bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of graphical markers for the differential diagnosis of primary progressive aphasia subtypes
Authors: Plonka Alexandra , Macoir Joel , Robert Philippe , Manera Valeria , Gros Auriane
Published: February 2022

Background

Primary Progressive Aphasia (PPA) brings together a group of neurodegenerative pathologies whose principal characteristic is to start with a progressive language disorder. Three main PPA subtypes were established, depending on the affected brain regions and the type of language disorder: the logopenic subtype (lvPPA), the non‐fluent/agrammatic subtype (nfavPPA) and the semantic subtype (svPPA) (Gorno‐Tempini et al., 2011). PPA diagnosis is often delayed in non‐specialised clinical settings. With the development of technologies, new diagnostic tools can be used, such as writing on a touch pad (Plonka et al., 2020). We have already highlighted differences between patients with typical Alzheimer's Disease (AD) and healthy controls (Gros et al., 2019), but the kinematic writing parameters are still understudied in the differential diagnosis of PPA subtypes.

Method

29 subjects with Primary Progressive Aphasia (lvPPA N= 18; nfavPPA N=6; svPPA N=5) were included in this study. They performed ten graphical markers tasks (2 non‐cognitive and non‐linguistic tasks, 4 cognitive and non‐linguistic tasks and 4 linguistic tasks) on an I‐Pad® tablet. Different writing parameters were extracted: writing pressure, velocity, jerk and stroke.

Result

Preliminary results revealed a main effect of diagnosis in linguistic tasks on average velocity (p= 0,041) and on average stroke (p= 0,029). The two‐by‐two comparison of different variants of PPA also showed significative differences: 1) lvPPA vs nfavPPA: significant differences in the average velocity, jerk and stroke were found, with lvPPA participants showing a higher velocity (p= 0,037), jerk (p= 0,046) and stroke (p= 0,014) than nfavPPA participants in linguistic tasks. 2) lvPPA vs svPPA: significant difference in velocity was found in linguistic task (p=0,037). 3) nfavPPA vs svPPA: significant difference in maximum pressure was found in linguistic task (p= 0,044), with nfavPPA participants showing a lower maximum pressure than svPPA participants.

Conclusion

These preliminary results suggest that the use of graphical markers can be helpful in the differential diagnosis of PPA subtypes. A longitudinal study is ongoing to collect a bigger and more balanced PPA sample by including these markers within an already existing screening test battery.
Neuroinflammation in medial temporal regions predicts cognitive decline in dementia with Lewy bodies
Authors: Malpetti Maura , Savulich George , Mak Elijah , Nicastro Nicolas , Jones P. Simon , Surendranathan Ajenthan , Su Li , Passamonti Luca , Chouliaras Leonidas , Fryer Tim D. , Hong Young T. , Aigbirho Franklin I. , Rowe James B. , O'Brien John T.
Published: February 2022

Background

Neuroinflammation is implicated in the pathogenesis of neurodegenerative disorders including dementia with Lewy bodies (DLB), but its relationship with disease progression, including cognitive decline, is less well understood. We investigated the predictive value of microglial activation, assessed in vivo using positron emission tomography (PET) imaging with [11C]PK11195, on the rate of annual cognitive decline in patients with DLB. We hypothesised a detrimental effect of inflammation on cognition over time.

Method

Nineteen patients with DLB underwent baseline assessment, including [11C]PK11195 PET and cognitive tests from the Neuroimaging of Inflammation in Memory and Related Other Disorders (NIMROD) study. Cognitive performance was then assessed at an average of 1.1‐year intervals (standard deviation (SD) ± 0.03 years) up to 3.4 years (average ± SD = 3.20 ± 0.16) using the revised Addenbrooke's Cognitive Examination (ACE‐R) and Cambridge Neuropsychological Test Automated Battery (CANTAB). Regional [11C]PK11195 binding potential values were included in a principal component analysis (PCA). Linear mixed effects models were applied to the longitudinal ACE‐R scores. Individual component scores were included in analysis of interaction with time to test for associations between [11C]PK11195 uptake at baseline and longitudinal cognitive performance scores. Age and sex were included as covariates in the models.

Result

Patients showed a mean 8.8‐point loss per year on the ACE‐R total score (p<0.001) (Figure 1, left panel). The PCA identified five components, explaining >80% of variance. Annual rate of change in the ACE‐R total score (estimate=‐2.90, p=0.0226) (Figure 1, right panel), memory (estimate=‐1.14, p=0.004) and attention/executive function (estimate=‐0.80, p=0.022) sub‐scores was negatively associated with increased microglial activation in component #4, which was mainly loaded onto the hippocampus, amygdala and pallidum but also parahippocampal cortex and the pre‐subgenual frontal area.

Conclusion

Higher levels of neuroinflammation in medial temporal regions and pallidum predict faster cognitive decline in patients with DLB, particularly in the domains of memory and attention/executive function. Our results encourage the application of [11C]PK11195 PET in DLB to inform prognosis, and to improve patient stratification for clinical trials.
Longitudinal clinical, cognitive and neuroanatomical changes over five years in GBA‐positive Parkinson’s disease patients
Authors: Leocadi Michela , Agosta Federica , Canu Elisa , Donzuso Giulia , Basaia Silvia , Piramide Noemi , Stojkovic Tanja , Stankovic Iva , Tomic Aleksandra , Markovic Vladana , Petrovic Igor , Stefanova Elka , Kostic Vladimir S. , Filippi Massimo
Published: February 2022

Background

Glucocerebrosidase gene (GBA) mutations are the greatest genetic cause of Parkinson’s disease (PD). The aim of this study was to monitor the longitudinal disease course of PD patients with GBA mutation (GBA‐positive) at diagnosis compared to PD noncarriers (GBA‐negative) along a 5‐year follow‐up, evaluating changes in clinical and cognitive outcomes, cortical thickness, and subcortical volumes.

