Distinct Atrophy Patterns Research Articles

Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn).

Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.

What do data‐driven Alzheimer’s disease subtypes tell us about white matter pathology and clinical progression?

AbstractBackgroundThe progression of Alzheimer’s Disease (AD) is heterogeneous with different brain regions manifesting pathology at different times during the disease. Differential sequences of grey matter (GM) volume changes, defining imaging‐based subtypes, may represent alternative disease origins, pathways or severity. Additionally, volumes of white matter hyperintensities (WMH), which increase with age, are associated with AD. In this study we used a machine learning method to estimate three types of AD trajectories and assessed their WMH load and longitudinal prognosis.MethodSubtype and Stage Inference (SuStaIn; Young et al., https://doi.org/10.1038/s41467‐018‐05892‐0) was used to estimate data‐driven disease progresion subtypes from cross‐sectional GM volumes in 795 subjects (from ADNI1/GO/2 with baseline 3T MRI scans processed with FreeSurfer 5.1 passing data quality control). Following additional quality control WMH volumes for 575 of these ADNI subjects were calculated using a Bayesian Model Selection method BaMoS (Sudre et al., https://doi.org/10.1109/TMI.2015.2419072) from three diagnostic groups: Healthy Controls (HC; n=181), Mild Cognitive Impairment (MCI; n=310) and Alzheimer’s Disease (AD; n=85). The association between SuStaIn subtypes and WMH and diagnostic conversion rates were evaluated using multiple pairwise comparisons and survival analysis, respectively.ResultSuStaIn identified three subtypes with distinct atrophy patterns: typical (atrophy begins in hippocampus and amygdala), cortical (atrophy starts in the temporal lobe, followed by cingulate and insula) and subcortical (atrophy begins in pallidum, putamen and caudate) (Figure 1). Participants with the subcortical subtype exhibited a significantly slower rate of clinical progression to MCI or AD (Figure 2, Table 1). Furthemore, subjects with the subcortical subtype showed lower median WMH (Figure 3a), a pattern observed across the diagnostic groups (Figure 3b).ConclusionParticipants classified as the subcortical subtype were least likely to have white matter structural changes associated with neurodegeneration and displayed the slowest rate of clinical conversion. While late hippocampal involvement in the subcortical subtype may explain less severe clinical symptoms and slower disease progression, the reason for lower WMH associated with this subtype trajectory is unknown. Further studies are needed to investigate the link between WMH and AD atrophy subtypes.

Investigating white matter connectomes in amnestic and non‐amnestic Alzheimer's disease clinical variants

AbstractBackgroundRecent models propose that spread of Alzheimer's disease (AD) pathology may be mediated by white matter connectivity. AD clinical variants exhibit partially distinct patterns of gray matter atrophy. However, it is less clear whether phenotypic differences extend to patients' white matter connectomes. We hypothesized that white matter connectivity would differ between healthy controls, amnestic AD (aAD), and non‐amnestic AD (naAD), including behavioral variant Alzheimer's disease (bvAD), logopenic‐variant primary progressive aphasia (lvPPA), and posterior cortical atrophy (PCA).MethodWe examined white matter connectomes in 194 participants (controls=60, aAD=42, bvAD=25, lvPPA=39, PCA=28). All participants with dementia had evidence of AD pathologic change based on autopsy, Aβ1–42 from cerebrospinal fluid, or [18]F‐florbetaben positron emission tomography. T1‐weighted and diffusion tensor images were collected using a 3 Tesla scanner. We calculated gray matter volume for cortical regions of interest and median fractional anisotropy (FA) for the tract connecting every pair of regions. Tracts with the most variable FA values across participants were excluded to yield graphs with 30% density. Linear regressions adjusted for sex and age were used to investigate group differences in degree and betweenness centrality as well as the relationship between gray matter volume and FA measures. Phenotypic differences in tractwise FA were evaluated using t‐tests.ResultBoth node and edge metrics indicated significant (p<.05, corrected) widespread differences between patient groups and controls. In contrasts between disease groups, aAD patients had reduced degree in multiple occipital and inferior temporal regions; and higher betweenness centrality in bilateral frontal and parietal regions relative to naAD phenotypes. Tractwise FA values did not differ between patient groups. Across groups, endpoint gray matter volume was correlated with tractwise FA measures.ConclusionBoth node‐ and edge‐based metrics suggested patients’ white matter connectomes exhibited degeneration relative to controls. Phenotypic differences between patient populations were few, suggesting that AD clinical variants with distinct patterns of gray matter disease nevertheless had broadly similar patterns of white matter degeneration. Gray matter atrophy was closely associated with lower values of FA of white matter tracts connecting atrophied regions, consistent with Wallerian degeneration of white matter.

