Patterns Of Gray Matter Atrophy Research Articles

Relationship between brain white matter damage and grey matter atrophy in hereditary spastic paraplegia types 4 and 5.

White matter (WM) damage is the main target of hereditary spastic paraplegia (HSP), but mounting evidence indicates that genotype-specific grey matter (GM) damage is not uncommon. Our aim was to identify and compare brain GM and WM damage patterns in HSP subtypes and investigate how gene expression contributes to these patterns, and explore the relationship between GM and WM damage. In this prospective single-centre cohort study from 2019 to 2022, HSP patients and controls underwent magnetic resonance imaging evaluations. The alterations of GM and WM patterns were compared between groups by applying a source-based morphometry approach. Spearman rank correlation was used to explore the associations between gene expression and GM atrophy patterns in HSP subtypes. Mediation analysis was conducted to investigate the interplay between GM and WM damage. Twenty-one spastic paraplegia type 4 (SPG4) patients (mean age 50.7 years ± 12.0 SD, 15 men), 21 spastic paraplegia type 5 (SPG5) patients (mean age 29.1 years ± 12.8 SD, 14 men) and 42 controls (sex- and age-matched) were evaluated. Compared to controls, SPG4 and SPG5 showed similar WM damage but different GM atrophy patterns. GM atrophy patterns in SPG4 and SPG5 were correlated with corresponding gene expression (ρ = 0.30, p = 0.008, ρ = 0.40, p < 0.001, respectively). Mediation analysis indicated that GM atrophy patterns were mediated by WM damage in HSP. Grey matter atrophy patterns were distinct between SPG4 and SPG5 and were not only secondary to WM damage but also associated with disease-related gene expression. NCT04006418.

Association Between Serum NT-proBNP and Gray Matter Atrophy Patterns in an Older Japanese Population: The Hisayama Study.

Several population-based studies have reported that higher serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels are associated with brain morphological changes. However, no population-based studies have examined the relationship between serum NT-proBNP and various regional brain volumes in detail. We here analyzed the brain MRI data of 1201 community-dwelling Japanese aged ≥65 years. Regional gray matter volumes (GMV) and intracranial volume (ICV) were estimated by applying voxel-based morphometry (VBM) methods. The associations of serum NT-proBNP with regional GMV/ICV were examined by analysis of covariance. The regional gray matter atrophy patterns associated with elevated serum NT-proBNP levels were investigated using VBM without a priori regions of interest. The multivariable-adjusted means of the frontal, temporal, hippocampal, parahippocampal, and entorhinal GMV/ICV decreased significantly with elevated serum NT-proBNP levels (all p for trend and q values of false discovery rate correction < .05). In VBM, elevated serum NT-proBNP levels were correlated with atrophy of the bilateral hippocampi, bilateral amygdalas, bilateral parahippocampal gyri, bilateral entorhinal areas, bilateral fusiform gyri, left middle temporal gyrus, left inferior temporal gyrus, right central operculum, right posterior orbital gyrus, bilateral middle frontal gyri, anterior cingulate gyrus and bilateral medial frontal cortices. In a sensitivity analysis excluding 254 participants with mild cognitive impairment or dementia, serum NT-proBNP levels were correlated with atrophy of the bilateral hippocampi, bilateral amygdalas, bilateral parahippocampal gyri, bilateral fusiform gyri, and left middle frontal gyrus. Our data suggest that elevated serum NT-proBNP levels are associated with gray matter atrophy in brain regions that play an important role in cognitive function.

