Atrophy In Alzheimer's Disease Research Articles

Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation

Background: Application of visual scoring scales for regional atrophy in Alzheimer’s disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer’s disease

BackgroundResearch has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer’s disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse.ObjectiveTo investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression.MethodsWe analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy.ResultsThe small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups.ConclusionThese findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

D-peptide-magnetic nanoparticles fragment tau fibrils and rescue behavioral deficits in a mouse model of Alzheimer's disease.

Amyloid fibrils of tau are increasingly accepted as a cause of neuronal death and brain atrophy in Alzheimer's disease (AD). Diminishing tau aggregation is a promising strategy in the search for efficacious AD therapeutics. Previously, our laboratory designed a six-residue, nonnatural amino acid inhibitor D-TLKIVW peptide (6-DP), which can prevent tau aggregation in vitro. However, it cannot block cell-to-cell transmission of tau aggregation. Here, we find D-TLKIVWC (7-DP), a d-cysteine extension of 6-DP, not only prevents tau aggregation but also fragments tau fibrils extracted from AD brains to neutralize their seeding ability and protect neuronal cells from tau-induced toxicity. To facilitate the transport of 7-DP across the blood-brain barrier, we conjugated it to magnetic nanoparticles (MNPs). The MNPs-DP complex retains the inhibition and fragmentation properties of 7-DP alone. Ten weeks of MNPs-DP treatment appear to reverse neurological deficits in the PS19 mouse model of AD. This work offers a direction for development of therapies to target tau fibrils.

Alzheimer's disease: diagnosis and treatment, errors in patient management

Alzheimer's disease (AD) is the most common degenerative disease of the brain leading to dementia. AD is the most common cause of disability among age-related diseases. The diagnosis of AD is based on clinical findings and is confirmed by the presence of positive biological markers of the disease, which reflect the pathological formation of beta-amyloid and tau protein in the brain. Magnetic resonance imaging of the brain shows brain atrophy in AD and helps to rule out other diseases. Psychosocial and behavioral approaches form the basis for the treatment of patients with AD; cognitive training in combination with regular physical exercise is recommended. Acetylcholinesterase inhibitors and the glutamate receptor blocker memantine are used as drugs that improve the cognitive functions of patients with AD. Dispersible forms of memantine are used to treat dysphagia in patients with AD. The efficacy and safety of pathogenetic therapy aimed at eliminating cerebral beta-amyloid by passive immunization is under discussion. Unfortunately, AD is rarely diagnosed in our country; doctors are not sufficiently informed about the diagnosis and modern treatment methods of AD. Many AD patients are observed with the misdiagnosis of chronic cerebrovascular disease and do not receive effective therapy. A clinical observation of a patient with early-onset AD is presented, reflecting typical errors in patient management. Issues for optimizing the management of AD patients are discussed.

Multi-pathological contributions toward atrophy patterns in the Alzheimer's disease continuum.

Heterogeneity in downstream atrophy in Alzheimer's disease (AD) is predominantly investigated in relation to pathological hallmarks (Aβ, tau) and co-pathologies (cerebrovascular burden) independently. However, the proportional contribution of each pathology in determining atrophy pattern remains unclear. We assessed heterogeneity in atrophy using two recently conceptualized dimensions: typicality (typical AD atrophy at the center and deviant atypical atrophy on either extreme including limbic predominant to hippocampal sparing patterns) and severity (overall neurodegeneration spanning minimal atrophy to diffuse typical AD atrophy) in relation to Aβ, tau, and cerebrovascular burden. We included 149 Aβ + individuals on the AD continuum (cognitively normal, prodromal AD, AD dementia) and 163 Aβ- cognitively normal individuals from the ADNI. We modeled heterogeneity in MRI-based atrophy with continuous-scales of typicality (ratio of hippocampus to cortical volume) and severity (total gray matter volume). Partial correlation models investigated the association of typicality/severity with (a) Aβ (global Aβ PET centiloid), tau (global tau PET SUVR), cerebrovascular (total white matter hypointensity volume) burden (b) four cognitive domains (memory, executive function, language, visuospatial composites). Using multiple regression, we assessed the association of each pathological burden and typicality/severity with cognition. (a) In the AD continuum, typicality (r = -0.31, p < 0.001) and severity (r = -0.37, p < 0.001) were associated with tau burden after controlling for Aβ, cerebrovascular burden and age. Findings imply greater tau pathology in limbic predominant atrophy and diffuse atrophy. (b) Typicality was associated with memory (r = 0.49, p < 0.001) and language scores (r = 0.19, p = 0.02). Severity was associated with memory (r = 0.26, p < 0.001), executive function (r = 0.24, p = 0.003) and language scores (r = 0.29, p < 0.001). Findings imply better cognitive performance in hippocampal sparing and minimal atrophy patterns. Beyond typicality/severity, tau burden but not Aβ and cerebrovascular burden explained cognition. In the AD continuum, atrophy-based severity was more strongly associated with tau burden than typicality after accounting for Aβ and cerebrovascular burden. Cognitive performance in memory, executive function and language domains was explained by typicality and/or severity and additionally tau pathology. Typicality and severity may differentially reflect burden arising from tau pathology but not Aβ or cerebrovascular pathologies which need to be accounted for when investigating AD heterogeneity.

Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling.

Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy. We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n=86). Regional cortical thickness was compared to a healthy reference cohort (n=33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels. The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count. Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.

Lewy body co‐pathology contributes to frontal lobe atrophy in Alzheimer’s disease and primary age‐related tauopathy

AbstractBackgroundAlzheimer’s disease (AD) is a heterogeneous proteinopathy. Co‐pathology with Lewy bodies (LB) is frequent and known to contribute to a differential pattern of executive cognitive impairment compared to typical AD. While primary age‐related tauopathy (PART) presents with neurofibrillary tangles, mainly restricted to the temporal lobe and in the absence of amyloid‐beta plaques, LB co‐pathology is also present. However, it is still unknown how the combination of these proteinopathies affects patterns of brain atrophy, which might help clarify the clinical importance and pathophysiology of combined pathology within these groups. Here we investigated the influence of LB on patterns of brain atrophy in AD and PART.MethodsWe selected 214 patients with AD neuropathological change (ADNC) with no LB (n = 102), ADNC with LB (ADNC+LB) (n = 77), PART with no LB (n = 22) and PART with LB (PART+LB) (n = 13) with ante‐mortem volumetric MRI from the National Alzheimer’s Coordinating Center, after exclusion of other neurodegenerative pathologies. Dementia severity was compared using Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB). Cortical and subcortical MRI volume residuals were compared between groups after correction for age. Statistical significance was considered at p‐value &lt; 0.05 after false discovery rate correction for multiple comparisons.ResultsWe found that ADNC, ADNC+LB and PART+LB presented similar levels of dementia severity, whereas PART with no LB was the least affected group. MRI revealed higher atrophy of the right frontal operculum and amygdala in ADNC+LB compared to ADNC. ADNC and ADNC+LB showed increased atrophy in regions across all cortical lobes, hippocampus, amygdala and putamen compared to PART, but did not reveal significant differences compared to PART+LB. Interestingly, PART+LB showed higher atrophy of the frontal poles bilaterally, right superior frontal gyrus, left frontal operculum and right putamen compared to PART.ConclusionsAltogether, these results suggest that LB co‐pathology contributes to frontal lobe regional atrophy in ADNC and likely drives it in PART. Particularly in PART+LB, it suggests that cognitive impairment might derive from alpha‐synuclein pathology rather than tauopathy. These findings indicate that LB pathology determines disease severity within the PART‐ and AD‐continuums.

Lewy body co‐pathology contributes to frontal lobe atrophy in Alzheimer’s disease and primary age‐related tauopathy