Visuospatial impairment in logopenic progressive aphasia: Why do we not “see” it?

AbstractBackgroundLogopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA) have distinct patterns of hypometabolism and atrophy. However, these clinical syndromes overlap: visuospatial impairment has been documented in LPA and aphasia in PCA. Rey‐Osterrieth Complex Figure – Copy (Rey‐O Copy) performance is correlated with parietal‐occipital atrophy in LPA. We explore visuospatial impairment on the Rey‐O Copy in LPA and PCA with the Mayo Older Americans Normative Studies (MOANS; which uses Meyers & Meyers criteria) and the Boston Qualitative Scoring System (BQSS). We also examine the proportion of visuospatial impairment in LPA, an under‐appreciated phenomenon, compared to PCA wherein it is fundamental.MethodPatients with LPA (n=25) or PCA (n=23) were recruited by the Neurodegenerative Research Group. Patients underwent comprehensive neuropsychological testing (including Rey‐O Copy) at baseline visit. Cut‐offs of T‐score=40 (BQSS) or scaled score=7 (MOANS) were used based on normative data. Proportions of patients with low performance were examined across LPA and PCA groups. Individual logistic regressions and AUROC analyses were then conducted to quantify differences in visuospatial impairment in LPA and PCA with both scoring systems. Identical analyses were conducted using raw scores corrected for age and sex, to avoid confounds of adjustments in normative data.ResultLPA and PCA groups were comparable on age, sex, and disease duration (LPA Median = 2.7 years, IQR=1.9‐3.5; PCA Median = 3.6 years, IQR=1.5‐4.9); p=.823). Surprisingly, 52‐72% of LPA participants demonstrated impaired performance across scoring systems, relative to 96% of the PCA group identified by both scoring systems. Odds ratios (OR) for the Rey‐O Copy MOANS (OR = 1.59; p=.008) and BQSS Copy Presence and Accuracy index (CPA; OR = 1.13, p=.002) were significant, persisting for age‐ and sex‐ controlled raw scores, suggesting the odds of a PCA diagnosis increased with decrement on Rey‐O Copy. AUROC values ranged from 0.77 to 0.88 across these indices.ConclusionA substantial number of LPA participants showed visuospatial impairment, despite initial primary language complaints. Differentiation of diagnostic groups by the Rey‐O is likely underscored by relatively uniform, profound, visuospatial impairment in PCA with more variability in LPA. However, the degree of visuospatial impairment overlaps remarkably across diagnoses.

Dementia with Lewy bodies subtypes identified by cluster analysis on structural MRI

AbstractBackgroundDementia with Lewy bodies (DLB) is a neurodegenerative disorder that presents with a variety of clinical symptoms. Part of the clinical heterogeneity within DLB has been related to the atrophy/sparing of the hippocampus. We extended that previous research by investigating whether different atrophy patterns exist in DLB, beyond and above the atrophy/sparing of the hippocampus, and whether those patterns contribute to the clinical heterogeneity in DLB. We aimed to identify DLB atrophy subtypes using a hypothesis‐free data‐driven clustering approach based on regional gray matter (GM) volumes and characterize the resulting subtypes across key measures.MethodWe included data from three centers of the European DLB consortium. A high‐resolution 3D T1‐weigthed magnetization prepared rapid gradient echo (MPRAGE) sequence was acquired for 97 DLB patients. Eighty‐four cortical regions and 12 subcortical regions from Mayo Clinic Adult Lifespan Template (https://www.nitrc.org/projects/mcalt/) atlases were obtained. Clusters of DLB patients were then determined using the random forest method. Between‐group differences in key demographic, clinical, MRI volumes and cerebrospinal fluid biomarkers (CSF) (amyloid‐beta, phosphorylated‐tau) were performed for clusters characterization.ResultWe identified three subtypes of DLB patients, with distinct patterns of GM volumes and differences in several clinical and demographic measures. Cluster 1 (C1) included 32 DLB patients (33%), cluster 2 (C2) included 29 DLB patients (30%), and cluster 3 (C3) included 36 DLB patients (37%). The clusters differed in age, with C1 being significantly older (72±8.6) than C3 (66±9.5) (F=3.93, p=0.02). C1 was characterized by lower volumes in fronto‐temporal regions and had a higher frequency of visual hallucinations (68%, vs. C2 35%). C2 showed lower volumes in occipital regions. C3 had higher GM volumes overall compared with the other two clusters and presented with a higher frequency of cognitive fluctuations (91%, vs. C1 67%). There were no differences between groups in CSF biomarkers. These results were independent of cluster differences in age and global GM volume.ConclusionThere are distinct patterns of brain atrophy in patients with DLB. These patterns seem to differentially contribute to the frequencies of visual hallucinations and cognitive fluctuations, independently of differences in age and global GM volume across subtypes.

Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease.

Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology. Cortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups. The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected. These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.

Characterizing the Clinical Features and Atrophy Patterns of MAPT-Related Frontotemporal Dementia With Disease Progression Modeling.

Background and ObjectiveMutations in the MAPT gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signature of MAPT mutations have grouped all different mutations together and shown an association with focal atrophy of the temporal lobe. The variability in atrophy patterns between each particular MAPT mutation is less well-characterized. We aimed to investigate whether there were distinct groups of MAPT mutation carriers based on their neuroanatomical signature.MethodsWe applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning technique that identifies groups of individuals with distinct progression patterns, to characterize patterns of regional atrophy in MAPT-associated FTD within the Genetic FTD Initiative (GENFI) cohort study.ResultsEighty-two MAPT mutation carriers were analyzed, the majority of whom had P301L, IVS10+16, or R406W mutations, along with 48 healthy noncarriers. SuStaIn identified 2 groups of MAPT mutation carriers with distinct atrophy patterns: a temporal subtype, in which atrophy was most prominent in the hippocampus, amygdala, temporal cortex, and insula; and a frontotemporal subtype, in which atrophy was more localized to the lateral temporal lobe and anterior insula, as well as the orbitofrontal and ventromedial prefrontal cortex and anterior cingulate. There was one-to-one mapping between IVS10+16 and R406W mutations and the temporal subtype and near one-to-one mapping between P301L mutations and the frontotemporal subtype. There were differences in clinical symptoms and neuropsychological test scores between subtypes: the temporal subtype was associated with amnestic symptoms, whereas the frontotemporal subtype was associated with executive dysfunction.ConclusionOur results demonstrate that different MAPT mutations give rise to distinct atrophy patterns and clinical phenotype, providing insights into the underlying disease biology and potential utility for patient stratification in therapeutic trials.

Frontotemporal dementia subtypes based on behavioral inhibition deficits.

IntroductionWe aimed to investigate phenotypic heterogeneity in the behavioral variant of frontotemporal dementia (bvFTD) through assessment of inhibition deficits.MethodsWe assessed occurrences of 16 behavioral inhibition deficits from video recordings of 15 bvFTD patients (early stage) and 15 healthy controls (HC) in an ecological setting. We extracted dimensions of inhibition deficit and analyzed their correlations with cognitive and clinical measures. Using these dimensions, we isolated patient clusters whose atrophy patterns were explored.ResultsAfter identifying two patterns of inhibition deficit (compulsive automatic behaviors and socially unconventional behaviors), we isolated three behavioral clusters with distinct atrophy patterns. BvFTD‐G0 (N = 3), an outlier group, showed severe behavioral disturbances and more severe ventromedial prefrontal cortex/orbitofrontal cortex atrophy. Compared to bvFTD‐G1 (N = 6), bvFTD‐G2 (N = 6) presented higher anxiety and depression along with less diffuse atrophy especially in midline regions.DiscussionIdentifying clinico‐anatomical profiles through behavior observation could help to stratify bvFTD patients for adapted treatments.

Distinct Patterns of Brain Atrophy associated with Mild Behavioral Impairment in Cognitively Normal Elderly Adults.