Structural gray matter changes in primary progressive aphasia variants

BACKGROUND: Primary progressive aphasia is a rare neurodegenerative disease with high clinical, genetic, and pathomorphological heterogeneity that greatly complicates its diagnosis. Voxel-based morphometry can be used to objectively assess structural gray matter changes and determine atrophy patterns in variants of primary progressive aphasia, which can improve the diagnosis and our understanding of its pathogenesis. AIMS: This study aimed to evaluate the patterns of atrophy in each of the primary progressive aphasia variants in comparison with the control group. MATERIALS AND METHODS: Patients diagnosed with one of the primary progressive aphasia variants, established in accordance with the current diagnostic criteria, were included in the main group. The control group consisted of healthy volunteers without any neurological symptoms or structural brain changes. All participants underwent brain magnetic resonance imaging. The obtained images were processed and used for voxel-based morphometry, which was performed by comparing the gray matter volume between each of the primary progressive aphasia variants and the control group. The study was adjusted for the sex, age, and intracranial volume of the participants. RESULTS: The study enrolled 25 patients with nonfluent, 11 with semantic, and 9 with logopenic variants of primary progressive aphasia, as well as 20 healthy volunteers. Voxel-based morphometry showed a specific atrophy pattern in each of the variants of primary progressive aphasia, with predominant involvement of the frontal and insular lobes in nonfluent, temporal lobe and hippocampus in semantic, and a more diffuse frontotemporal pattern in logopenic variants. CONCLUSIONS: The study revealed gray matter atrophy patterns specific to each variant of primary progressive aphasia. The obtained results mainly correspond to the clinical presentations of the disease. Moreover, some findings (e.g., absence of the posterior perisylvian atrophy and reduced motor cortex volume in the logopenic variant, atrophy of the orbitofrontal cortex and cerebellum in the nonfluent variant, and premotor cortex, precentral, and inferior frontal gyrus degeneration in the semantic variant) do not correlate with the usual understanding of primary progressive aphasia pathogenesis and require further study.

The MRI Analysis of 300 Subjects Revealed Grey Matter Atrophy of the Frontal Lobe and Limbic System in Four SARS-CoV-2 Strains (S21.004)

<h3>Objective:</h3> To compare patterns of grey matter atrophy (GMA) in post-COVID syndrome according to four different strains of SARS-CoV-2 <h3>Background:</h3> The neuropsychiatric symptoms of the post-COVID syndrome are frequent and debilitating and represents a major public health concern. Unfortunately, neither the physiopathology nor the underlying cerebral alterations are understood. <h3>Design/Methods:</h3> We analyzed 3T T1 MRI of 300 post-COVID subjects (non-hospitalized, median age 40 years, 209 women, an average of 150 days from diagnosis): 188 infected with the Alpha strain, 29 infected with the P.2-Gamma strain, 61 infected with the P.1+P.1.*-Gamma strain, 22 infected with the Delta strain and 90 healthy controls scanned during the pandemic. We used the Fiocruz database (http://www.genomahcov.fiocruz.br/dashboard/) to separate groups according to the predominant strains by intervals. The CAT12-toolbox/SPM12/MATLAB2019 was used to perform Voxel-Based Morphometry (VBM) analysis and investigate GMA among groups (adjusted for intracranial volume, age, and sex). We reported significant p-values&lt;0.001 corrected for multiple comparisons. <h3>Results:</h3> Overall, all four groups of strains presented GMA atrophy of the bilateral anterior cingula and ventromedial frontal lobes. Besides, Strain P.2-Gamma showed GMA of the right cuneus and left superior occipital gyrus. The P.1+P.1.*-Gamma Strain presented more widespread atrophy (right angular gyrus, fusiform gyrus, frontal operculum and precuneus, and left frontal operculum and precuneus). Interestingly, the Alpha and Delta Strains revealed exclusive frontal lobe GMA. <h3>Conclusions:</h3> Despite the mild acute infection, all four groups presented GMA of the anterior cingula and ventromedial frontal area, suggesting similarities in neurotropism, cerebral injury, and physiopathology. Additionally, different strains showed individual variations of GMA. These findings. The atrophy of the limbic system and frontal lobes may be somehow associated with the highly prevalent neuropsychiatric symptoms (dysexecutive syndrome, depression, and anxiety) in the post-COVID syndrome. Further analyses are necessary to correlate brain atrophy with neuropsychiatric dysfunction and eventually provide therapeutic targets. <b>Disclosure:</b> Lucas Silva has nothing to disclose. Miss Costa has nothing to disclose. Mr. Guimaraes Correa has nothing to disclose. Prof. João has received research support from FAPESP. Mateus Nogueira has nothing to disclose. Dr. Karmann Aventurato has received research support from Coordenadoria de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES). Dr. Alvim has nothing to disclose. Mariana Brito has nothing to disclose. Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for UCB Pharma. Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for UCB Biopharma. Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for United Medical – Brazil. Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Zodiac Pharma . Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Eurofarma – Brazil . Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Epilepsia. Dr. Cendes has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Frontiers in Neurology - Epilepsy. The institution of Dr. Cendes has received research support from São Paulo Research Foundation - FAPESP. The institution of Dr. Cendes has received research support from Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brazil . The institution of Dr. Cendes has received research support from NIH. Clarissa Yasuda has nothing to disclose.