AbstractBackgroundAlzheimer’s disease (AD) is a heterogeneous proteinopathy. Co‐pathology with Lewy bodies (LB) is frequent and known to contribute to a differential pattern of executive cognitive impairment compared to typical AD. While primary age‐related tauopathy (PART) presents with neurofibrillary tangles, mainly restricted to the temporal lobe and in the absence of amyloid‐beta plaques, LB co‐pathology is also present. However, it is still unknown how the combination of these proteinopathies affects patterns of brain atrophy, which might help clarify the clinical importance and pathophysiology of combined pathology within these groups. Here we investigated the influence of LB on patterns of brain atrophy in AD and PART.MethodsWe selected 214 patients with AD neuropathological change (ADNC) with no LB (n = 102), ADNC with LB (ADNC+LB) (n = 77), PART with no LB (n = 22) and PART with LB (PART+LB) (n = 13) with ante‐mortem volumetric MRI from the National Alzheimer’s Coordinating Center, after exclusion of other neurodegenerative pathologies. Dementia severity was compared using Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB). Cortical and subcortical MRI volume residuals were compared between groups after correction for age. Statistical significance was considered at p‐value &lt; 0.05 after false discovery rate correction for multiple comparisons.ResultsWe found that ADNC, ADNC+LB and PART+LB presented similar levels of dementia severity, whereas PART with no LB was the least affected group. MRI revealed higher atrophy of the right frontal operculum and amygdala in ADNC+LB compared to ADNC. ADNC and ADNC+LB showed increased atrophy in regions across all cortical lobes, hippocampus, amygdala and putamen compared to PART, but did not reveal significant differences compared to PART+LB. Interestingly, PART+LB showed higher atrophy of the frontal poles bilaterally, right superior frontal gyrus, left frontal operculum and right putamen compared to PART.ConclusionsAltogether, these results suggest that LB co‐pathology contributes to frontal lobe regional atrophy in ADNC and likely drives it in PART. Particularly in PART+LB, it suggests that cognitive impairment might derive from alpha‐synuclein pathology rather than tauopathy. These findings indicate that LB pathology determines disease severity within the PART‐ and AD‐continuums.

Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease.

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.

Assessing motor cortex excitability and it's relationship with cortical atrophy in Alzheimer's dementia

Assessing motor cortex excitability and it's relationship with cortical atrophy in Alzheimer's dementia

A 6-items Questionnaire (6-QMD) captures a Mediterranean like dietary pattern and is associated with memory performance and hippocampal volume in elderly and persons at risk for Alzheimer’s disease

BACKGROUND: There is evidence that adherence to Mediterranean-like diet reduces cognitive decline and brain atrophy in Alzheimer's disease (AD). However, lengthy dietary assessments, such as food frequency questionnaires (FFQs), discourage more frequent use. OBJECTIVE: Here we aimed to validate a 6-items short questionnaire for a Mediterranean-like diet (6-QMD) and explore its associations with memory performance and hippocampal atrophy in healthy elders and individuals at risk for AD. METHODS: We analyzed 938 participants (N = 234 healthy controls and N = 704 participants with an increased AD risk) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE). The 6-QMD was validated against the Mediterranean Diet (MeDi) score and the Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) score, both derived from a detailed FFQ. Furthermore, associations between the 6-QMD and memory function as well as hippocampal atrophy were evaluated using linear regressions. RESULTS: The 6-QMD was moderately associated with the FFQ-derived MeDi adherence score (ρ = 0.25, p &lt; 0.001) and the MIND score (ρ = 0.37, p= &lt; 0.001). Higher fish and olive oil consumption and lower meat and sausage consumption showed significant associations in a linear regression, adjusted for diagnosis, age, sex and education, with memory function (β = 0.1, p = 0.008) and bilateral hippocampal volumes (left: β = 0.15, p &lt; 0.001); (right: β = 0.18, p &lt; 0.001)). CONCLUSIONS: The 6-QMD is a useful and valid brief tool to assess the adherence to MeDi and MIND diets, capturing associations with memory function and brain atrophy in healthy elders and individuals at increased AD dementia risk, making it a valid alternative in settings with time constraints.

Longitudinal associations between β-amyloid and cortical thickness in mild cognitive impairment.