Method

10 GBA‐positive and 20 GBA‐negative PD at diagnosis underwent clinical, neuropsychological and brain MRI assessments at study entry and once a year for 5 years. At baseline and at the last visit, each group of patients was compared in terms of cortical thickness and subcortical volumes to a group of 22 age‐matched healthy controls (HC). Clinical, cognitive and cortical/subcortical MRI features were compared between groups at baseline and over time.

Result

At baseline, GBA‐positive and GBA‐negative patients had similar clinical and cognitive profiles. Compared to GBA‐negative and HC, GBA‐positive patients showed cortical thinning of left temporal, parietal and occipital gyri. Over time, compared to GBA‐negative, GBA‐positive worsened significantly in motor and cognitive symptoms, and showed a greater pattern of bilateral cortical thinning involving also frontal cortices. After 60 months, compared to HC, GBA‐negative PD patients showed a pattern of cortical thinning similar to that shown by GBA‐positive at baseline. The two groups of patients showed similar patterns of subcortical volume loss over time.

Conclusion

Compared to GBA‐negative patients, GBA‐positive PD showed a greater and earlier cortical thinning which worsened over time. GBA‐negative PD patients reached the pattern of cortical thinning of GBA‐positive at the baseline only after 5 years, reflecting a slower disease progression. This study highlights the importance of the early detection of GBA mutation in PD patients. Funding: This study was supported by the Ministry of Education and Science of the Republic of Serbia (Grant #175090).
Topographic distribution of MRI‐visible enlarged perivascular space (ePVS) in Alzheimer's disease: Role of ePVS located at the temporal lobe
Authors: Na Han Kyu , Baek Min seok , Kim Han‐Kyeol , Cho Hanna , Lyoo Chul Hyoung
Published: February 2022

Background

Accumulating data suggests that MRI‐visible enlarged PVS (ePVS) may serve as an imaging marker of glymphatic dysfunction. Until now, ePVS in various regions such as basal ganglia (BG‐ePVS) or centrum semiovale (CS‐ePVS) have been introduced and their topographic distributions are recognized as a marker to differentiate the underlying etiologies, although they failed to predict amyloid‐positivity. As the conventional classification of ePVS was insufficient to predict Alzheimer pathologies, we developed a new grading system for ePVS located at the temporal lobe (T‐ePVS), a strategic region where both amyloid and tau overlaps in their early stages. We investigated whether amyloid‐positivity or tau‐positivity is associated with T‐ePVS along with other conventional ePVS markers.

Method

ePVS were manually counted on magnetic resonance (MR) images obtained from 272 participants (cognitively normal, n=96; cognitively impaired, n=176) who underwent dual PET scans (18F‐flortaucipir and 18F‐florbetaben). T‐ePVS and BG‐ePVS were defined as “extensive” when the counts in any hemisphere were >10, while the cutoff of CS‐ePVS was >20. Subjects were grouped based on the combinations of the severity of region‐specific ePVS and their relationship with amyloid‐/tau‐positivity were investigated. Moreover, logistic regression models were constructed to investigate whether severity of region‐specific ePVS were predictive of amyloid‐ or tau‐positivity.

Result

Compared to those with low T‐ePVS burden (21/136, 15.4%), subjects with high T‐ePVS burden were more likely to show amyloid‐positivity (89/136, 65.4%). Among the 8 groups based on region‐specific ePVS burden, subjects with high T‐/CS‐ePVS but low BG‐ePVS burden showed highest amyloid‐positivity (46/56, 82.3%). Furthermore, high T‐ePVS burden (Odds ratio [OR]=7.458; 95% confidence interval [CI]=3.387‐16.419), low BG‐ePVS burden (OR=0.238; 95% CI=0.108‐0.524), and APOE ε4 allele presence (OR=5.175; 95% CI=2.444‐10.959) were significantly associated with amyloid‐positivity. Similar results were reproduced when the analyses were performed within subjects with cognitive impairment. Within the amyloid‐positive subjects, subjects with high CS‐ePVS but low BG‐ePVS burden were likely to exhibit tau‐positivity, while the severity of each region‐specific ePVS were not independently predictive of tau‐positivity.

Conclusion

Our findings suggest that investigating the load and topographic distribution of ePVS based on MR images may be useful for predicting amyloid‐ and tau‐positivity, suggesting that glymphatic dysfunction may contribute to Alzhiemer’s pathogenesis.
Caring for Spirit: Developing online dementia education and training with Aboriginal and Torres Strait Islander Australians
Authors: Poulos Lauren , Wall Sharon , Pross Eliza , Daylight Gail , Delbaere Kim , Broe Gerald A , Radford Kylie
Published: February 2022

Background

Aboriginal and Torres Strait Islander peoples (hereafter Aboriginal Australians) experience rates of dementia 3‐5 times higher than the general Australian population. Promoting dementia health literacy in culturally appropriate ways has been identified by Aboriginal communities as a priority to improve awareness of dementia and support timely diagnosis and service access, enhance care, and support wellbeing for family caregivers. This project aimed to respond to the need for culturally relevant, evidence‐based and accessible dementia education with this population.