A cross-sectional study was conducted to evaluate patterns of gray matter changes in cognitively normal elderly adults with mild behavioral impairment (MBI). Sixteen MBI patients and 18 healthy controls were selected. All the participants underwent a neuropsychological assessment battery, including the Mini-mental State Examination (MMSE), Geriatric Depression Scale (GDS), Self-rating Anxiety Scale (SAS), and Chinese version of the mild behavioral impairment-checklist scale (MBI-C), and magnetic resonance imaging (MRI) scans. Imaging data was analyzed based on voxel-based morphometry (VBM). There was no significant difference in age, gender, MMSE score, total intracranial volume, white matter hyperdensity, gray matter volume, white matter volume between the two groups (p > 0.05). MBI group had shorter education years and higher MBI-C score, GDS and SAS scores than the normal control group (p < 0.05). For neuroimaging analysis, compared to the normal control group, the MBI group showed decreased volume in the left brainstem, right temporal transverse gyrus, left superior temporal gyrus, left inferior temporal gyrus, left middle temporal gyrus, right occipital pole, right thalamus, left precentral gyrus and left middle frontal gyrus(uncorrected p < 0.001). The grey matter regions correlated with the MBI-C score included the left postcentral gyrus, right exterior cerebellum, and left superior frontal gyrus. This suggests a link between MBI and decreased grey matter volume in cognitively normal elderly adults. Atrophy in the left frontal cortex and right thalamus in MBI patients is in line with frontal-subcortical circuit deficits, which have been linked to neuropsychiatric symptoms (NPS) in dementia. These initial results imply that MBI might be an early harbinger for subsequent cognitive decline and dementia.

Brain atrophy trajectories predict differential functional performance in Alzheimer's disease: Moderations with apolipoprotein E and sex.

IntroductionWe examine whether distinct brain atrophy patterns (using brain parenchymal fraction [BPF]) differentially predict functional performance and decline in Alzheimer's disease (AD), and are independently moderated by (1) a key AD genetic risk marker (apolipoprotein E [APOE]), (2) sex, and (3) high‐risk group (women APOE ɛ4 carriers).MethodsWe used a 2‐year longitudinal sample of AD patients (baseline N = 170; mean age = 71.3 [9.1] years) from the Sunnybrook Dementia Study. We applied latent class analysis, latent growth modeling, and path analysis. We aimed to replicate our findings (N = 184) in the Alzheimer's Disease Neuroimaging Initiative.ResultsWe observed that high brain atrophy class predicted lower functional performance and steeper decline. This association was moderated by APOE, sex, and high‐risk group. Baseline findings as moderated by APOE and high‐risk group were replicated.DiscussionWomen APOE ɛ4 carriers may selectively be at a greater risk of functional impairment with higher brain atrophy.

Characterising the spatiotemporal heterogeneity of neurodegenerative diseases using subtype and stage inference

AbstractBackgroundNeurodegenerative diseases are highly heterogeneous, consisting of multiple disease subtypes with different spatiotemporal patterns of pathology. These patterns evolve dynamically with disease stage, making it challenging to disentangle disease subtypes in vivo from biomarker data where precise staging information is unavailable.MethodSubtype and Stage Inference (SuStaIn) is an unsupervised learning technique that disentangles biomarker heterogeneity into subgroups of individuals (subtypes) with distinct progression patterns (stages). SuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence in which biomarkers transition between different z‐scores. We used SuStaIn in a number of conditions to identify subgroups of individuals with distinct patterns of brain volume loss using structural MRI data. We present results from several datasets, reviewing results in Alzheimer’s disease (ADNI dataset), and presenting new results in genetic frontotemporal dementia (GENFI dataset), and in the aging population (UK Biobank). In each dataset we explored genetic, neuropsychological, clinical and biomarker associations with each of the subtypes identified by SuStaIn. We used longitudinal data to assess the consistency of the subtypes and stages assigned by the SuStaIn model at follow‐up visits.ResultIn Alzheimer’s disease we identified multiple spatiotemporal patterns of neurodegeneration associated with differences in memory and executive function scores. We found a genetic link between a ‘limbic‐predominant’ atrophy pattern and type 2 diabetes through a GWAS and analysis of polygenic risk scores. In genetic frontotemporal dementia we identified and confirmed two distinct atrophy patterns amongst C9orf72 mutation carriers (N=182). In MAPT mutation carriers (N=82), we identified two atrophy subtypes that were strongly associated with different MAPT mutations: a ‘temporal’ subtype, which had a one‐to‐one mapping with IVS10+16 and R406W mutations, and a ‘frontotemporal’ subtype, which had a near one‐to‐one mapping with P301L mutations. The subtype assignments showed strong stability at follow‐up visits (99% consistency). In the UK Biobank (N=21390), we identified multiple patterns of brain volume loss associated with distinct characteristics.ConclusionOur results demonstrate the utility of SuStaIn for identifying disease subgroups and associating imaging patterns with genetics and cognition. We show that SuStaIn can provide enhanced patient stratification capability across multiple conditions.