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment. 45 patients with mild cognitive impairment and 12 controls underwent a cross-sectional [11C]-Pittsburgh Compound B PET and two retrospective longitudinal structural imaging (follow-up: 23.65 ± 2.04 months) to assess global/regional amyloid and regional cortical thickness, respectively. Separate linear mixed models were constructed to evaluate relationships of either global or regional amyloid with regional cortical thinning longitudinally. In patients with mild cognitive impairment, regional amyloid in the right banks of the superior temporal sulcus was associated with longitudinal cortical thinning in the right medial orbitofrontal cortex (P = 0.04 after False Discovery Rate correction). In the mild cognitive impairment group, greater right banks amyloid burden and less cortical thickness in the right medial orbitofrontal cortex showed greater visual and verbal memory decline over time, which was not observed in controls. Global amyloid was not associated with longitudinal cortical thinning in any locations in either group. Our findings indicate an increasing influence of amyloid on neurodegeneration and memory along the preclinical to prodromal spectrum. Future multimodal studies that include additional biomarkers will be well-suited to delineate the interplay between various pathological processes and amyloid and memory decline, as well as clarify their additive or independent effects along the disease deterioration.

Impact of sex and APOE-ε4 genotype on patterns of regional brain atrophy in Alzheimer's disease and healthy aging.

Alzheimer's Disease (AD) is a heterogeneous disease that disproportionately affects women and people with the APOE-ε4 susceptibility gene. We aim to describe the not-well-understood influence of both risk factors on the dynamics of brain atrophy in AD and healthy aging. Regional cortical thinning and brain atrophy were modeled over time using non-linear mixed-effect models and the FreeSurfer software with t1-MRI scans from the Alzheimer's Disease Neuroimaging Initiative (N = 1,502 subjects, 6,728 images in total). Covariance analysis was used to disentangle the effect of sex and APOE genotype on the regional onset age and pace of atrophy, while correcting for educational level. A map of the regions mostly affected by neurodegeneration is provided. Results were confirmed on gray matter density data from the SPM software. Women experience faster atrophic rates in the temporal, frontal, parietal lobes and limbic system and earlier onset in the amygdalas, but slightly later onset in the postcentral and cingulate gyri as well as all regions of the basal ganglia and thalamus. APOE-ε4 genotypes leads to earlier and faster atrophy in the temporal, frontal, parietal lobes, and limbic system in AD patients, but not in healthy patients. Higher education was found to slightly delay atrophy in healthy patients, but not for AD patients. A cohort of amyloid positive patients with MCI showed a similar impact of sex as in the healthy cohort, while APOE-ε4 showed similar associations as in the AD cohort. Female sex is as strong a risk factor for AD as APOE-ε4 genotype regarding neurodegeneration. Women experience a sharper atrophy in the later stages of the disease, although not a significantly earlier onset. These findings may have important implications for the development of targeted intervention.

Lateral temporal atrophy is a better visual scale to predict MMSE score than hippocampal atrophy in Alzheimer’s disease: A retrospective cross‐sectional study

AbstractBackgroundMini‐Mental State Exam (MMSE) is helpful in the cognitive evaluation and staging of Alzheimer patients (AD), but its score could be affected by acute medical conditions, depression, or anxiety. Conversely, head magnetic resonance imaging (MRI) is an independent diagnostic tool to evaluate specific cerebral pathology in AD patients. Many studies have investigated the correlation between head MRI visual scales such as hippocampal atrophy and baseline MMSE to aid in the diagnosis of AD. However, there has not been a study that evaluates the correlation between a comprehensive head MRI visual scale with baseline MMSE in AD patients.MethodWe retrospectively collected records of outpatients diagnosed as probable AD according to DSM V criteria in two dementia wards. Patients with age of onset before 65 years old were considered early‐onset AD patients. The comprehensive visual rating scale (CVRS) was used to semiquantitative structural lesions, including hippocampal, cortical, subcortical atrophy, and small vessel disease. The relationship between MRI lesions and baseline MMSE was evaluated using Bayesian model averaging (BMA) to control confounders. Data analysis was done using R‐4.0.3.ResultWe collected 65 AD patients with 21 (32.31%) early‐onset AD. Lateral temporal atrophy, level of education, and late age‐onset were the strongest independent predictors of baseline MMSE, while other structural lesions were not significant in the BMA model. Furthermore, hippocampal atrophy was only correlated with delayed recall, while temporal atrophy was correlated with orientations, attentions, language, and visual‐spatial.ConclusionOur study suggests that the lateral temporal atrophy correlates more with the baseline MMSE score in patients with AD than the hippocampal atrophy.

Model-based whole-brain perturbational landscape of neurodegenerative diseases.