Method

The ‘Caring for Spirit’ project sought to develop a dementia education website and online training program for the Aboriginal health and aged care workforce, as well as for family caregivers. Resource development applied the following overarching principles: (1)Dementia information was produced from Aboriginal cultural perspectives, foregrounding research evidence and lived experience in Aboriginal populations; (2)The project was led by an Aboriginal project manager and engaged Aboriginal consultants throughout; (3)Training was designed using appropriate and engaging pedagogical methods; (4)Resources needed to be accessible to a wide range of users, taking into consideration cultural diversity, technology access, disabilities and literacy levels; (5)Extensive community consultation and engagement guided the development and dissemination of the resources. The project team worked beside Aboriginal Elders, colleagues and wider networks, established community research partners, and dementia researchers.

Result

Workshops were conducted with community‐based researchers and service providers to scope education needs and plan the content, and later, seek feedback on initial website content and design. Infographics and videos were important elements, including interviews with Aboriginal and non‐Aboriginal dementia experts from across Australia, and scripted drama to illustrate key concepts in understanding dementia. To complement the website, an interactive online training program featured exploratory non‐linear progression across four modules, reflective exercises, voice‐over narrations, and multimedia. Training modules were evaluated by our network of project collaborators to ensure that information provided was accurate, supported by research, and presented in a way that is culturally safe, respectful, practical, and easy to understand and apply.

Conclusion

These resources, designed with older Aboriginal people and their communities, will contribute to reducing the high burden of dementia in this population, and enhance dementia care.
Brain MRI signatures of atrophy in genetic frontotemporal lobar degeneration
Authors: Spinelli Edoardo Gioele , Ghirelli Alma , Basaia Silvia , Cividini Camilla , Riva Nilo , Magnani Giuseppe , Caso Francesca , Caroppo Paola , Prioni Sara , Rossi Giacomina , Tremolizzo Lucio , Appollonio Ildebrando , Silani Vincenzo , Carrera Paola , Filippi Massimo , Agosta Federica
Published: February 2022

Background

Genetic heterogeneity underlying different clinical presentations of the frontotemporal lobar degeneration (FTLD) spectrum hampers the identification of useful biomarkers that may be able to monitor disease progression and/or facilitate the enrolment in clinical trials. The aim of this study was to assess cortical, subcortical and cerebellar grey matter (GM) atrophy using magnetic resonance imaging (MRI) in patients affected by disorders of the FTLD spectrum with known genetic mutations.

Method

Sixty‐six patients carrying FTLD‐related mutations were enrolled, including 44 with pure motor neuron disease (MND) and 22 with frontotemporal dementia (FTD). Sixty‐one patients with sporadic FTLD (sFTLD) matched for age, sex and disease severity with genetic FTLD (gFTLD) were also included, as well as 52 healthy controls (HC). A whole‐brain voxel‐based morphometry (VBM) analysis was performed. GM volumes of subcortical and cerebellar structures were also obtained.

Result

Compared with HC, GM volume loss on VBM was generally greater and more diffuse in genetic FTD (gFTD) cases, followed by sporadic FTD (sFTD) cases and genetic MND (gMND) cases, whereas sporadic MND (sMND) showed very focal atrophy of the motor cortex. Patients carrying GRN and C9ORF72 mutations showed the most widespread cortical volume loss, whereas SOD1 and TARDBP patients were the least atrophic. Greater atrophy of the parietal cortices and thalami was found, globally, in genetic FTLD patients compared with sporadic FTLD and, particularly, in C9‐MND patients compared with sMND. When assessing deep GM volumes, genetic FTLD patients showed significant volume loss compared with sporadic FTLD in the caudate nuclei and thalami. In particular, greater atrophy of the left caudate and right thalamus was found in C9MND compared with sMND. At the cerebellar level, greater atrophy of the right lobule VIIb could discriminate genetic FTLD from sporadic FTLD patients.

Conclusion

Our data suggest that measures of deep GM and cerebellar involvement might be useful markers of genetic FTLD, particularly C9ORF72‐related disorders, regardless of the clinical presentation within the FTLD spectrum.

Supported by: Italian Ministry of Health (RF‐2011‐02351193; GR‐2011‐02351217) and European Research Council (StG‐2016_714388_NeuroTRACK).
Quantification of neocortical sulcal widening as a rapidly‐computable brain atrophy metric: Application to frontotemporal dementia
Authors: Yeh Chien‐Lin , Clarke Mica T.M. , Takano Akihiro , Rabiner Eugenii A. , Rohrer Jonathan D. , Schwarz Adam J.
Published: February 2022

Background

Regional patterns of brain atrophy are characteristic features of neurodegenerative disorders. Radiological assessments of cortical brain atrophy tend to reflect sulcal widening and brain shrinkage, whereas quantitative assessments based on grey matter volume or cortical thickness can be hampered by poor SNR and inaccurate estimation of grey/white matter boundaries. Our aim was to define a simple and rapidly‐computable marker of localized sulcal widening based on standard brain tissue segmentation tools and assess it in a cohort of individuals with frontotemporal dementia (FTD) and SV2A PET scans.

Method

3DT1 MRI and [11C]UCB‐J PET scans were analyzed from individuals with FTD (N=8, MMSE range 10‐28) and age‐matched healthy volunteers (HV, N=17) within the MIND‐MAPS consortium. 3DT1 images were segmented into gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) using SPM12. The lobar CSF occupancy fraction (CSFOF) was calculated as CSF/(GM+WM+CSF) in the frontal, parietal, temporal and occipital lobes. The tissue type masks were created by a threshold of total tissue occupancy > 50% then assigning each voxel to the tissue for which it has the maximum probability. 3DT1 scans were also assessed using cortical thickness (Freesurfer v6.0). SV2A binding was quantified as distribution volume ratio (DVR‐1) from [11C]UCB‐J using centrum semiovale as reference region.