Stepwise functional brain architecture from disease epicenter correlates with atrophy in progressive supranuclear palsy (P11-6.001)

<h3>Objective:</h3> This study applied SFC to test the hypothesis that topological stepwise architecture propagating from the disease epicenter would shape patterns of grey matter (GM) atrophy in a cohort of patients with progressive supranuclear palsy (PSP). <h3>Background:</h3> MRI connectomics is an ideal tool to test the model of network-based spread of pathological protein aggregates in neurodegenerative disorders. Stepwise functional connectivity (SFC) is a graph-theory-based neuroimaging method, which detects whole-brain functional couplings of a selected region of interest, at increasing link-step topological distances. <h3>Design/Methods:</h3> Twenty-eight patients with PSP and 50 healthy controls underwent brain magnetic resonance imaging (MRI) on a 3T scanner, including 3D-T1 weighted and resting-state functional MRI sequences. GM was parcellated into 90 regions using the Automated Anatomical Labeling (AAL) atlas. Correlations between SFC architecture in controls and atrophy patterns in PSP patients were tested. The disease epicenter was identified as the peak of atrophy observed in an independent cohort of 13 cases with <i>post mortem</i> confirmation of PSP pathology, and used as seed region for SFC analysis. <h3>Results:</h3> The disease epicenter was identified in the left midbrain tegmental region. Compared with controls, PSP patients showed prevalent atrophy in the subcortical GM (mostly, in the thalami and caudate nuclei), but also in frontal, parietal and cerebellar cortical regions. For each region of the AAL atlas, a strong correlation was found between average link-step distance from the left midbrain in controls and mean normalized GM volume in PSP patients (r=0.37, p&lt;0.001). <h3>Conclusions:</h3> Our findings demonstrate that the brain architectural topology, as described by SFC propagating from the disease epicenter, shapes the pattern of atrophic changes in PSP, supporting the view of a network-based pathology propagation in this disease and holding the promise to be used to model disease progression in future longitudinal studies. <b>Disclosure:</b> Dr. Spinelli has nothing to disclose. Miss Ghirelli has nothing to disclose. Miss Bottale has nothing to disclose. Silvia Basaia has nothing to disclose. Ms. Cividini has nothing to disclose. Dr. Volonte has nothing to disclose. Mr. Galantucci has nothing to disclose. Giuseppe Magnani has nothing to disclose. Dr. Caso has nothing to disclose. The institution of Elisa Canu has received research support from Italian Ministry of Health . Dr. Castelnovo has nothing to disclose. Paola Caroppo has nothing to disclose. Sara Prioni has nothing to disclose. Miss Villa has nothing to disclose. Dr. Josephs has nothing to disclose. Dr. Whitwell has nothing to disclose. Federica Agosta has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Philips. Federica Agosta has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier INC. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Bayer, Biogen Idec, Merck-Serono, Novartis, Roche, Sanofi Genzyme, Takeda, and Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, Teva Pharmaceutical Industries, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA).

PASSED: Brain atrophy in non-demented individuals in a long-term longitudinal study from two independent cohorts.

