Normal Cognition Research Articles

FNIRS as a biomarker for individuals with subjective memory complaints and MCI.

Identifying individuals at risk of developing dementia is crucial for early intervention. Mild cognitive impairment (MCI) and subjective memory complaints (SMCs) are considered its preceding stages. This study aimed to assess the utility of functional near-infrared spectroscopy (fNIRS) in identifying individuals with MCI and SMC. One hundred fifty-one participants were categorized into normal cognition (NC); amnestic MCI (aMCI); non-amnestic MCI (naMCI); and mild, moderate, and severe SMC groups. Task-related prefrontal hemodynamics were measured using fNIRS during a visual memory span task. Results showed significantly lower oxyhemoglobin (HbO) levels in aMCI, but not in naMCI, compared to the NC. In addition, severe SMC had lower HbO levels than the NC, mild, and moderate SMC. Receiver operating characteristic analysis demonstrated 69.23% and 69.70% accuracy in differentiating aMCI and severe SMC from NC, respectively. FNIRS may serve as a potential non-invasive biomarker for early detection of dementia. Only amnestic mild cognitive impairment (aMCI), but not non-amnestic MCI, showed lower oxyhemoglobin (HbO) than normal individuals. Reduced HbO was observed in those with severe subjective memory complaints (SMCs) compared to normal cognition (NC), mild, and moderate SMCs. Functional near-infrared spectroscopy measures were associated with performance in memory assessments. Prefrontal hemodynamics could distinguish aMCI and severe SMC from NC.

Low testosterone levels relate to poorer cognitive function in women in an APOE-ε4-dependant manner

BackgroundPast research suggests that low testosterone levels relate to poorer cognitive function and higher Alzheimer’s disease (AD) risk; however, these findings are inconsistent and are mostly derived from male samples, despite similar age-related testosterone decline in females. Both animal and human studies demonstrate that testosterone’s effects on brain health may be moderated by apolipoprotein E ε4 allele (APOE-ε4) carrier status, which may explain some previous inconsistencies. We examined how testosterone relates to cognitive function in older women versus men across healthy aging and the AD continuum and the moderating role of APOE-ε4 genotype.MethodsFive hundred and sixty one participants aged 55–90 (155 cognitively normal (CN), 294 mild cognitive impairment (MCI), 112 AD dementia) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who had baseline cognitive and plasma testosterone data, as measured by the Rules Based Medicine Human DiscoveryMAP Panel were included. There were 213 females and 348 males (self-reported sex assigned at birth), and 52% of the overall sample were APOE-ε4 carriers. We tested the relationship of plasma testosterone levels and its interaction with APOE-ε4 status on clinical diagnostic group (CN vs. MCI vs. AD), global, and domain-specific cognitive performance using ANOVAs and linear regression models in sex-stratified samples. Cognitive domains included verbal memory, executive function, processing speed, and language.ResultsWe did not observe a significant difference in testosterone levels between clinical diagnostic groups in either sex, regrardless of APOE-ε4 status. Across clinical diagnostic group, we found a significant testosterone by APOE-ε4 interaction in females, such that lower testosterone levels related to worse global cognition, processing speed, and verbal memory in APOE-ε4 carriers only. We did not find that testosterone, nor its interaction with APOE-ε4, related to cognitive outcomes in males.ConclusionsFindings suggest that low testosterone levels in older female APOE-ε4 carriers across the aging-MCI-AD continuum may have deleterious, domain-specific effects on cognitive performance. Although future studies including additional sex hormones and longitudinal cognitive trajectories are needed, our results highlight the importance of including both sexes and considering APOE-ε4 carrier status when examining testosterone’s role in cognitive health.

Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition.

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

Definition and analysis of gray matter atrophy subtypes in mild cognitive impairment based on data-driven methods.

Mild cognitive impairment (MCI) is an important stage in Alzheimer's disease (AD) research, focusing on early pathogenic factors and mechanisms. Examining MCI patient subtypes and identifying their cognitive and neuropathological patterns as the disease progresses can enhance our understanding of the heterogeneous disease progression in the early stages of AD. However, few studies have thoroughly analyzed the subtypes of MCI, such as the cortical atrophy, and disease development characteristics of each subtype. In this study, 396 individuals with MCI, 228 cognitive normal (CN) participants, and 192 AD patients were selected from ADNI database, and a semi-supervised mixture expert algorithm (MOE) with multiple classification boundaries was constructed to define AD subtypes. Moreover, the subtypes of MCI were obtained by using the multivariate linear boundary mapping of support vector machine (SVM). Then, the gray matter atrophy regions and severity of each MCI subtype were analyzed and the features of each subtype in demography, pathology, cognition, and disease progression were explored combining the longitudinal data collected for 2 years and analyzed important factors that cause conversion of MCI were analyzed. Three MCI subtypes were defined by MOE algorithm, and the three subtypes exhibited their own features in cortical atrophy. Nearly one-third of patients diagnosed with MCI have almost no significant difference in cerebral cortex from the normal aging population, and their conversion rate to AD are the lowest. The subtype characterized by severe atrophy in temporal lobe and frontal lobe have a faster decline rate in many cognitive manifestations than the subtype featured with diffuse atrophy in the whole cortex. APOE ε4 is an important factor that cause the conversion of MCI to AD. It was proved through the data-driven method that MCI collected by ADNI baseline presented different subtype features. The characteristics and disease development trajectories among subtypes can help to improve the prediction of clinical progress in the future and also provide necessary clues to solve the classification accuracy of MCI.

Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection

This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer’s disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.

Correlation between lipoprotein-associated phospholipase A2 and poststroke mild cognitive impairment

ABSTRACT Objectives This study aimed to investigate the correlationbetween serum lipoprotein-associated phospholipase A2 (Lp-PLA2) and poststrokemild cognitive impairment (PSMCI). Methods The patients included in the study were divided into PSMCI (68 cases)and cognitively normal (CN) (218 cases) groups and followed up for six months.Demographic and clinical data were collected. A logistic regression analysiswas performed to determine whether Lp-PLA2 is an independent risk factor forPSMCI. Spearman’s correlation analysis was used to examine the correlationbetween Lp-PLA2 levels and Montreal CognitiveAssessment (MoCA) scores. A receiver operating characteristic (ROC) curveanalysis was performed to determine the diagnostic threshold value of Lp-PLA2for PSMCI. Results Serum Lp-PLA2 levels were significantly higherin the PSMCI group than in the CN group. The logistic regression analysisshowed that Lp-PLA2 was an independent risk factor for PSMCI (OR = 1.05, 95% CI= 1.03–1.07). Spearman’s correlation analysis revealed a significantcorrelation between the Lp-PLA2 levels and MoCA scores (R= -0.49). The area under the ROC curve forLp-PLA2 was 0.849, and the threshold value for PSMCI occurrence was 236.8 ng/ml. Conclusions Elevated serum Lp-PLA2 is an independent riskfactor for PSMCI and may serve as a potential biomarker for PSMCI.

Tau-PET in early cortical Alzheimer brain regions in relation to mild behavioral impairment in older adults with either normal cognition or mild cognitive impairment

Mild Behavioral Impairment (MBI) leverages later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high-risk group for incident dementia. Phosphorylated tau (p-tau) is a hallmark biological manifestation of Alzheimer disease (AD). We investigated associations between MBI and tau accumulation in early-stage AD cortical regions. In 442 Alzheimer’s Disease Neuroimaging Initiative participants with normal cognition or mild cognitive impairment, MBI status was determined alongside corresponding p-tau and Aβ. Two meta-regions of interest were generated to represent Braak I and III neuropathological stages. Multivariable linear regression modelled the association between MBI as independent variable and tau tracer uptake as dependent variable. Among Aβ positive individuals, MBI was associated with tau uptake in Braak I (β=0.45(0.15), p<.01) and Braak III (β=0.24(0.07), p<.01) regions. In Aβ negative individuals, MBI was not associated with tau in the Braak I region (p=.11) with a negative association in Braak III (p=.01). These findings suggest MBI may be a sequela of neurodegeneration, and can be implemented as a cost-effective framework to help improve screening efficiency for AD.

Neurocognitive testing to predict ICANS post-CAR T.

e19003 Background: Since the approvals of chimeric antigen receptor T-cell (CAR T) therapy in acute lymphoblastic leukemia (ALL), non-Hodgkin lymphoma (NHL), and multiple myeloma (MM), it has been increasingly offered in centers nationwide. Immune effector cell-associated neurotoxicity syndrome (ICANS) poses a potentially fatal risk with CAR-T. Identifying patients at risk of having high grade ICANS is a thus a priority. Scoring systems such as the EASIX scores have accounted for disease and comorbidity factors but haven’t addressed neurocognitive function. The St. Louis University Mental Status (SLUMS) Exam and the Montreal Cognitive Assessment (MoCA) have not been examined for validity and reliability in this population, so we performed a retrospective review of CAR T patients at the University of Kansas Medical Center to identify if incidence or severity of ICANS could be correlated with neurocognitive testing performed in the pre-infusion period. Methods: Patients receiving CAR T targeting CD19 or BCMA between December 2017 and December 2023 were included. Neurocognitive testing was performed by trained onco-psychology, neurology, or hematology-oncology staff. The SLUMS exam is an 11 question exam scored on a 30 point scale to predict normal neurocognitive function (27-30 points), mild neurocognitive disorders (21-26 points), and dementia (0-20 points). The MoCA is an 11 question exam scored on a 30 point scale to predict normal cognition (26-30 points), mild cognitive impairment (18-25 points), moderate cognitive impairment (10-17 points), and severe cognitive impairment (&lt;10 points). Incidence of ICANS and severity of ICANS were compared between patients who had any detected cognitive dysfunction and those who had none, as well as between the tiered result system unique to each test. Results: We included 360 patients in this analysis, (257 NHL, 84 MM and 19 ALL) leading to comparisons of 276 CD19-directed CAR T patients and 84 BCMA-directed CAR T patients. In CD19-directed patients, there were no differences in ICANS incidence according to the SLUMS scores. There was a significant increase in ICANS incidence for patients who had a positive MoCA exam, p=0.006. Additionally, ICANS incidence was correlated with more severe MoCA results, p=0.01. No differences in ICANS severity were observed using any neurocognitive test. In BCMA-directed patients, no differences in ICANS were observed using the SLUMS or MoCA. Conclusions: Our results suggest that while the SLUMS exam does not predict ICANS, the MoCA may independently predict incidence of ICANS. Patients with a higher score correlating with normal cognition are less likely to develop ICANS and may be appropriate for outpatient therapy. For those with lower scores correlating with more severe neurocognitive disorders, inpatient monitoring and early intervention will continue to be important.