Result

The CSFOF computation time was approximately 6 minutes per scan on a 2019 MacBook pro. CSFOF was significantly higher in FTD than in HV (ANOVA F=19.6, p<0.0001, Figure 1). Corrected, non‐parametric linear relationships between CSFOF and MMSE, cortical thickness and [11C]UCB‐J DVR‐1 are summarized in Table 1 and Figure 2 for the frontal lobe. The relationships were weaker in the FTD cohort alone, compared with the full sample. Cortical thickness was the only nominally significant association remained after correction for age and MMSE, suggesting that CSFOF and SV2A density may reflect different aspects of the disease.

Conclusion

The CSFOF provides a simple, rapid and robust quantification of lobar brain atrophy, reflecting sulcal widening and both grey and white matter atrophy. It complements other imaging markers of neurodegeneration and extends the concepts of ventricular volume and hippocampal occupancy fraction to the wider neocortex.
Converging longitudinal patterns of atrophy in clinical variants of frontotemporal lobar degeneration
Authors: Spinelli Edoardo Gioele , Basaia Silvia , Cividini Camilla , Riva Nilo , Magnani Giuseppe , Caso Francesca , Caroppo Paola , Prioni Sara , Tremolizzo Lucio , Appollonio Ildebrando , Silani Vincenzo , Carrera Paola , Filippi Massimo , Agosta Federica
Published: February 2022

Background

The aim of this study was to assess longitudinal patterns of atrophy shown by magnetic resonance imaging (MRI) in the whole‐brain GM of patients affected by different clinical variants of the FTLD spectrum.

Method

Fifty‐nine patients, including 26 with behavioral variant of frontotemporal dementia (bvFTD), 10 non‐fluent/agrammatic variant of primary progressive aphasia (nfvPPA), 12 semantic variant of PPA (svPPA), and 11 MND, in the absence of known pathogenic mutations, underwent MRI on a 3T scanner at 6‐month intervals for one year. Thirty‐three healthy controls underwent the same protocol. 3D T1‐weighted MRI sequences were analyzed using voxel‐based morphometry to assess the longitudinal evolution of GM atrophy in patients, compared with HC.

Result

At baseline, severe diffuse atrophy of frontotemporal cortical regions and basal ganglia was found in bvFTD, nfvPPA and svPPA groups, whereas MND did not show significant GM atrophy. At 6‐month follow‐up, bvFTD and PPA groups showed significant progression of atrophy in the insular (bvFTD, nfvPPA and svPPA) and anterior cingulate cortex (bvFTD and nfvPPA), bilaterally, as well as in the left caudate nucleus and middle temporal cortex (svPPA). At 12‐month follow‐up, similar patterns of atrophy progression were found, with the additional involvement of the superior frontal cortical gyri in nfvPPA, bilaterally, and the right hippocampus in svPPA. No significant progression of atrophy was found in MND patients.

Conclusion

Our data suggest that atrophy of insular and anterior cingulate cortical regions closely reflects the progression of neurodegeneration across the behavioral and linguistic presentations of frontotemporal dementia (FTD), in contrast with a substantial sparing of GM in MND. Supported by: European Research Council (StG‐2016_714388_NeuroTRACK).
Baseline MRI and CSF measurements in cognitively normal individuals as prognostic markers of progression to mild cognitive impairment
Authors: Prosser Lloyd , Macdougall Amy , Fiford Cassidy M. , Sudre Carole H. , Manning Emily N. , Malone Ian B. , Walsh Phoebe , Goodkin Olivia , Pemberton Hugh , Barkhof Frederik , Biessels Geert Jan , Cash David M. , Barnes Jo
Published: February 2022

Background

Alzheimer’s disease is currently one of our greatest socioeconomic challenges. Understanding the ability of biomarkers to predict clinical progression, prior to significant cognitive decline, is critical when considering secondary prevention strategies. Here we consider the association between a range of single time‐point baseline biomarkers in a cognitively‐normal group and the subsequent risk of developing Mild Cognitive Impairment (MCI).

Method

Data from Alzheimer’s Disease Neuroimaging Initiative (ADNIGO/2/3) were used. Baseline markers (CSF amyloid 1‐42, CSF ptau, white matter hyperintensities [WMH], microbleeds, whole‐brain volume, and hippocampal volume) were obtained for 192 cognitively‐normal (CN) individuals (controls and with significant memory concern; see table). Longitudinal diagnostic data to calculate conversion time was also collected. Cox regression models were fitted for time to diagnosis of MCI. Models were fitted for each marker separately, and in a fully‐adjusted model (containing all markers); adjustments for total intracranial volume (TIV) were performed when needed. Models were refitted adjusting for baseline age.

Result

There was strong evidence that increased WMH volume and lower hippocampal volume were associated with conversion to MCI. This was found in both individual models, and in the fully‐adjusted model (see figure). Decreased whole brain volume, CSF amyloid, and increased microbleeds were found to be associated with conversion to MCI when considered individually but not in a fully‐adjusted model. CSF ptau was not associated with conversion, either individually or in the fully‐adjusted model. Adjustments for baseline age did not materially change results.