Alzheimer's disease (AD) is indicated by a decrease in amyloid beta 42 (Aβ42) level or the Aβ42/Aβ40 ratio, and by increased levels of Tau with phosphorylated threonine at position 181 (pTau181) in cerebrospinal fluid (CSF) years before the onset of clinical symptoms. However, once only pTau181 is increased, cognitive decline in individuals with subjective or mild cognitive impairment is slowed compared to individuals with AD. Instead of a decrease in Aβ42 levels, an increase in Aβ42 was observed in these individuals, leading to the proposal to refer to them as nondemented subjects with increased pTau-levels and Aβ surge with subtle cognitive deterioration (PASSED). In this study, we determined the longitudinal atrophy rates of AD, PASSED, and Biomarker-negative nondemented individuals of two independent cohorts to determine whether these groups can be distinguished by their longitudinal atrophy patterns or rates. Depending on their CSF-levels of pTau 181 (T), total Tau (tTau, N), Aβ42 or ratio of Aβ42/Aβ40 (A), 185 non-demented subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 62 non-demented subjects from Erlangen AD cohort were assigned to an ATN group (A-T-N-, A-T+N±, A+T-N±and A+T+N±) and underwent T1-weighted structural magnetic resonance imaging (sMRI). Longitudinal grey matter (GM) atrophy patterns were assessed with voxel-based morphometry (VBM) using the cat12 toolbox on spm12 (statistical parametric mapping) of MRI scans from individuals in the ADNI cohort with a mean follow-up of 2 and 5 years, respectively. The annualized atrophy rate for individuals in the Erlangen cohort was determined using region of interest analysis (ROI) in terms of a confirmatory analysis. In the A-T+N± group, VBM did not identify any brain region that showed greater longitudinal atrophy than the A+T+N±, A+T+N± or biomarker negative control group. In contrast, marked longitudinal atrophy in the temporal lobe was evident in the A+T-N± group compared with A+T-N± and biomarker-negative subjects. The ROI in the angular gyrus identified by VBM analysis of the ADNI cohort did not discriminate better than the hippocampal volume and atrophy rate between AD and PASSED in the confirmatory analysis. In this study, nondemented subjects with PASSED did not show a unique longitudinal atrophy pattern in comparison to nondemented subjects with AD. The nonsignificant atrophy rate compared with controls suggests that increased pTau181-levels without concomitant amyloidopathy did not indicate a neurodegenerative disorder.

Study of gray matter atrophy pattern with subcortical ischemic vascular disease-vascular cognitive impairment no dementia based on structural magnetic resonance imaging.

This study explored the structural imaging changes in patients with subcortical ischemic vascular disease (SIVD)-vascular cognitive impairment no dementia (VCIND) and the correlation between the changes in gray matter volume and the field of cognitive impairment to provide new targets for early diagnosis and treatment. Our study included 15 patients with SIVD-normal cognitive impairment (SIVD-NCI), 63 with SIVD-VCIND, 26 with SIVD-vascular dementia (SIVD-VD), and 14 normal controls (NC). T1-weighted images of all participants were collected, and DPABI and SPM12 software were used to process the gray matter of the four groups based on voxels. Fisher's exact test, one-way ANOVA and Kruskal-Wallis H test were used to evaluate all clinical and demographic data and compare the characteristics of diencephalic gray matter atrophy in each group. Finally, the region of interest (ROI) of the SIVD-VCIND was extracted, and Pearson correlation analysis was performed between the ROI and the results of the neuropsychological scale. Compared to the NC, changes in gray matter atrophy were observed in the bilateral orbitofrontal gyrus, right middle temporal gyrus, superior temporal gyrus, and precuneus in the SIVD-VCIND. Gray matter atrophy was observed in the left cerebellar region 6, cerebellar crural region 1, bilateral thalamus, right precuneus, and calcarine in the SIVD-VD. Compared with the SIVD-VCIND, gray matter atrophy changes were observed in the bilateral thalamus in the SIVD-VD (p < 0.05, family-wise error corrected). In the SIVD-VCIND, the total gray matter volume, bilateral medial orbital superior frontal gyrus, right superior temporal gyrus, middle temporal gyrus, and precuneus were positively correlated with Boston Naming Test score, whereas the total gray matter volume, right superior temporal gyrus, and middle temporal gyrus were positively correlated with overall cognition. Structural magnetic resonance imaging can detect extensive and subtle structural changes in the gray matter of patients with SIVD-VCIND and SIVD-VD, providing valuable evidences to explain the pathogenesis of subcortical vascular cognitive impairment and contributing to the early diagnosis of SIVD-VCIND and early warning of SIVD-VD.

Structural atrophy and functional dysconnectivity patterns in the cerebellum relate to cerebral networks in svMCI.

