Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer’s Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness

Teipel, Stefan J.; Kuper-Smith, Jan Oliver; Bartels, Claudia; Brosseron, Frederic; Buchmann, Martina; Büerger, Katharina; Catak, Cihan; Janowitz, Daniel; Dechent, Peter; Dobisch, Laura; Ertl‐Wagner, Birgit; Fließbach, Klaus; Haynes, John‐Dylan; Heneka, Michael T.; Kilimann, Ingo; Laske, Christoph; Li, Siyao; Metzger, Coraline D.; Priller, Josef; Pross, Verena; Ramı́rez, Alfredo; Scheffler, Klaus; Schneider, Anja; Spottke, Annika; Spruth, Eike Jakob; Wagner, Michael; Wiltfang, Jens; Wolfsgruber, Steffen; Düzel, Emrah; Jessen, Frank; Dyrba, Martin

doi:10.3233/jad-190446

Cited by 12 publications

(12 citation statements)

References 53 publications

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“…Consistent with our expectation, we found an impaired transcallosal inhibition in up to 68% of cases which is in line with previously published data from independent cohorts (3,4). In contrast, however, ALS patients with MMs in the current sample did not show microstructural involvement of the corpus callosum, which is in contrast with some (22,34), but not all previous studies (39). One interpretation of the lack of corpus callosum changes in our study may be that our patients exhibited an earlier functional disturbance potentially preceding the microstructural findings in transcallosal pathways.…”

Section: Discussionsupporting

confidence: 93%

“…For specific analysis of changes in diffusivity of individual fiber tracts we conducted a region of interest (ROI) analysis. The selection of ROIs from JHU was done as described before (34). Based on the JHU white matter atlas (35) we defined seven regions: corpus callosum with subregions genu, truncus, and splenium, corticospinal tract, and crus posterior of the internal capsula on both sides.…”

Section: Mri Data Processingmentioning

confidence: 99%

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Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

et al. 2020

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Objective: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder predominantly affecting the motor system. In a number of patients, mirror movements (MMs) suggest involvement of transcallosal fiber tracts in conjunction with upper motor neuron involvement. The aim of the study was to elucidate functional and structural alterations of callosal integrity in ALS patients with MMs.Methods: Nineteen patients with ALS displaying MMs and 20 controls underwent clinical assessment, transcranial magnetic stimulation (TMS), and diffusion tensor imaging (DTI). TBSS (tract based spatial statistics) was performed. We investigated ipsilateral silent period (iSP) as a measure of transcallosal inhibition, and diffusion changes in the corpus callosum and corticospinal tract (CST) as measure of structural integrity.Results: In ALS patients TMS revealed a longer mean iSP latency than controls. Twelve ALS patients (63.2%) showed loss of iSP, but none of the controls. Using region of interest analysis, fractional anisotropy (FA) values of the CST were significantly lower in ALS patients compared with controls, but diffusion parameters of the corpus callosum did not differ between patients and controls. The lack of diffusion changes in the corpus callosum was confirmed in whole brain tract based statistics, assessing FA as well as mean, radial, and axial diffusivity. There was a significant negative correlation between resting motor threshold and FA values of the CST, but not between iSP and FA of the corpus callosum.Conclusion: In conclusion the study failed to show microstructural changes in the corpus callosum in conjunction with MMs. One possible reason may be that functional disturbance of transcallosal pathways precede microstructural changes in the corpus callosum.

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Section: Discussionsupporting

confidence: 93%

Section: Mri Data Processingmentioning

confidence: 99%

Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

et al. 2020

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“…Also, gradual disturbances among CN, SCD, aMCI, and AD were revealed through correlation analyses between ALFF alterations and neuropsychological assessments ( Yang et al, 2018 ). DTI tractography may assist in distinguishing significantly deteriorated white matter integrity and serve as biomarkers for MCI ( Teipel et al, 2019 ; Zhang et al, 2019 ). SCD suffered from more subtle alterations than MCI, indicating a potential disease progression sequence ( Brueggen et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer’s Disease Stages

Wang

Rao

Yue

et al. 2021

Front. Hum. Neurosci.

