Neda , Thompson Paul M.
Published: January 2025

Background

Normative models (NM) of brain metrics based on large, diverse populations offer novel strategies to detect individual brain abnormalities. To create an age‐dependent statistical model of brain microstructure over the human lifespan, we built the largest multi‐site NM of white matter (WM) diffusion tensor imaging (DTI) metrics based on 54,591 subjects. We used state‐of‐the‐art tools to adjust for site‐dependent effects.

Method

We used Hierarchical Bayesian Regression (HBR) to determine the age trajectory and lifespan centile curves (ages 3‐100 years) by merging data from different sections of the lifespan. We analyzed 19 international public datasets with the ENIGMA‐DTI protocol. Mean fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AxD, RD) were extracted for 21 bilateral ROIs from the JHU‐WM atlas and the whole WM skeleton. Regressions were run with each metric per ROI as a function of age and sex, and the DTI protocol as the batch effect (37 protocols). Z‐scores were derived for 81 patients with Alzheimer’s disease (AD; mean age: 77y±8.4, 46M/35F) and 225 MCI participants (mean age: 75.1y±8.1, 127M/98F) and extreme deviations (Z>|2|) were quantified for each condition. ROI‐wise areas under the ROC curve were calculated to determine the classification accuracy of the Z‐scores.

Result

The average FA for the overall WM skeleton reached its peak value at 27 years, with minima for MD, RD and AxD at 47, 42 and 54 years, respectively. We found extreme FA deviations (Z>2) for MCI (AUC=0.67) and AD (AUC=0.66) in the genu of the corpus callosum, extreme RD deviations (Z<2) in the tapetum (AUC=0.73) and Extreme AxD deviations (Z>2) for MCI in the superior longitudinal fasciculus (AUC=0.65). See Figures 1 & 2.

Conclusion

NM of brain microstructure with HBR yielded lifespan trajectories for widely‐used DTI metrics. By pooling large scale multi‐site dMRI to create a statistical reference, single subject deviations are detectable across the lifespan for AD and MCI. These NM will be publicly available to the research community.
Leveraging T1 MRI Images for Amyloid Status Prediction in Diverse Cognitive Conditions Using Advanced Deep Learning Models
Authors: Hosseini Seyyed Ali , Servaes Stijn , Rahmouni Nesrine , Therriault Joseph , Tissot Cécile , Macedo Arthur C. , Wang Yi‐Ting , Arias Jaime Fernandez , Socualaya Kely Monica Quispialaya , Zheng Yansheng , Chan Tevy , Trudel Lydia , Gauthier Serge , Rosa‐Neto Pedro
Published: January 2025

Background

Timely and non‐invasive prediction of amyloid status are pivotal in Alzheimer’s disease (AD) diagnostics. This research leverages T1 MRI images to predict amyloid positivity or negativity, offering an economical and less invasive alternative to amyloid PET scans. Using the comprehensive TRAID dataset from McGill University, the study evaluates a spectrum of cognitive conditions including AD, atypical AD, Cognitively Normal (CN), Mild Cognitive Impairment (MCI), MCI not due to AD, Suspected Non‐Alzheimer’s Pathophysiology (SNAP), and Vascular MCI (VMCI).

Method

The study involved 588 subjects, representing a cross‐section of cognitive states. A VGG16‐based feature extraction process was meticulously applied to T1 MRI images to capture complex biomarkers of AD. These high‐dimensional features, alongside demographic data, were harnessed to train two deep learning models: a Long Short‐Term Memory (LSTM) model and a Convolutional Neural Network (CNN) ensemble model. To prevent overfitting and data leakage, the models employ 10‐fold StratifiedKFold validation and early stopping, alongside regularization techniques like dropout and batch normalization. Data preprocessing is contained within StratifiedKFolds, and an independent test set is used for unbiased evaluation. The predictive accuracy was scrutinized using a range of metrics, including accuracy, F1‐score, recall, precision, and ROC AUC on the validation sets.

Result

The LSTM model reported average validation metrics with an accuracy of 70.88%, F1‐score of 70.64%, recall of 78.07%, precision of 70.88%, and a ROC AUC of 71.86%. The CNN ensemble model showed a marked improvement, particularly in its recall of 88.00% and ROC AUC of 82.47%, alongside an accuracy of 76.27% and F1‐score of 75.86% (Figure 1 and 2). However, in another model, it was the CNN ensemble model that demonstrated remarkable predictive power, achieving an ROC AUC of 88% within the AD spectrum (CN, MCI, and AD subjects), indicating a high degree of predictive power in classifying amyloid status.

