Semantic Intrusions and Failure to Recover From Semantic Interference in Mild Cognitive Impairment: Relationship to Amyloid and Cortical Thickness

Curiel, Rosie E.; Loewenstein, David; Rosselli, Mónica; Peñate, Ailyn; Greig‐Custo, Maria T.; Bauer, Russell M.; Guinjoan, Salvador M.; Hanson, Kevin; Li, Chunfei; Lizarraga, Gabriel; Barker, William; Torres, Valeria L.; DeKosky, Steven T.; Adjouadi, Malek; Duara, Ranjan

doi:10.2174/1567205015666180427122746

Cited by 16 publications

(8 citation statements)

References 32 publications

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“…There is increasing evidence that semantic intrusion errors are likely the result of incomplete or faulty storage and consolidation of initial to‐be‐remembered information. When a semantically related list of competing information is presented, deficits in inhibitory systems and elective impairments in source memory occur 17,52–54 . We are aware that correlation does not necessarily imply causation and that the current results should not be taken to imply that amyloid in and of itself is responsible for increased PSI and frPSI errors.…”

Section: Discussionmentioning

confidence: 70%

“…When a semantically related list of competing information is presented, deficits in inhibitory systems and elective impairments in source memory occur. 17,[52][53][54] We are aware that correlation does not necessarily imply causation and that the current results should not be taken to imply that amyloid in and of itself is responsible for increased PSI and frPSI errors. In fact, even in early AD, synaptic dysconnectivity, 32 tau deposition, 17 and other brain-related processes may underlie PSI and frPSI errors.…”

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confidence: 75%

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The relationship of semantic intrusions to different etiological subtypes of MCI and cognitively healthy older adults

Kitaigorodsky

Crocco

Curiel-Cid

et al. 2021

Alz & Dem Diag Ass & Dis Mo

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Introduction: There is increasing evidence that susceptibility to proactive semantic interference (PSI) and the failure to recover from PSI (frPSI) as evidenced by intrusion errors may be early cognitive markers of both preclinical and prodromal Alzheimer's disease (AD). Methods: One hundred forty‐five participants were administered extensive clinical and neuropsychological evaluations including the Loewenstein‐Acevedo Scales for Semantic Interference and Learning (LASSI‐L), a sensitive cognitive stress test measuring PSI and frPSI. Participants also underwent structural magnetic resonance imaging (MRI) and amyloid positron emission tomography/computed tomography (PET/CT) imaging. Results: PSI and frPSI errors were much more prevalent in the mild cognitive impairment (MCI)‐AD (amyloid positive) group than the other diagnostic groups. The number of intrusion errors observed across the other MCI groups without amyloid pathology and those with normal cognition were comparable. Discussion: Semantic intrusion errors on the LASSI‐L occur much less frequently in persons who have different types of non–AD‐related MCI and may be used as an early cognitive marker of prodromal AD.

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Section: Discussionmentioning

confidence: 70%

mentioning

confidence: 75%

The relationship of semantic intrusions to different etiological subtypes of MCI and cognitively healthy older adults

Kitaigorodsky

Crocco

Curiel-Cid

et al. 2021

Alz & Dem Diag Ass & Dis Mo

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“…Thus, mean uptake in counts for each of the FreeSurfer defined regions were calculated and global standard uptake value ratio (SUVR) was calculated by averaging cortical uptake in the frontal, parietal, lateral temporal, occipital, and anterior and posterior cingulate regions, normalized to mean gray matter counts in the cerebellum. SUVR values were normalized to the mean cerebellar gray matter, which has minimal amyloid load in AD (Curiel et al, 2018). The typical mean SUVR cut-off for amyloid positivity is 1.42 (Bullich et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Types of errors on a semantic interference task in mild cognitive impairment and dementia.