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BackgroundSubjective cognitive decline (SCD), non-amnestic mild cognitive impairment (naMCI), and amnestic mild cognitive impairment (aMCI) are regarded to be at high risk of converting to Alzheimer’s disease (AD). Amplitude of low-frequency fluctuations (ALFF) can reflect functional deterioration while diffusion tensor imaging (DTI) is capable of detecting white matter integrity. Our study aimed to investigate the structural and functional alterations to further reveal convergence and divergence among SCD, naMCI, and aMCI and how these contribute to cognitive deterioration.MethodsWe analyzed ALFF under slow-4 (0.027–0.073 Hz) and slow-5 (0.01–0.027 Hz) bands and white matter fiber integrity among normal controls (CN), SCD, naMCI, and aMCI groups. Correlation analyses were further utilized among paired DTI alteration, ALFF deterioration, and cognitive decline.ResultsFor ALFF calculation, ascended ALFF values were detected in the lingual gyrus (LING) and superior frontal gyrus (SFG) within SCD and naMCI patients, respectively. Descended ALFF values were presented mainly in the LING, SFG, middle frontal gyrus, and precuneus in aMCI patients compared to CN, SCD, and naMCI groups. For DTI analyses, white matter alterations were detected within the uncinate fasciculus (UF) in aMCI patients and within the superior longitudinal fasciculus (SLF) in naMCI patients. SCD patients presented alterations in both fasciculi. Correlation analyses revealed that the majority of these structural and functional alterations were associated with complicated cognitive decline. Besides, UF alterations were correlated with ALFF deterioration in the SFG within aMCI patients.ConclusionsSCD shares structurally and functionally deteriorative characteristics with aMCI and naMCI, and tends to convert to either of them. Furthermore, abnormalities in white matter fibers may be the structural basis of abnormal brain activation in preclinical AD stages. Combined together, it suggests that structural and functional integration may characterize the preclinical AD progression.

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“…These developments prove the accuracy of locations, support our results that these features were correlated with SUVR values, played good roles in predicting future cognitive decline (AUCs 64.9%, 72.9% and 76.1% respectively; 83.9% when combined), and probably being the imaging biomarkers of preclinical AD. Interestingly, we found the retained features only came from sMRI modality, this is probably in part due to the relatively small number of fMRI and DTI features, additionally, a latest study concluded that DTI parameters is not useful for the identi cation of preclinical AD [58] , and to the best of our knowledge, it is the rst time that texture analysis of fMRI have been applied to the eld of AD [36] . Uncertainty still exists, the signi cance of DTI and fMRI radiomics features cannot be completely denied in the exploratory study.…”

Section: Discussionmentioning

confidence: 81%

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

Li¹,

Wu²,

Jiang³

et al. 2020

Preprint

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Background: Diagnosing Alzheimer’s disease (AD) in the preclinical stage offers opportunities to early intervention, however, there is a lack of convenient biomarkers currently. By using the methods of radiomics analysis, we aimed to determine whether the features extracted from multi-parameter magnetic resonance imaging (MRI) can be used as potential biomarkers. Methods: This study is part of the SILCODE project (NCT03370744). All participants were cognitively healthy at baseline. The cohort 1 (n=183) was divided into individuals with preclinical AD (n=78) and controls (n=105) by amyloid-positron emission tomography, used as the training dataset (80%) and validation dataset (the rest 20%); cohort 2 (n=51) was divided into “converters” and “non-converters” by individuals’ future cognitive status, used as a separate test dataset; cohort 3 included 37 “converters” (13 from ADNI), was used as another test set for independent longitudinal researches. We extracted radiomics features from multi-parameter MRI of each participant, used t-tests, autocorrelation tests, and three independent selection algorithms, respectively, to select features. Then we established two classification models (support vector machine (SVM) and random forest (RF)) to verify the efficiency of retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort 3 by paired two-sample t-tests and survival analyses, in order to identify whether their levels change with cognitive decline and impact conversion time. Results: The SVM and RF models both showed excellent classification efficiency, with the average accuracy of 89.65%-95.90% and 87.07%-90.81% respectively in the validation set, 81.86%-89.10% and 83.19%-83.68% respectively in the test set. Three stable high-frequency features were identified, namely Large zone high-gray-level emphasis feature of right posterior cingulate gyrus, Variance feature of left superior parietal gyrus and Coarseness feature of left posterior cingulate gyrus, all based on structural MRI modality; their levels were correlated with amyloid-β deposition, played good roles in predicting future cognitive decline (AUCs 64.9%-76.1%). In addition, levels of the Variance feature at baseline timepoint decreased with cognitive decline, and can affect the conversion time (p<0.05). Conclusion: In this exploratory study, we show radiomics features of multi-parameter MRI can be potential biomarkers of preclinical AD.

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Multicenter Tract-Based Analysis of Microstructural Lesions within the Alzheimer’s Disease Spectrum: Association with Amyloid Pathology and Diagnostic Usefulness

Cited by 12 publications

References 53 publications

Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

Mirror Movements in Amyotrophic Lateral Sclerosis: A Combined Study Using Diffusion Tensor Imaging and Transcranial Magnetic Stimulation

Altered Frequency-Dependent Brain Activation and White Matter Integrity Associated With Cognition in Characterizing Preclinical Alzheimer’s Disease Stages

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

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