Distinct patterns of gray matter atrophy in hippocampal sclerosis of aging and Alzheimer’s disease neuropathology

AbstractBackgroundHippocampal sclerosis of aging (HS) and Alzheimer’s Disease Neuropathology (ADNP) both contribute to dementia in the elderly but are difficult to distinguish in vivo. Currently, there are no established biomarkers for HS, and few studies have investigated the distinction between HS‐ and ADNP‐related patterns of brain atrophy. We hypothesized that HS‐related atrophy would be restricted to the hippocampus and surrounding structures, while ADNP‐related atrophy would be most pronounced in the lateral temporal and parietal lobes.MethodWe used data from the National Alzheimer’s Coordinating Center (NACC) (09/2005‐03/2017) and identified 134 participants with suitable 3D T1w MRI and autopsy data. HS pathology was available as present/absent and we defined ADNP as present by a combination of high Braak stage (V/VI) and CERAD neuritic plaque score (3). We used the Computational Anatomy Toolbox (CAT12) to segment the MRI scans into tissue types and generate gray matter density maps. We examined the following regions of interest (ROIs): hippocampus, parahippocampus, amygdala, temporal and parietal regions, and global tissues measures. Additionally, we performed a voxel‐based morphometry (VBM) analysis. We studied the relationship between volumes/densities and neuropathologies using multiple linear regression models while adjusting for sex, age, education, dementia status, years from MRI to death, and intracranial volume.Result Table 1 depicts participant characteristics. We found that HS, but not ADNP, was associated with the volumes of the hippocampus (HS P&lt;0.001, ADNP P=0.4), parahippocampus (anterior only, HS P=0.007, ADNP P=0.8), and amygdala (HS P=0.009, ADNP=0.2, Table 2). ADNP was significantly associated with lateral temporal lobe volumes, most strongly the inferior/middle temporal gyri. We found a trend of association between ADNP and gray matter atrophy (P=0.051). HS was significantly associated with white matter atrophy (P=0.001). We display the VBM results in Figure 1, which largely confirm the ROI results. Additionally, we found HS was associated with bilateral thalamic and insular regions and ADNP with the left amygdala and hippocampal tail atrophy.ConclusionWhile HS is strongly associated with atrophy of the hippocampus and surrounding structures, ADNP is associated with atrophy of lateral temporal lobe regions. These diverging atrophy patterns are potential biomarkers for differentiating these two clinically indistinguishable conditions during life.

Brain atrophy patterns in patients with frontotemporal dementia: voxel-based morphometry

Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by language and behaviour deficits, which is considered the second most common cause of early-onset dementia. Detection of brain atrophy patterns is important for FTD diagnosis. However, the visual assessment of magnetic resonance imaging data may not be sensitive enough requiring the use of objective gray matter (GM) volume determination method. The study was aimed to assess the GM atrophy pattern in patients with FTD compared to control group patients using voxel-based morphometry (VBM). The study included 16 patients with FTD (12 patients with nonfluent agrammatic variant primary progressive aphasia (nfvPPA), three patients with behavioral variant of FTD, and one patient with logopenic variant PPA) and 10 healthy volunteers. VBM of patients with FTD and healthy controls revealed three significant (pFWE-corr &lt; 0.05) atrophy areas in the left inferior frontal, left fusiform, and left supramarginal gyri. Taking into account the predominance of patients with nfvPPA in the group of FTD patients, the additional VBM of this group and control group was carried out, which revealed a distinct atrophy pattern: the reduced GM volume was detected in the left inferior frontal and left middle frontal gyri (pFWE-corr &lt; 0.05). The results obtained indicate that regardless of the clinical variant, there is a certain atrophy pattern characteristic of FTD, which involves both frontotemporal areas and parietal lobe. The example of nfvPPA shows that each variant of the disease is associated with distinct localization of atrophy.