Subcortical vascular mild cognitive impairment (svMCI) is associated with structural and functional changes in the cerebral cortex affecting major brain networks. While recent studies have shown that the intrinsic cerebral connectivity networks can be mapped onto the cerebellum, and the cortex and cerebellum are interconnected via the cortico-basal ganglia-cerebellar circuit, structural and functional disruptions in cerebellum in svMCI are rarely studied. In this study, we conducted voxel-based morphometry analysis to investigate gray matter atrophy pattern across cerebellar regions in 40 svMCI patients, and explored alterations in functional connectivity between the basal ganglia and cerebellum. The results showed that the amount of cerebellar atrophy within the default mode, salience, and frontoparietal networks correlated with their counterpart in the cerebral cortex. Moreover, key regions of the cerebellum, including the lobule VI, VIIb, VIII, and Crus I, which are reported to have a role in cognitive function, showed both anatomical atrophy and decreased functional connectivity with the striatum. These atrophy and connectivity patterns in the cerebellum also correlated with memory performances. These findings demonstrate that there are coupled changes in cerebral and cerebellar circuits, reflecting that degeneration patterns in svMCI are not limited to the cerebral cortex but similarly extend to the cerebellum as well, and suggest the cortico-basal ganglia-cerebellar circuit may play an important role in the pathology of svMCI.

Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease.

Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.

Cervical spinal cord atrophy in amyotrophic lateral sclerosis across disease stages.

Spinal cord degeneration is a hallmark of amyotrophic lateral sclerosis. The assessment of gray matter and white matter cervical spinal cord atrophy across clinical stages defined using the King's staging system could advance the understanding of amyotrophic lateral sclerosis progression. We assessed the invivo spatial pattern of gray and white matter atrophy along cervical spinal cord (C2 to C6 segments) using 2D phase-sensitive inversion recovery imaging in a cohort of 44 amyotrophic lateral sclerosis patients, evaluating its change across the King's stages and the correlation with disability scored by the amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) and disease duration. A mathematical model inferring the potential onset of cervical gray matter atrophy was developed. In amyotrophic lateral sclerosis patients at King's stage 1, significant cervical spinal cord alterations were mainly identified in gray matter, whereas they involved both gray and white matter in patients at King's stage ≥ 2. Gray and white matter areas correlated with clinical disability at all cervical segments. C3-C4 level was the segment showing early gray matter atrophy starting about 7 to 20months before symptom onset according to our model. Our findings suggest that cervical spinal cord atrophy spreads from gray to white matter across King's stages in amyotrophic lateral sclerosis, making spinal cord magnetic resonance imaging an invivo assessment tool to measure the progression of the disease.

Anterior horn atrophy in the cervical spinal cord: A new biomarker in progressive multiple sclerosis

Background: Spinal cord (SC) gray and white matter pathology plays a central role in multiple sclerosis (MS). Objective: We aimed to investigate the extent, pattern, and clinical relevance of SC gray and white matter atrophy in vivo. Methods: 39 relapsing–remitting patients (RRMS), 40 progressive MS patients (PMS), and 24 healthy controls (HC) were imaged at 3T using the averaged magnetization inversion recovery acquisitions sequence. Total and lesional cervical gray and white matter, and posterior (SCPH) and anterior horn (SCAH) areas were automatically quantified. Clinical assessment included the expanded disability status scale, timed 25-foot walk test, nine-hole peg test, and the 12-item MS walking scale. Results: PMS patients had significantly reduced cervical SCAH — but not SCPH — areas compared with HC and RRMS (both p < 0.001). In RRMS and PMS, the cervical SCAH areas increased significantly less in the region of cervical SC enlargement compared with HC (all p < 0.001). This reduction was more pronounced in PMS compared with RRMS (both p < 0.001). In PMS, a lower cervical SCAH area was the most important magnetic resonance imaging (MRI)-variable for higher disability scores. Conclusion: MS patients show clinically relevant cervical SCAH atrophy, which is more pronounced in PMS and at the level of cervical SC enlargement.

Emotional Recognition and Its Relation to Cognition, Mood and Fatigue in Relapsing–Remitting and Secondary Progressive Multiple Sclerosis