Conclusion

Single time‐point baseline measurements of increased WMH and decreased hippocampal volume were independently predictive of subsequent progression to MCI. Hippocampal volume and WMH may represent different processes; lower hippocampal volumes representing greater neurodegeneration and WMH as a surrogate marker of numerous pathologies, including cerebrovascular disease. While decreased whole brain volume, decreased CSF amyloid, and increased microbleeds were predictive of conversion to MCI when assessed separately, their non‐significant association in the fully‐adjusted model suggests that these are not as strongly predictive of conversion.
Identifying imaging genetics biomarkers in Alzheimer’s disease via integrating graph convolutional neural network and canonical correlation analysis
Authors: Kim Mansu , Yao Xiaohui , Saykin Andrew J. , Moore Jason H. , Long Qi , Kim Dokyoon , Shen Li
Published: February 2022

Background

Brain imaging genetics is an emerging research topic in the study of Alzheimer’s disease (AD). The conventional approach, such as canonical correlation analysis (CCA), has been widely used to identify imaging genetic associations. A deep learning model has recently been proposed to better understand the roots of the complex association between imaging and genetic measures. We propose a graph convolutional neural network (GCN) with CCA loss function to integrate and identify the complex imaging genetics associations in AD.

Method

We proposed a spectral GCN approach with CCA loss function (GCN‐CCA) to extract feature representations from imaging and genetics data. Briefly, the graph embeddings on the graph nodes were filtered in the Fourier domain. We used two hidden layers with 64 hidden units for extracting imaging and genetic data. ReLU activations were used after each graph convolution layer. A canonical correlation loss function was optimized based on Adam optimizer. We compared our model with the deep CCA model (DCCA) for AD classification.

Result

We downloaded data for 310 participants (103 AD and 207 Cognitive Normal [CN]) including neuroimaging and genetic data from the ADNI database. We used average structural connectivity based on the AAL atlas as the graph in our GCN model, three imaging measurements (VBM, FDG, FBR) as initial attributes on the graph nodes. We selected 2,644 candidate SNPs from the GWAS catalog (https://www.ebi.ac.uk/gwas/). The proposed model obtained 82.25 % test accuracy for the AD/CN classification, outperforming the DCCA model (77.41%). For interpretation, we generated the saliency maps using guided gradient backpropagation (Figs 1 and 2). We observed the imaging phenotypes from left middle temporal gyrus, superior temporal, frontal inferior triangularis, putamen, paracentral lobule, frontal medial orbital, and pallidum, and right posterior cingulum, and genetic markers from ABCA13 (rs2163935, rs6955132, rs4024044) and APOE (rs429358) contributed to the AD outcome prediction.

Conclusion

Here, we demonstrated the utility of GCN‐CCA model and its interpretability. The GCN‐CCA not only obtained higher prediction performance but also highlighted important regions for AD classification. We plan to apply our algorithm to other AD cohorts to see if our algorithm generalizes to independent data sets.
The impact of accuracy on functional neural correlates of the useful field of view (UFOV) task
Authors: Kraft Jessica N. , Albizu Alejandro , O'Shea Andrew , Hausman Hanna K. , Evangelista Nicole D. , Boutzoukas Emanuel , Hardcastle Cheshire , Van Etten Emily J. , Bharadwaj Pradyumna K. , Song Hyun , Smith Samantha G. , Porges Eric , DeKosky Steven T. , Hishaw Georg A. , Wu Samuel S. , Marsiske Michael , Cohen Ronald , Alexander Gene , Woods Adam J.
Published: February 2022

Background

Rapid declines in processing speed performance occur in aging and serve as a critical marker of cognitive abilities. Studies suggest that Useful Field of View (UFOV) training may ameliorate cognitive decline. The ACTIVE trial, consisting of over 2,800 participants, found a 29% reduction in ADRD risk 10 years following intervention. Recent clinical trials aimed at slowing cognitive decline in AD have focused on UFOV‐based interventions. Despite its efficacy, little is known about its neural correlates. The current study investigated blood‐oxygen level dependent (BOLD) activation patterns of the UFOV task.

Method

233 older adults completed the UFOV task while undergoing task‐based fMRI. The UFOV task requires participants to identify a central target, as well as a peripheral target amongst distractors, presented for a brief period of time. First‐level contrasts modeled hemodynamic response, while second‐level analyses assessed unique BOLD activation associated with encoding and recall components of the task. Age, education, sex and scanner were included as covariates. Significant clusters were defined as >10 voxels, and all clusters were corrected at a false discovery rate (FDR) of p<0.05.

Result

Previous data have shown widespread bilateral whole‐brain activation associated with the encoding component, whereas discrete activation patterns were related to the recall component of the task. Additionally, numerous shared brain regions were critical to both the encoding and recall components of the UFOV task. We further investigated cortical activation patterns associated with age‐related differences in UFOV performance. Variability in performance was related to widespread patterns of activation during the encoding component of the UFOV task, namely the superior parietal lobule, insula, hippocampus and DLPFC. Assessing UFOV performance variability within recall, no significant clusters survived FDR correction. Furthermore, the inferior parietal lobule, cerebellum, and inferior frontal gyrus were critical in both encoding and recall in UFOV performance.

Conclusion

The present study elucidates pivotal regions associated with UFOV task performance. Notably, we found that variability in UFOV performance is directly associated with neural activation during the presentation/apprehension of the stimuli, but not during recall. These findings suggest that variation in neural activation during speeded attentional processing is central to age‐related UFOV performance differences, and interventions targeting these neural processes may be of benefit.
The Overlap Index: A new means for early detection of serial tau PET signal change
Authors: Lee Jeyeon , Min Hoon‐Ki , Lundt Emily S. , Albertson Sabrina M. , Botha Hugo , Senjem Matthew L. , Gunter Jeffrey L. , Schwarz Christopher G. , Jones David T. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Lowe Val J.
Published: February 2022

Background

In longitudinal tau (flortaucipir) positron emission tomography (PET), a reliable method to detect a tau accumulation while minimizing a random variability is crucial. Variability can be a problem especially in the early stages of tau pathology in which only small regions and low levels of accumulation may exist relative to the range of random noise fluctuation.

