Correlation of Hemispatial Neglect with White Matter Tract Integrity: A DTI Study

Kwon, Bo Mi; Lee, Jin-Youn; Ko, Nayeon; Kim, Bo-Ram; Moon, Won-Jin; Choi, Dong-Hee; Lee, Jongmin

doi:10.12786/bn.2022.15.e6

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Therefore, WM degeneration is an effective biomarker for MCI. Diffusion tensor imaging (DTI) is an important tool for studying the microstructure of the brain, which can examine the fine structural changes in WM fibers and provide highly specific anatomical information [5][6][7]. DTI diffusion tensor parameters include fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and so on.…”

Section: Introductionmentioning

confidence: 99%

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Yang,

Xia,

et al. 2023

Applied Sciences

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Alzheimer’s disease (AD) is one of the most common irreversible brain diseases in the elderly. Mild cognitive impairment (MCI) is an early symptom of AD, and the early intervention of MCI may slow down the progress of AD. However, due to the subtle neuroimaging differences between MCI and normal control (NC), the clinical diagnosis is subjective and easy to misdiagnose. Machine learning can extract depth features from neural images, and analyze and label them to assist the diagnosis of diseases. This paper combines diffusion tensor imaging (DTI) and support vector machine (SVM) to classify AD, MCI, and NC. First, the white matter connectivity network was constructed based on DTI. Second, the nodes with significant differences between groups were screened out by the two-sample t-test. Third, the optimal feature subset was selected as the classification feature by recursive feature elimination (RFE). Finally, the Gaussian kernel support vector machine was used for classification. The experiment tested and verified the data downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the area under the curve (AUC) of AD/MCI and MCI/NC are 0.94 and 0.95, respectively, which have certain competitive advantages compared with other methods.

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Section: Introductionmentioning

confidence: 99%

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Yang,

Xia,

et al. 2023

Applied Sciences

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The relationship between fractional amplitude of low-frequency fluctuations (fALFF) and the severity of neglect in patients with unilateral spatial neglect (USN) after stroke: A functional near-infrared spectroscopy study

Shi,

Meng,

et al. 2025

IBRO Neuroscience Reports

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Severity-Dependent Interhemispheric White Matter Connectivity Predicts Poststroke Neglect Recovery

Kaufmann,

Pastore-Wapp,

Bartolomeo

et al. 2024

J. Neurosci.

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Left-sided spatial neglect is a very common and challenging issue after right-hemispheric stroke, which strongly and negatively affects daily living behaviour and recovery of stroke survivors. The mechanisms underlying recovery of spatial neglect remain controversial, particularly regarding the involvement of the intact, contralesional hemisphere, with potential contributions ranging from maladaptive to compensatory. In the present prospective, observational study, we assessed neglect severity in 54 right-hemispheric stroke patients (32 male; 22 female) at admission to and discharge from inpatient neurorehabilitation. We demonstrate that the interaction of initial neglect severity, and spared white matter (dis) connectivity resulting from individual lesions (as assessed by diffusion tensor imaging, DTI) explains a significant portion of the variability of post-stroke neglect recovery. In mildly impaired patients, spared structural connectivity within the lesioned hemisphere is sufficient to attain good recovery. Conversely, in patients with severe impairment, successful recovery critically depends on structural connectivity within the intact hemisphere and between hemispheres. These distinct patterns, mediated by their respective white matter connections, may help to reconcile the dichotomous perspectives regarding the role of the contralesional hemisphere as exclusively compensatory or not. Instead, they suggest a unified viewpoint wherein the contralesional hemisphere can – but must not necessarily - assume a compensatory role. This would depend on initial impairment severity and on the available, spared structural connectivity. In the future, our findings could serve as a prognostic biomarker for neglect recovery and guide patient-tailored therapeutic approaches.Significance StatementVisuospatial neglect is a common and challenging issue affecting the daily living of stroke survivors. Mechanisms underlying the recovery of neglect, especially the contribution of the intact hemisphere, remain controversial, ranging from maladaptive to compensatory. In 54 neglect patients, we show that a tight interaction of initial neglect severity and structural (dis)connectivity profiles relate to good recovery: in mild neglect, spared ipsilesional structural connectivity is sufficient for good recovery; conversely, in more severe neglect, structural connectivity within the contralesional hemisphere and between hemispheres plays a central role. These findings may help to reconcile rival models concerning the role of the contralesional hemisphere in neglect recovery after stroke. Furthermore, they could serve as a prognostic biomarker and guide patient-tailored therapeutic approaches.

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Correlation of Hemispatial Neglect with White Matter Tract Integrity: A DTI Study

Cited by 3 publications

References 33 publications

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

Classification of Alzheimer’s Disease Based on White Matter Connectivity Network

The relationship between fractional amplitude of low-frequency fluctuations (fALFF) and the severity of neglect in patients with unilateral spatial neglect (USN) after stroke: A functional near-infrared spectroscopy study

Severity-Dependent Interhemispheric White Matter Connectivity Predicts Poststroke Neglect Recovery

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