(1) Background: Emotional recognition (ER), the ability to read into others' minds and recognize others' emotional states, is important in social environment adaptation. Recently it has been found that ER difficulties affect patients with multiple sclerosis (pMS) and relate to different gray matter atrophy patterns from secondary progressive (SP-pMS) and relapsing-remitting (RR-pMS). The aim of this study was to compare the performances of the two MS phenotypes on the Reading the Mind in the Eyes test (RMEt) and other cognitive, mood and fatigue measures. We also examined associations between performance on the RMEt and cognitive, mood and fatigue variables. (2) Methods: A total of 43 pMS (27RR-pMS/16SP-pMS) underwent a clinical assessment, the RMEt, the cognitive battery, and completed mood and fatigue questionnaires. Both groups' performances on the RMEt were then correlated with all these measures. (3) Results: the RMEt scores of RR-pMS were significantly correlated with the impairment degree in some cognitive scores. SP-pMS scores correlated mainly with fatigue, anxiety, anger and depression. (4) Conclusions: ER performances relate to cognitive aspects in RR-pMS, whereas mainly to mood outcomes in the SP-pMS group. We can hypothesize that deficits in ER are a further sign of disease progression. Our data support the different roles of cognitive and emotional deficits related to different disease courses and lesional correlates.

G-RMOS: GPU-accelerated Riemannian Metric Optimization on Surfaces

Surface mapping is used in various brain imaging studies, such as for mapping gray matter atrophy patterns in Alzheimer’s disease. Riemannian metrics on surface (RMOS) is a state-of-the-art surface mapping algorithm that optimizes Riemannian metrics to establish one-to-one correspondences between surfaces in the Laplace–Beltrami embedding space. However, owing to the complex calculation with accurate one-to-one correspondences, RMOS registration takes a long time. In this study, we propose G-RMOS, a graphics processing unit (GPU)-accelerated RMOS registration pipeline that uses three GPU kernel design strategies: 1. using GPU computing capability with a batch scheme; 2. using the cache in the GPU block to minimize memory latency in register and shared memory; and 3. maximizing the effective number of instructions per GPU cycle using instruction level parallelism. Using the experimental results, we compare the acceleration speed of the G-RMOS framework with that of RMOS using hippocampus and cortical surfaces, and show that G-RMOS achieves a significant speedup in surface mapping. We also compare the memory requirements for cortical surface mapping and show that G-RMOS uses less memory than RMOS.

Association Between Diabetes and Gray Matter Atrophy Patterns in a General Older Japanese Population: The Hisayama Study.

To examine the association between diabetes and gray matter atrophy patterns in a general older Japanese population. In 2012, a total of 1,189 community-dwelling Japanese aged ≥65 years underwent brain MRI scans. Regional gray matter volumes (GMV) and intracranial volume (ICV) were measured by applying voxel-based morphometry (VBM) methods. The associations of diabetes and related parameters with the regional GMV/ICV were examined using an ANCOVA. The regional gray matter atrophy patterns in the subjects with diabetes or elevated fasting plasma glucose (FPG) or 2-h postload glucose (2hPG) levels were investigated using VBM. Subjects with diabetes had significantly lower mean values of GMV/ICV in the frontal lobe, temporal lobe, insula, deep gray matter structures, and cerebellum than subjects without diabetes after adjusting for potential confounders. A longer duration of diabetes was also significantly associated with lower mean values of GMV/ICV in these brain regions. The multivariable-adjusted mean values of the temporal, insular, and deep GMV/ICV decreased significantly with elevating 2hPG levels, whereas higher FPG levels were not significantly associated with GMV/ICV of any brain regions. In the VBM analysis, diabetes was associated with gray matter atrophy in the bilateral superior temporal gyri, right middle temporal gyrus, left inferior temporal gyrus, right middle frontal gyrus, bilateral thalami, right caudate, and right cerebellum. The current study suggests that a longer duration of diabetes and elevated 2hPG levels are significant risk factors for gray matter atrophy in various brain regions.

Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume.

BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer’s disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.

Magnetic Resonance Imaging Markers for Cognitive Impairment in Parkinson's Disease: Current View.

Cognitive impairment (CI) ranging from mild cognitive impairment (MCI) to dementia is a common and disturbing complication in patients with Parkinson’s disease (PD). Numerous studies have focused on neuropathological mechanisms underlying CI in PD, along with the identification of specific biomarkers for CI. Magnetic resonance imaging (MRI), a promising method, has been adopted to examine the changes in the brain and identify the candidate biomarkers associated with CI. In this review, we have summarized the potential biomarkers for CI in PD which have been identified through multi-modal MRI studies. Structural MRI technology is widely used in biomarker research. Specific patterns of gray matter atrophy are promising predictors of the evolution of CI in patients with PD. Moreover, other MRI techniques, such as MRI related to small-vessel disease, neuromelanin-sensitive MRI, quantitative susceptibility mapping, MR diffusion imaging, MRI related to cerebrovascular abnormality, resting-state functional MRI, and proton magnetic resonance spectroscopy, can provide imaging features with a good degree of prediction for CI. In the future, novel combined biomarkers should be developed using the recognized analysis tools and predictive algorithms in both cross-sectional and longitudinal studies.