Subtype and stage inference identifies distinct atrophy patterns in genetic frontotemporal dementia that MAP onto specific MAPT mutations

AbstractBackgroundMutations in the MAPT gene are known to cause frontotemporal dementia (FTD), but there is heterogeneity in FTD phenotype across individuals. Here we used an unsupervised learning algorithm – Subtype and Stage Inference (SuStaIn) – to relate phenotypic heterogeneity to specific mutations in the MAPT gene.MethodSuStaIn evaluates the optimal grouping of individuals into disease subtypes, where each subtype consists of a sequence (set of stages) in which biomarkers transition between different z‐scores. We applied SuStaIn to cross‐sectional regional brain volumes extracted from T1‐weighted MRI data from MAPT carriers in the GENFI study to find the best stratification of the data into subtypes, and the temporal progression of each subtype. We used data from 82 MAPT carriers (57 presymptomatic and 25 symptomatic) to identify subtypes and data from a control group of 300 non‐carriers to derive z‐scores. We subtyped and staged individuals at up to five annual follow‐up visits to assess the consistency of the subtypes longitudinally. We compared the specific mutations and clinical and neuropsychological test scores of individuals assigned to each subtype.ResultSuStaIn identified two groups of MAPT carriers with distinct atrophy patterns (Figure 1), which we termed a ‘temporal’ subtype and a ‘frontotemporal’ subtype. The subtype assignments were consistent at follow‐up visits (Table 1): there were no individuals that changed from the temporal to the frontotemporal subtype or vice‐versa. Subtype assignment was strongly associated with IVS10+16, R406W and P301L mutations (Table 2): there was a one‐to‐one mapping between IVS10+16 and R406W mutations and the temporal subtype, and a near one‐to‐one mapping between P301L mutations and the frontotemporal subtype. The temporal subtype was associated with memory problems, whereas the frontotemporal subtype was associated with worse performance on tests of attention and visuospatial skills (Table 3).ConclusionOur results demonstrate the utility of SuStaIn for identifying disease subgroups and associating imaging patterns with genetics and cognition. We show that different MAPT mutations give rise to distinct atrophy patterns and clinical syndromes, providing insights into the underlying disease biology, and potential utility for patient stratification.

Distinct Atrophy Pattern of Hippocampal Subfields in Patients with Progressive and Stable Mild Cognitive Impairment: A Longitudinal MRI Study.

Predicting the prognosis of mild cognitive impairment (MCI) has outstanding clinical value, and the hippocampal volume is a reliable imaging biomarker of AD diagnosis. We aimed to longitudinally assess hippocampal sub-regional difference (volume and asymmetry) among progressive MCI (pMCI), stable MCI (sMCI) patients, and normal elderly. We identified 29 pMCI, 52 sMCI, and 102 normal controls (NC) from the ADNI database. All participants underwent neuropsychological assessment and 3T MRI scans three times. The time interval between consecutive MRI sessions was about 1 year. Volumes of hippocampal subfield were measured by Freesurfer. Based on the analysis of variance, repeated measures analyses, and receiver operating characteristic curves, we compared cross-sectional and longitudinal alteration sub-regional volume and asymmetry index. Compared to NC, both MCI groups showed significant atrophy in all subfields. At baseline, pMCI have a smaller volume than sMCI in the bilateral subiculum, molecular layer (ML), the molecular and granule cell layers of the dentate gyrus, cornu ammonis 4, and right tail. Furthermore, repeated measures analyses revealed that pMCI patients showed a faster volume loss than sMCI in bilateral subiculum and ML. After controlling for age, gender, and education, most results remained unchanged. However, none of the hippocampal sub-regional volumes performed better than the whole hippocampus in ROC analyses, and no asymmetric difference between pMCI and sMCI was found. The faster volume loss in subiculum and ML suggest a higher risk of disease progression in MCI patients. The hippocampal asymmetry may have smaller value in predicting the MCI prognosis.