Method

We recruited 76 participants who had serial scans for a total of 234 tau PET scans. In this cohort we tested a PET signal measurement method we developed called the “overlap index” (OI). The OI was calculated by identifying voxels that have elevated tau PET signal that overlap on two successive tau PET scans in the same participant (Figure 1). This analysis procedure tests the hypothesis that voxel‐wise, elevated‐uptake consistency might be more sensitive for early detection of tau tangles than standard ROI based measures. We compared OI to ROI based measures for early detection of increasing tau PET signal and detection clinical progression. A meta ROI used in this analysis included the amygdala, entorhinal cortex, fusiform, parahippocampal, and inferior temporal and middle temporal gyri.

Result

OI showed regional progression of tau in the temporal lobe followed by increases throughout the brain at relatively low standardized uptake value ratio (SUVr) levels (Figure 2A and B). The topographical pattern of OI was in agreement in some respects with the sequential appearance of tau aggregation (Figure 2B and C). The high‐OI group (OI>0.5) had a significantly higher ΔSUVr compared to the low‐OI group (OI<0.5) (Figure 3A and B). OI identified subjects that did or didn’t have tau accumulation within meta SUVr subgroups even when below the meta ROI SUVr threshold. OI was associated with early clinical disease stage progression vs meta‐ROI SUVr measures (Figure 3C‐E).

Conclusion

In this study, we found that OI tau PET measurement was able to quantify voxel‐wise consistency using a set of serial scans. OI may detect earlier tau tangle accumulation and earlier disease progression than standard SUVr measurements in tau PET.
Automatic brain extraction using deep learning
Authors: Weatheritt Jack , Vega Barbara Brito , Reinwald Michael , Joules Richard , Lorenzini Luigi , Barkhof Frederik , Mutsaerts Henk‐Jan , Wink Alle Meije , Wolz Robin
Published: February 2022

Background

Brain extraction from cranial MR images is an important precursor to many medical‐imaging algorithms prominent in Alzheimer’s disease research, including image registration, atlas‐based segmentation and longitudinal analyses. Moreover, skull stripping can be an important step in deidentification by removing facial features from the image. One widely used method is PiNCRAM, which provides reliable results, however, is computationally intensive and time consuming. This is a bottleneck for scalable clinical trial data processing and potential clinical applications. The emergence of deep‐learning segmentation tools (convolutional neural networks – CNNs) provide a framework for drastically reducing the processing time and potentially increasing the reproducibility. In this work, we develop a CNN‐based skull‐stripping method that mimics PiNCRAM, but runs in a fraction of the time.

Method

A cohort of 2300 T1 weighted scans from healthy participants with a slightly increased risk for Alzheimer’s Disease, drawn from the European Prediction of Alzheimer’s Dementia (EPAD) consortium was used to train a CNN. The examples fed to the model are generated from PiNCRAM masks. The network architecture is a 3D U‐NET consisting of around 200,000 parameters and was trained using the ADAM optimiser with default settings (as implemented in Tensorflow 2).

We used 50 T1 weighted scans from the Open Access Series of Imaging Studies (OASIS) test‐retest cohort as a validation set.

Result

Visually, the CNN is seen to segment the brain well (Fig 1). The mean dice overlap score on the validation set is 0.9811 (std. 0.002791). The median CNN test‐retest volume change percentage (expressed between 0 and 1) was ‐0.000854 (std. 0.006703), whilst PiNCRAM was 0.00883 (std. 0.0115; Fig. 2, Table 1). From the superior reproducibility, one could infer that dice overlap less than unity does not imply CNN error (Fig 1.)

Our CNN pipeline takes approximately 1.1% of the time taken by PiNCRAM. Each brain takes approximately 20 seconds (when in batch mode) versus 30 minutes for PiNCRAM.

Conclusion

The PiNCRAM‐CNN provides a robust and highly accurate brain extraction alternative to PiNCRAM. Not only is it two orders of magnitude faster to run, but it also displays an order of magnitude better reproducibility.
Ventricle shape features as a reliable differentiator between the behavioral variant frontotemporal dementia and other dementias
Authors: Manera Ana Laura , Dadar Mahsa , Collins Louis , Ducharme Simon
Published: February 2022

Background

In the absence of a pathologic genetic mutation, diagnostic certainty of the behavioral variant frontotemporal dementia (bvFTD) still relies on convergence of clinical criteria and imaging findings. Using deformation‐based morphometry (DBM), we recently showed that ventricular volume can discriminate bvFTD from cognitively normal controls (CN)1. Here, we investigate the performance of shape‐based ventricular features to differentiate bvFTD from Alzheimer’s Dementia (AD), mild cognitive impairment (MCI), semantic and progressive non‐fluent aphasia variants of FTD (SV and PNFA), and CN.