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&lt;.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.

Learning to synthesise the ageing brain without longitudinal data

How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data. Our method synthesises images conditioned on two factors: age (a continuous variable), and status of Alzheimer’s Disease (AD, an ordinal variable). With an adversarial formulation we learn the joint distribution of brain appearance, age and AD status, and define reconstruction losses to address the challenging problem of preserving subject identity. We compare with several benchmarks using two widely used datasets. We evaluate the quality and realism of synthesised images using ground-truth longitudinal data and a pre-trained age predictor. We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at https://github.com/xiat0616/BrainAgeing.

Amyotrophic lateral sclerosis features predict TDP-43 pathology in frontotemporal lobar degeneration

Clinical and pathological heterogeneity is common in patients with frontotemporal lobar degeneration (FTLD) pathology. This investigated clinical or imaging characteristics that differentiate FTLD-TDP from FTLD-tau, FTLD-TDP subtypes from each other, or pathological stages of FTLD-TDP. Initial clinical, neuropsychological and neuroimaging characteristics were compared between pathologically defined FTLD-tau and FTLD-TDP groups. Voxel-based morphometry analyses contrasted grey matter atrophy patterns. Twenty-six FTLD-TDP, 28 FTLD-tau and 78 controls were included. Amyotrophic lateral sclerosis features, when present, were highly specific FTLD-TDP, which displayed greater cortical and subcortical atrophy than FTLD-tau. FTLD-TDP-43 type B had significantly shorter survival than type A. Type A patients were more cognitively impaired than type B, and basal ganglia atrophy appeared to distinguish type A from type B. Age at onset and survival duration were comparable between stages II and IV. In conclusion, Amyotrophic lateral sclerosis features may be useful in distinguishing FTLD-TDP from FTLD-tau. TDP-43 type A and B appear to present with distinct profiles. The relationship between clinical features and pathological staging in FTLD-TDP-43 is complex, and TDP-43 subtyping may have more clinical utility.

Primary Progressive Aphasia Associated With GRN Mutations: New Insights Into the Nonamyloid Logopenic Variant.

To determine relative frequencies and linguistic profiles of primary progressive aphasia (PPA) variants associated with GRN (progranulin) mutations and to study their neuroanatomic correlates. Patients with PPA carrying GRN mutations (PPA-GRN) were selected among a national prospective research cohort of 1,696 patients with frontotemporal dementia, including 235 patients with PPA. All patients with amyloid-positive CSF biomarkers were excluded. In this cross-sectional study, speech/language and cognitive profiles were characterized with standardized evaluations, and gray matter (GM) atrophy patterns using voxel-based morphometry. Comparisons were performed with controls and patients with sporadic PPA. Among the 235 patients with PPA, 45 (19%) carried GRN mutations, and we studied 32 of these. We showed that logopenic PPA (lvPPA) was the most frequent linguistic variant (n = 13, 41%), followed by nonfluent/agrammatic (nfvPPA; n = 9, 28%) and mixed forms (n = 8, 25%). Semantic variant was rather rare (n = 2, 6%). Patients with lvPPA, qualified as nonamyloid lvPPA, presented canonical logopenic deficit. Seven of 13 had a pure form; 6 showed subtle additional linguistic deficits not fitting criteria for mixed PPA and hence were labeled as logopenic-spectrum variant. GM atrophy involved primarily left posterior temporal gyrus, mirroring neuroanatomic changes of amyloid-positive-lvPPA. Patients with nfvPPA presented agrammatism (89%) rather than apraxia of speech (11%). This study shows that the most frequent PPA variant associated with GRN mutations is nonamyloid lvPPA, preceding nfvPPA and mixed forms, and illustrates that the language network may be affected at different levels. GRN testing is indicated for patients with PPA, whether familial or sporadic. This finding is important for upcoming GRN gene-specific therapies.