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Transferability of Alzheimer's disease progression subtypes to an independent population cohort

In the past, methods to subtype or biotype patients using brain imaging data have been developed. However, it is unclear whether and how these trained machine learning models can be successfully applied to population cohorts to study the genetic and lifestyle factors underpinning these subtypes. This work, using the Subtype and Stage Inference (SuStaIn) algorithm, examines the generalisability of data-driven Alzheimer's disease (AD) progression models.We first compared SuStaIn models trained separately on Alzheimer's disease neuroimaging initiative (ADNI) data and an AD-at-risk population constructed from the UK Biobank dataset. We further applied data harmonization techniques to remove cohort effects. Next, we built SuStaIn models on the harmonized datasets, which were then used to subtype and stage subjects in the other harmonized dataset.The first key finding is that three consistent atrophy subtypes were found in both datasets, which match the previously identified subtype progression patterns in AD: ‘typical’, ‘cortical’ and ‘subcortical’. Next, the subtype agreement was further supported by high consistency in individuals’ subtypes and stage assignment based on the different models: more than 92% of the subjects, with reliable subtype assignment in both ADNI and UK Biobank dataset, were assigned to an identical subtype under the model built on the different datasets. The successful transferability of AD atrophy progression subtypes across cohorts capturing different phases of disease development enabled further investigations of associations between AD atrophy subtypes and risk factors. Our study showed that (1) the average age is highest in the typical subtype and lowest in the subcortical subtype; (2) the typical subtype is associated with statistically more-AD-like cerebrospinal fluid biomarkers values in comparison to the other two subtypes; and (3) in comparison to the subcortical subtype, the cortical subtype subjects are more likely to associate with prescription of cholesterol and high blood pressure medications.In summary, we presented cross-cohort consistent recovery of AD atrophy subtypes, showing how the same subtypes arise even in cohorts capturing substantially different disease phases. Our study opened opportunities for future detailed investigations of atrophy subtypes with a broad range of early risk factors, which will potentially lead to a better understanding of the disease aetiology and the role of lifestyle and behaviour on AD.

Microglia-mediated T cell infiltration drives neurodegeneration in tauopathy.

Extracellular deposition of amyloid-β as neuritic plaques and intracellular accumulation of hyperphosphorylated, aggregated tau as neurofibrillary tangles are two of the characteristic hallmarks of Alzheimer's disease1,2. The regional progression of brain atrophy in Alzheimer's disease highly correlates with tau accumulation but not amyloid deposition3-5, and the mechanisms of tau-mediated neurodegeneration remain elusive. Innate immune responses represent a common pathway for the initiation and progression of some neurodegenerative diseases. So far, little is known about the extent or role of the adaptive immune response and its interaction with the innate immune response in the presence of amyloid-β or tau pathology6. Here we systematically compared the immunological milieux in the brain of mice with amyloid deposition or tau aggregation and neurodegeneration. We found that mice with tauopathy but not those with amyloid deposition developed a unique innate and adaptive immune response and that depletion of microglia or T cells blocked tau-mediated neurodegeneration. Numbers of T cells, especially those of cytotoxic T cells, were markedly increased in areas with tau pathology in mice with tauopathy and in the Alzheimer's disease brain. T cell numbers correlated with the extent of neuronal loss, and the cells dynamically transformed their cellular characteristics from activated to exhausted states along with unique TCR clonal expansion. Inhibition of interferon-γ and PDCD1 signalling both significantly ameliorated brain atrophy. Our results thus reveal a tauopathy- and neurodegeneration-related immune hub involving activated microglia and T cell responses, which could serve as therapeutic targets for preventing neurodegeneration in Alzheimer's disease and primary tauopathies.

Structural and functional differences in the brains of patients with MCI with and without depressive symptoms and their relations with Alzheimer's disease: an MRI study.