Conclusion

This study underscores the CNN ensemble model’s robustness in predicting amyloid status across a broad spectrum of cognitive conditions, with particular emphasis on its application in high‐sensitivity clinical settings. These findings represent a significant advancement in the field of AD diagnostics, contributing to the development of more accessible and cost‐effective prognostic techniques.
Multimodal characterization of Alzheimer’s Disease using speech, eye movement, and handwriting
Authors: Moro‐Velazquez Laureano , Favaro Anna , Thebaud Thomas , Wang Yuzhe , Butala Ankur , Oh Esther S , Dehak Najim
Published: January 2025

Background

Phenotyping Alzheimer’s Disease (AD) can be crucial to providing personalized treatment. Several studies have analyzed the use of digital biomarkers to characterize a subject’s behavior, usually obtained from a single modality, such as speech. However, combining several modalities in a single study has not been deeply studied. We aim to characterize people with AD and differentiate them from a cognitively normal control group (CN), employing metrics obtained through from three different modalities: speech, handwriting, and eye movement.

Method

We collected data from a group of 11 participants in the early stages of AD or with, mild cognitive impairment due to AD, and an age‐matched CN group of 13 participants. Each participant read out loud the Rainbow Passage, while their speech was recorded with a headset microphone and their eye movement with a high precision eye‐tracker, simultaneously. Then, the participants were asked to draw a spiral on a digital pad. We obtained 57 speech metrics, related to articulation, prosody, and hesitation; 90 handwriting metrics, characterizing drawing entropy, writing speed, and acceleration; 32 eye movement metrics related to saccade, fixation, and blink statistics. Several multilayer perceptron (MLP) classifiers were trained per modality and using all modalities together, following a cross‐validation strategy. SHAP values were calculated to rank metric impact in the differentiation between AD and CN.

Result

Cross‐validation accuracy in the binary classification of AD/CN ranged between 75.3% and 77.4% for single modalities, whereas the combination of the three modalities provided a 77.9% accuracy. The drawing velocity skewness metric provided the highest area under the receiver operating characteristic curve (AUC) from all the handwriting features (0.83), whereas the pause variability was the speech metric with the highest AUC (0.79). The number of fixations provided the best differentiation between groups in the eye movement modality, with AUC = 0.69.

Conclusion

Combining the three proposed modalities provides a higher differentiation power than using each modality separately. The combination can provide a more insightful and complementary characterization of patients. Handwriting metrics have the most impact on the prediction of the multimodal MLP model, according to the SHAP values on the multimodal classifier.
Identifying an EEG Biomarker for Memory Recall through EEG and EDA in the Presence of Music
Authors: Das Rupak Kumar , Khan Arshia A , Imtiaz Nabiha Zainab
Published: January 2025

Background

There is ample evidence that music can boost brain activity and jog deeply embedded memories. Literature indicates a significant improvement in autobiographical memory (ABM) recall for different individuals during background music sessions. Existing research is based solely on qualitative data, although music has a significant impact on physiological activity. Thus, it’s important to explore the connection between memory recall and physiological activities.

Method

To better understand memory recall, the electroencephalogram (EEG) and electrodermal activity (EDA) data were gathered from healthy participants using wearable sensors. Physiological signals such as the electroencephalogram (EEG) and electrodermal activity (EDA) were recorded as quantitative data using various wearable sensors from 40 participants of different age groups while playing different background music sessions. The study involved listening to nine music sessions (three happy, three sad, and three neutral). Immediately after each piece of music, a post‐study survey was conducted to gauge if the participants recalled any autobiographical memories. A machine learning algorithm was developed to train a model using features collected from physiological data to determine if the memory recall was successful. The purpose of the study was to identify an EEG biomarker.

Result

The results of the EEG and EDA data analysis revealed that for all four EEG channels, there was a consistent increase in the alpha power (on average 16.2%) during the memory “recall” scenario (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0.0288) compared to the “no‐recall” control. There was also a significant surge in the Beta power for two channels (F3: p = 0.0100 and F4: p = 0.0210) but not for the control (F7: p = 0.6792 and F8: p = 0.0814). Additionally, the EDA data analysis revealed significant differences in the phasic standard deviation (p = 0.0260), phasic max (p = 0.0011), phasic energy (p = 0.0478), tonic min (p = 0.0092), tonic standard deviation (p = 0.0171), and phasic energy (p = 0.0478). This implies that the memory recall biomarker is alpha power (8‐12 Hz).

Conclusion

The results indicate that the biomarker for memory recall is alpha power (8‐12Hz).