Torres¹,

Rosselli²,

Loewenstein³

et al. 2019

Neuropsychology

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Objective: This research aimed to determine whether qualitative analysis of different types of intrusion errors on a verbal cognitive task was useful in detecting subtle cognitive impairment in preclinical stages prior to the progression to dementia. Method: Different types of semantic intrusions on the Loewenstein-Acevedo Scales of Semantic Interference and Learning (LASSI-L) were compared across 160 individuals diagnosed as cognitively normal (CN), amnestic Mild Cognitive Impairment (aMCI), and dementia. The sample included Hispanics and non-Hispanic European Americans. Results: Across diagnostic groups, the most common type of intrusion error was actual targets presented from a competing word list under conditions eliciting proactive semantic interference (PSI), and retroactive semantic interference (RSI), followed by intrusions that represented one of three overlapping semantic categories but none of the targets from List A or B. Nonsemantic intrusions rarely occurred. These competing list intrusions (CLI) and semantically related intrusions (SRI) differentiated between aMCI and CN participants. Further, these intrusion error were related to brain amyloid load, indicating their importance as potential primary markers of AD-related neurodegeneration. Ethnicity effects were not seen across the types of intrusion errors. Conclusions: Two types of intrusion errors (CLI and SRI) showed differences between the CN and aMCI group, with the aMCI group evidencing a higher rate of these intrusion errors compared with the CN group. These results support previous literature about the LASSI-L’s sensitivity at the earliest stages of abnormal aging.

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“…It should be noted that the brain regions involved in the significant abnormal connectivity pathways (with over 50 selected frequency times) are located mainly within the default mode network (DMN), including Temporal_Pole_Sup_R, Temporal_Pole_Sup_L, Temporal_Pole_ Mid_R, ParaHippocampal_L, Cingulum_Post_L, Angular_L, Supp_Motor_Area_L, Frontal_ Med_Orb_R, Frontal_Sup_R, Cingulum_Ant_R. These brain regions, listed in Table 4 and displayed in Figure 6C , are reported as highly associated with AD pathology (Rose et al, 2006 ; Matsuda, 2013 ; Salvatore et al, 2015 ; Scheff et al, 2015 ; Xu et al, 2016 ; Loewenstein et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

Liu

Huang

et al. 2018

Front. Neuroinform.

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Background/Aims: Brain functional connectivity networks constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for classifying Alzheimer's disease (AD) from normal controls (NC). However, conventional correlation analysis methods only capture the pairwise information, which may not be capable of revealing an adequate and accurate functional connectivity relationship among brain regions in the whole brain. Additionally, the non-sparse connectivity networks commonly contain a large number of spurious or insignificant connections, which are inconsistent with the sparse connectivity of actual brain networks in nature and may deteriorate the classification performance of Alzheimer's disease.Methods: To address these problems, in this paper, a new classification framework is proposed by combining the Group-constrained topology structure detection with sparse inverse covariance estimation (SICE) method to build the functional brain sub-network for each brain region. Particularly, to tune the sensitive analysis of the regularized parameters in the SICE method, a nested leave-one-out cross-validation (LOOCV) method is adopted. Sparse functional connectivity networks are thus effectively constructed by using the optimal regularized parameters. Finally, a decision classification tree (DCT) classifier is trained for classifying AD from NC based on these optimal functional brain sub-networks. The convergence performance of our proposed method is furthermore evaluated by the trend of coefficient variation.Results: Experiment results indicate that a LOOCV classification accuracy of 81.82% with a sensitivity of 80.00%, and a specificity of 83.33% can be obtained by using the proposed method for the classification AD from NC, and outperforms the most state-of-the-art methods in terms of the classification accuracy. Additionally, the experiment results of the convergence performance further suggest that our proposed scheme has a high rate of convergence. Particularly, the abnormal brain regions and functional connections identified by our proposed framework are highly associated with the underpinning pathological mechanism of the AD, which are consistent with previous studies.Conclusion: These results have demonstrated the effectiveness of the proposed Group- constrained SICE method, and are capable of clinical value to the diagnosis of Alzheimer's disease.

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Semantic Intrusions and Failure to Recover From Semantic Interference in Mild Cognitive Impairment: Relationship to Amyloid and Cortical Thickness

Cited by 16 publications

References 32 publications

The relationship of semantic intrusions to different etiological subtypes of MCI and cognitively healthy older adults

The relationship of semantic intrusions to different etiological subtypes of MCI and cognitively healthy older adults

Types of errors on a semantic interference task in mild cognitive impairment and dementia.

Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification

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