The mild cognitive impairment (MCI) stage among elderly individuals is very complex, and the level of diagnostic accuracy is far from ideal. Some studies have tried to improve the 'MCI due to Alzheimer's disease (AD)' classification by further stratifying these patients into subgroups. Depression-related symptoms may play an important role in helping to better define the MCI stage in elderly individuals. In this work, we explored functional and structural differences in the brains of patients with nondepressed MCI (nDMCI) and patients with MCI with depressive symptoms (DMCI), and we examined how these groups relate to AD atrophy patterns and cognitive functioning. Sixty-five participants underwent MRI exams and were divided into four groups: cognitively normal, nDMCI, DMCI, and AD. We compared the regional brain volumes, cortical thickness, and white matter microstructure measures using diffusion tensor imaging among groups. Additionally, we evaluated changes in functional connectivity using fMRI data. In comparison to the nDMCI group, the DMCI patients had more pronounced atrophy in the hippocampus and amygdala. Additionally, DMCI patients had asymmetric damage in the limbic-frontal white matter connection. Furthermore, two medial posterior regions, the isthmus of cingulate gyrus and especially the lingual gyrus, had high importance in the structural and functional differentiation between the two groups. It is possible to differentiate nDMCI from DMCI patients using MRI techniques, which may contribute to a better characterization of subtypes of the MCI stage.

Associations between different tau-PET patterns and longitudinal atrophy in the Alzheimer’s disease continuum: biological and methodological perspectives from disease heterogeneity

BackgroundSubtypes and patterns are defined using tau-PET (tau pathology) and structural MRI (atrophy) in Alzheimer’s disease (AD). However, the relationship between tau pathology and atrophy across these subtypes/patterns remains unclear. Therefore, we investigated the biological association between baseline tau-PET patterns and longitudinal atrophy in the AD continuum; and the methodological characterization of heterogeneity as a continuous phenomenon over the conventional discrete subgrouping.MethodsIn 366 individuals (amyloid-beta-positive cognitively normal, prodromal AD, AD dementia; amyloid-beta-negative cognitively normal), we examined the association between tau-PET patterns and longitudinal MRI. We modeled tau-PET patterns as a (a) continuous phenomenon with key dimensions: typicality and severity; and (b) discrete phenomenon by categorization into patterns: typical, limbic predominant, cortical predominant and minimal tau. Tau-PET patterns and associated longitudinal atrophy were contextualized within the Amyloid/Tau/Neurodegeneration (A/T/N) biomarker scheme.ResultsLocalization and longitudinal atrophy change vary differentially across different tau-PET patterns in the AD continuum. Atrophy, a downstream event, did not always follow a topography akin to the corresponding tau-PET pattern. Further, heterogeneity as a continuous phenomenon offered an alternative and useful characterization, sharing correspondence with the conventional subgrouping. Tau-PET patterns also show differential A/T/N profiles.ConclusionsThe site and rate of atrophy are different across the tau-PET patterns. Heterogeneity should be treated as a continuous, not discrete, phenomenon for greater sensitivity. Pattern-specific A/T/N profiles highlight differential multimodal interactions underlying heterogeneity. Therefore, tracking multimodal interactions among biomarkers longitudinally, modeling disease heterogeneity as a continuous phenomenon, and examining heterogeneity across the AD continuum could offer avenues for precision medicine.

Personalized Patch-based Normality Assessment of Brain Atrophy in Alzheimer's Disease.

Cortical thickness is an important biomarker associated with gray matter atrophy in neurodegenerative diseases. In order to conduct meaningful comparisons of cortical thickness between different subjects, it is imperative to establish correspondence among surface meshes. Conventional methods achieve this by projecting surface onto canonical domains such as the unit sphere or averaging feature values in anatomical regions of interest (ROIs). However, due to the natural variability in cortical topography, perfect anatomically meaningful one-to-one mapping can be hardly achieved and the practice of averaging leads to the loss of detailed information. For example, two subjects may have different number of gyral structures in the same region, and thus mapping can result in gyral/sulcal mismatch which introduces noise and averaging in detailed local information loss. Therefore, it is necessary to develop new method that can overcome these intrinsic problems to construct more meaningful comparison for atrophy detection. To address these limitations, we propose a novel personalized patch-based method to improve cortical thickness comparison across subjects. Our model segments the brain surface into patches based on gyral and sulcal structures to reduce mismatches in mapping method while still preserving detailed topological information which is potentially discarded in averaging. Moreover,the personalized templates for each patch account for the variability of folding patterns, as not all subjects are comparable. Finally, through normality assessment experiments, we demonstrate that our model performs better than standard spherical registration in detecting atrophy in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).