Hierarchical cluster analysis of multimodal imaging data identifies brain atrophy and cognitive patterns in Parkinson’s disease

BackgroundParkinson's disease (PD) is a heterogeneous condition. Cluster analysis based on cortical thickness has been used to define distinct patterns of brain atrophy in PD. However, the potential of other neuroimaging modalities, such as white matter (WM) fractional anisotropy (FA), which has also been demonstrated to be altered in PD, has not been investigated. ObjectiveWe aim to characterize PD subtypes using a multimodal clustering approach based on cortical and subcortical gray matter (GM) volumes and FA measures. MethodsWe included T1-weighted and diffusion-weighted MRI data from 62 PD patients and 33 healthy controls. We extracted mean GM volumes from 48 cortical and 17 subcortical regions using FSL-VBM, and the mean FA from 20 WM tracts using Tract-Based Spatial Statistics (TBSS). Hierarchical cluster analysis was performed with the PD sample using Ward's linkage method. Whole-brain voxel-wise intergroup comparisons of VBM and TBSS data were also performed using FSL. Neuropsychological and demographic statistical analyses were conducted using IBM SPSS Statistics 25.0. ResultsWe identified three PD subtypes, with prominent differences in GM patterns and little WM involvement. One group (n = 15) with widespread cortical and subcortical GM volume and WM FA reductions and pronounced cognitive deficits; a second group (n = 21) with only cortical atrophy limited to frontal and temporal regions and more specific neuropsychological impairment, and a third group (n = 26) without detectable atrophy or cognition impairment. ConclusionMultimodal MRI data allows classifying PD patients into groups according to GM and WM patterns, which in turn are associated with the cognitive profile.

Towards individualized cortical thickness assessment for clinical routine

BackgroundCortical thickness measures the width of gray matter of the human cortex. It can be calculated from T1-weighted magnetic resonance images (MRI). In group studies, this measure has been shown to correlate with the diagnosis/prognosis of a number of neurologic and psychiatric conditions, but has not been widely adapted for clinical routine. One of the reasons for this might be that there is no reference system which allows to rate individual cortical thickness data with respect to a control population.MethodsTo address this problem, this study compared different methods to assess statistical significance of cortical thinning, i.e. atrophy. All compared methods were nonparametric and encompassed rating an individual subject’s data set with respect to a control data population. Null distributions were calculated using data from the Human Connectome Project (HCP, n = 1000), and an additional HCP data set (n = 113) was used to calculate sensitivity and specificity to compare the different methods, whereas atrophy was simulated for sensitivity assessment. Validation measures were calculated for the entire cortex (“cumulative”) and distinct brain regions (“regional”) where possible.ResultsThe approach yielding the highest combination of specificity and sensitivity implemented generating null distributions for anatomically distinct brain regions, based on the most extreme values observed in the population. With that method, while regional variations were observed, cumulative specificity of 98.9% and cumulative sensitivity at 80% was achieved for simulated atrophy of 23%.ConclusionsThis study shows that validated rating of individual cortical thickness measures is possible, which can help clinicians in their daily routine to discover signs of atrophy before they become visually apparent on an unprocessed MRI. Furthermore, given different pathologies present with distinct atrophy patterns, the regional validation proposed here allows to detect distinct patterns of atrophy, which can further enhance differential diagnosis/prognosis.

Impact of brain atrophy on tDCS and HD-tDCS current flow: a modeling study in three variants of primary progressive aphasia.

During transcranial direct current stimulation (tDCS), the amount and distribution of current that reaches the brain depends on individual anatomy. Many progressive neurodegenerative diseases are associated with cortical atrophy, but the importance of individual brain atrophy during tDCS in patients with progressive atrophy, including primary progressive aphasia (PPA), remains unclear. In the present study, we addressed the question whether brain anatomy in patients with distinct cortical atrophy patterns would impact brain current intensity and distribution during tDCS over the left IFG. We developed state-of-the-art, gyri-precise models of three subjects, each representing a variant of primary progressive aphasia: non-fluent variant PPA (nfvPPA), semantic variant PPA (svPPA), and logopenic variant PPA (lvPPA). We considered two exemplary montages over the left inferior frontal gyrus (IFG): a conventional pad montage (anode over F7, cathode over the right cheek) and a 4 × 1 high-definition tDCS montage. We further considered whether local anatomical features, specifically distance of the cortex to skull, can directly predict local electric field intensity. We found that the differences in brain current flow across the three PPA variants fall within the distribution of anatomically typical adults. While clustering of electric fields was often around individual gyri or sulci, the minimal distance from the gyri/sulci to skull was not correlated with electric field intensity. Limited to the conditions and assumptions considered here, this argues against a specific need to adjust the tDCS montage for these patients any more than might be considered useful in anatomically typical adults. Therefore, local atrophy does not, in isolation, reliably predict local electric field. Rather, our results are consistent with holistic head anatomy influencing brain current flow, with tDCS producing diffuse and individualized brain current flow patterns and HD-tDCS producing targeted brain current flow across individuals.