Method

Data included 825 participants: 59 bvFTD, 28 SV, 30 PNFA, and 105 CN from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) and 322 age‐matched amyloid β+ MCI, 127 age‐matched amyloid β+ AD and 164 age‐matched CN from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). A previously validated patch‐based label fusion technique was employed to segment the lateral ventricles 2. Linearly registering the images to ICBM2009c template, the ventricles were divided into anterior‐posterior (using coronal slice y=120) and left‐right halves (separately for frontal and temporal lobes). Total ventricle volume (TVV), anterior‐to‐posterior ratio (APR) and frontal and temporal parenchymal left‐right ratios (LRFR and LRTR) were used alone and in combination with each other, age, and sex, to differentiate bvFTD from all other cohorts. A support vector machine classifier (fitcsvm from MATLAB with a linear kernel) was trained on each feature set to perform the classification task using 10‐fold cross validation, repeated 100 times.

Result

Using APR to identify bvFTD from a mixed age‐matched cohort (Control, MCI, AD, SV and PNFA) yielded an accuracy of 92%. Adding additional features did not improve global classification performances. The top accuracies against each individual cohort were 89% for bvFTD vs Control, 91% for bvFTD vs MCI using APR+TVV, and 83% for bvFTD vs AD using APR+LRTR. The best accuracies discriminating bvFTD from SV and PNFA were 71% and 76% respectively, using APR+TVV+LRTR+LRFR.

Conclusion

The APR is an easy to obtain and generalizable ventricle‐based feature from T1‐weighted MRIs (routinely acquired and available in the clinic) that can be used to differentiate bvFTD from normal subjects, other FTD variants, MCI, and AD patients.
Dementia care in diverse older adults in the Deep South and the rest of the United States
Authors: Pisu Maria , Martin Roy C , Shan Liang , Pilonieta Giovanna , Kennedy Richard E , Oates Gabriela , Kim Young‐Il , Geldmacher David S
Published: February 2022

Background

Prevalence of Alzheimer’s disease and related dementia (ADRD) is increasing especially in the southeastern US. For older adults with ADRD, gaps exist in utilization of specialists (neurologists, psychiatrists, geriatricians) and ADRD drugs, e.g., across racial/ethnic (R/E) patient groups. Because some R/E groups, e.g., African Americans (AAs), are overrepresented in the Southeast, closer scrutiny of their ADRD care is needed. Within the Southeast, the Deep South (DS) includes some of the poorest US states (Alabama, Mississippi, Louisiana, Georgia, and South Carolina). We examined R/E disparities in specialist and drug utilization in DS vs. non‐DS states, and examined individual and context‐level factors that impact utilization in each region.

Methods

Population, outcomes, and individual‐level covariates were identified from claims for fee‐for service Part A/B (hospital, outpatient) and D (prescription drugs) Medicare beneficiaries in 2013‐2015 (66 and older). For context‐level covariates, we combined Area Health Resource File county‐level data in exploratory factor analysis (EFA). We conducted adjusted analyses for DS and non‐DS with outcomes: 1) ≥ 1 specialist visit; and 2) ≥ 1 prescription for donepezil, galantamine, rivastigmine, or memantine. R/E groups included AAs, Non‐Hispanic Whites, Hispanic, Asian/Pacific Islander, and Other.

Results

We identified 127,512 Medicare beneficiaries with ADRD (9.4% in DS). Specialist utilization ranged from 37% AAs to 42% Whites in DS, 45% Whites to 50% AAs in non‐DS. ADRD drug utilization ranged from 48% AAs to 55% Hispanics in DS, 42% AAs to 48% Hispanics in non‐DS. EFA identified 4 context‐level factors with two common to DS and non‐DS. R/E disparities were not significant, except for non‐DS Asian/Pacific Islanders vs. Whites (RR 0.81, CI = 0.777‐0.849, p<.0001). In DS and non‐DS, comorbidities, poverty, and EFA factor “Medical Resources Availability” were associated with utilization outcomes. No other factor was associated with outcomes in DS. In non‐DS, factors “Population Health”, “Health Insurance Market” were associated with outcomes.

Conclusion

ADRD specialist and drug utilization is relatively low in both DS and non‐DS, with no significant R/E differences. Research should continue to examine factors that may drive medical care decisions of older adults with ADRD in US regions with different socio‐economic and medical resource contexts.
Automated delineation of enlarged perivascular spaces (EPVS)
Authors: Goryawala Mohammed , Del Brutto Andrade Victor , Saporta Anita Seixas Dias , Loewenstein David , Rundek Tatjana
Published: February 2022

Background

Enlarged Perivascular Spaces (EPVS) are associated with small vessel disease. The objective of this study was to develop semi‐automated techniques for the quantification of EPVS burden.

Method

An intensity‐based threshold was used to identify EPVS. A threshold of maximum plus two standard deviations of intensities of normal‐appearing white matter in the supratentorial region was used to identify EPVS. To compare the performance of the semi‐automatic approach for EPVS segmentation to visually rated EPVS scores, two regions of interest were outlined in the basal ganglia (BG) region and the centrum semiovale (CS). For both these regions, a score was generated by an experienced neurologist by visually counting the number of EPVS with a score of zero representing the absence of any EPVS, a score of 1 representing 1‐10 EPVS, a score of 2 representing 11‐20 EPVS, a score of 3 representing 21‐40 EPVS, and a score of 4 representing >40 EPVS. The semi‐automatic approach provided a count of the number of EPVS that were converted to grade using the same scaling as the visual approach. Spearman Rank Order Correlation Coefficient was calculated to estimate the correlation between the two methods. Mann‐Whitney U tests of ranks are employed to determine if any group differences are present. Finally, Cohen’s kappa was used to compare the concordance between measures between the two different methods.