Normative brain volume reports may improve differential diagnosis of dementing neurodegenerative diseases in clinical practice.

Normative brain volume reports (NBVRs) are becoming more and more available for the workup of dementia patients in clinical routine. However, it is yet unknown how this information can be used in the radiological decision-making process. The present study investigates the diagnostic value of NBVRs for detection and differential diagnosis of distinct regional brain atrophy in several dementing neurodegenerative disorders. NBVRs were obtained for 81 consecutive patients with distinct dementing neurodegenerative diseases and 13 healthy controls (HC). Forty Alzheimer's disease (AD; 18 with dementia, 22 with mild cognitive impairment (MCI), 11 posterior cortical atrophy (PCA)), 20 frontotemporal dementia (FTD), and ten semantic dementia (SD) cases were analyzed, and reports were tested qualitatively for the representation of atrophy patterns. Gold standard diagnoses were based on the patients' clinical course, FDG-PET imaging, and/or cerebrospinal fluid (CSF) biomarkers following established diagnostic criteria. Diagnostic accuracy of pattern representations was calculated. NBVRs improved the correct identification of patients vs. healthy controls based on structural MRI for rater 1 (p < 0.001) whereas the amount of correct classifications was rather unchanged for rater 2. Correct differential diagnosis of dementing neurodegenerative disorders was significantly improved for both rater 1 (p = 0.001) and rater 2 (p = 0.022). Furthermore, interrater reliability was improved from moderate to excellent for both detection and differential diagnosis of neurodegenerative diseases (κ = 0.556/0.894 and κ = 0.403/0.850, respectively). NBVRs deliver valuable and observer-independent information, which can improve differential diagnosis of neurodegenerative diseases. • Normative brain volume reports increase detection of neurodegenerative atrophy patterns compared to visual reading alone. • Differential diagnosis of regionally distinct atrophy patterns is improved. • Agreement between radiologists is significantly improved from moderate to excellent when using normative brain volume reports.

Automated Brain MRI Volumetry Differentiates Early Stages of Alzheimer's Disease From Normal Aging.

As an enrichment strategy supplemented by the diagnostic framework of subjective cognitive decline (SCD), SCD plus identifies features that may increase the likelihood of including future-Alzheimer's disease (AD) patients. This study aimed to identify the shared and distinct atrophy patterns between patients specified by SCD plus and amnestic mild cognitive impairment (aMCI, a prodromal stage of AD) and to investigate the extent that automated brain magnetic resonance imaging (MRI) volumetry can differentiate patients with SCD from normal control (NC) participants and patients with aMCI. We acquired structural MRI brain scans from 44 patients with aMCI, 40 patients with SCD (who met the major criteria of SCD plus), and 48 NC participants. Automatic brain segmentation was performed to quantify the volumetric measures of cognitive-relevant areas. These volumetric measures were compared across the 3 groups with analysis of variance. In addition, we performed support vector machine analyses using volumetric measures of single regions or multiple regions to further evaluate the sensitivity of automated brain volumetry in differentiating a specific group from another. The atrophy patterns in patients with aMCI and SCD were similar. Using the regional volumetric measures, we achieved high performance in differentiating aMCI and SCD from NCs (average classification accuracy [ACC] > 90%). However, the performance was not ideal when differentiating aMCI from SCD (ACC < 63%). In conclusion, patients with SCD specified by SCD plus presented similar atrophy patterns as patients with aMCI, which was distinguishable from NC participants. Future studies should aim to associate the atrophy patterns of SCD with possible conversion to aMCI or AD in a longitudinal design.