Result

Figure 2 shows the results of the segmentation in subjects with differing EPVS burden. Spearman Rank Order Correlation analysis showed a significant correlation between the EPVS scores determined visually and those obtained semi‐automatically in 60 subjects (Table 1). Results of the Mann‐Whitney U tests showed that there was no statistically significant (p > 0.05) difference between the EPVS scores obtained visually and using the semi‐automatic approach (Table 2). Cohen's κ test showed that there was moderate agreement between the two estimation methods in the BG (κ = .522, p < .0001) and CS regions (κ = .571, p < .0001).

Conclusion

The developed method showed good concordance with expert visual read and can provide quantitative measures of EPVS burden (counts and volume) for evaluation of vascular risk.
The plasma fraction GRF6019 slows brain atrophy in mild‐to‐moderate Alzheimer’s disease
Authors: Lehallier Benoit , Lohr Scott , Braithwaite Steven P. , Hannestad Jonas
Published: February 2022

Background

The plasma fraction GRF6019 shows multiple benefits on age‐related changes in the mouse brain, including enhanced synaptic density and neurogenesis. In a trial of GRF6019 in mild‐to‐moderate Alzheimer’s disease (AD), cognition stabilized over a 24‐week period (Hannestad et al., 2020). Here we present the volumetric MRI data from that trial.

Method

Participants were 60‐90 years old with probable AD according to NIA‐AA criteria, and Mini Mental State Examination score 12‐24. They received 1 daily infusion of 100 mL or 250 mL of GRF6019 over 5 consecutive days at Week 1 and Week 13. MRIs were done at baseline at the end of the study using a MP‐RAGE or IR‐SPGR sequence. The 3D T1‐weighted scans were acquired sagittally over approximately 192 slices at a thickness of 1.0‐1.2. Images underwent the Freesurfer pipeline and hand editing to correct over‐ and under‐estimations of hippocampal volumes. Hippocampal and lateral ventricular volumes were extracted and normalized by the estimated total intracranial volume. Fold‐changes between baseline and 6 months were estimated and log2‐transformed to obtain a symmetrical distribution. MRI images from ADNI were analyzed in the same manner.

Result

Hippocampal volumes decreased from baseline by 2% (p<0.001, one‐sample Wilcoxon test) in ADNI AD participants (n=75), while hippocampal volumes did not significantly change in ADNI control participants (n=82; 0.03%, p>0.05) or in GRF6019‐treated AD trial participants (n=34; 0.3%, p>0.05). Lateral ventricle volumes increased from baseline by 5.2% in ADNI AD but showed smaller increases in ADNI controls (2.7%) and in GRF6019‐treated individuals (3.1%). The difference in change from baseline between GRF6019 AD and ADNI AD was significant in the right lateral ventricle (p<0.005, two‐samples Wilcoxon test) but not in the left lateral ventricle. There was no apparent difference in the change from baseline in MRI volumes between the 100 ml and 250 mL dose of GRF6019.

Conclusion

In mild‐to‐moderate AD, the plasma fraction GRF6019 slowed hippocampal atrophy and lateral ventricle enlargement compared to matched individuals with AD in ADNI. The MRI data are consistent with the lack of cognitive worsening observed in these trial participants and supports continued clinical development of GRF6019 in AD.
The preliminary efficacy of Caring of Caregivers Organically (CoCO) health education program on the health of Latina caregivers of persons with Alzheimer’s disease and related dementias
Authors: Guzman Jacqueline , Rocha Judith Susan , Soto Yuliana , Aguiñaga Susan
Published: February 2022

Background

By 2060, the prevalence of Alzheimer’s disease and related dementias (ADRD) in Latinos is projected to increase by 832%,1 representing an increased demand of caregivers. Caregiving has been shown to increase risk for depression and anxiety,2 poorer quality of life (QoL)3 and health behaviors.4 There is a critical need to create linguistically and culturally responsive interventions to promote caregiver health as they often forgo their own health to help the person with ADRD. CoCO is a health education program centered on caregiver’s physical and mental health. The purpose of this study was to assess the preliminary efficacy of CoCO on health‐related QoL, health behaviors, and symptoms of depression among Latina ADRD family caregivers.

Methods

Fourteen Latina ADRD family caregivers were randomized to the 8‐week, 2‐hours per week CoCO health education program delivered by health companions (n=8, mage=59.41±11.1) or a wait‐list control group who received the CoCO manual (n=6, mage=43.3±18.2). Health‐related QoL was assessed through the RAND 36‐item Health Survey (SF‐36).5 The Positive Health Behaviors scale6 was used to assess engagement in healthy activities. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale.7‐8 T‐tests were used to assess change in the measures per group pre‐post intervention. Cohen’s d was used to calculate effect sizes.

Results

There were statistically significant increases in depressive symptoms pre‐post in both the intervention group (PreM=14.1±13.3, PostM=22.5±14.9, p=.007, d=1.32) and the control (PreM=5.5±3.6, PostM=14.0±10.0, p=.046, d=1.08). There were statically significant decreases in the emotional well‐being domain of the SF‐36 questionnaire in the control group only (PreM=79.3±18.5, PostM=67.3±15.5, p=.030, d=1.23). Cohen’s d values showed medium effect sizes showing a positive directionality in the physical activity domain of the health behavior scale for the intervention group only (PreM=5.50±4.90, PostM=7.75±4.03, p=.152, d=0.57).

Conclusion

Participants in the intervention demonstrated medium effects for the physical activity domain, suggesting an increase in physical activity. However, findings also indicate increased depressive symptoms and poorer health‐related quality of life. Health education interventions promoting health behavior changes in Latina ADRD family caregivers might benefit from including more extensive problem solving and coping strategies to improve depressive symptoms and QoL.