Brain magnetic resonance imaging image classification for Alzheimer's disease and its hardware acceleration

&lt;span lang="EN-US"&gt;Alzheimer's is a progressive neurodegenerative disorder and is considered the sixth leading cause of death after cancer and heart attack. Early detection and diagnosis provide individuals to go through a wider variety of clinical trials and get multiple medical benefits. Research on the application of deep learning and machine learning to the early detection of Alzheimer's disease has recently gained considerable attention. In this paper, we propose a deep learning classification framework to classify the individual with different progression stages of Alzheimer's disease such as mild cognitive impairment (MCI) and cognitive normal (CN). The dataset from Alzheimer’s disease neuroimaging initiative (ADNI) is considered in this paper which is a multisite having collection of Neuroimaging data for researchers. Structural magnetic resonance imaging (MRI) images are considered from the ADNI data set and feature extraction is done using a 2D discrete wavelet transform. 97% of data reduction is achieved during data pre-processing. The algorithm is trained and validated. The algorithm is accelerated in Nvidia Tx2 graphics processing unit (GPU) to get the better throughput. The result shows our algorithm outperforms the other deep learning algorithms with 91.56% accuracy. &lt;/span&gt;

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

Alzhimer Detection Using CNN

Abstract: Alzheimer's Disease (AD) is a progressive neurodegenerative disease. It is most popular Alzheimer disease symptoms include trouble thinking, making judgment and decision, not able to do familiar task it’s going to also cause alteration in personality and behavior .The factor of AD development is poorly known. As the sickness advances, an individual with Alzheimer disease will develop severe amnesia and lose the capacity to perform everyday activation. This incurable disease is mainly found within elderly people. Neuroimaging technique like MRI and PET scan are used for AD detection. For better result multi model neuroimaging technique are used with DL algorithm for Alzheimer classification process. In this project we are going to use MRI along with PET scan and CNN (convolution neural network) for image classification into normal cognitive (NC).

Age-adjusted CSF t-tau and NfL do not improve diagnostic accuracy for prodromal Alzheimer’s disease

Cerebrospinal fluid total-tau (t-tau) and neurofilament light chain (NfL) are biomarkers of neurodegeneration and are increased in Alzheimer’s disease (AD). In order to adjust for age-related increases in t-tau and NfL, cross-sectional age-adjusted norms were developed based on amyloid negative cognitively normal (CN) adults aged 41–78 years (CN, n = 137). The age-adjusted norms for t-tau and NfL did not improve receiver operating curve based diagnostic accuracies in individuals with mild cognitive impairment (MCI) due to AD (AD-MCI, n = 144). Furthermore, while NfL was correlated with higher age in AD-MCI, no significant correlation was found for t-tau. The cox proportional hazard models, applied in 429 participants with baseline t-tau and NfL, showed higher hazard ratio of progression to MCI or dementia without age-adjustments (HR = 3.39 for t-tau and HR = 3.17 for NfL), as compared to using our norms (HR = 2.29 for t-tau and HR = 1.89 for NfL). Our results indicate that utilizing normative reference data could obscure significant age-related increases in these markers associated with neurodegeneration and AD leading to a potential loss of overall diagnostic accuracy.

Patient and Care Partner Perspective on Potential Undertreatment of Patients With Mild Cognitive Impairment for Cardiovascular Disease.

Mild cognitive impairment (MCI) affects up to 22% of US older adults aged 65 and older. Research suggests that physicians may recommend less cardiovascular disease (CVD) treatment for older adults with MCI due to assumptions about their preferences. To delve into the disparity between patient preferences and physician assumptions in CVD treatment recommendations, we conducted a multi-site qualitative study to explore the underlying reasons for this discrepancy, providing insights into potential communication barriers and strategies to enhance patient-physician relationships. Employing a descriptive qualitative approach, we conducted interviews with 20 dyads, comprising older adults with MCI (n = 11) and normal cognition NC (n = 9), and their respective care partners. During these interviews, participants were prompted to reflect on physicians recommending fewer guideline-concordant CVD treatments to older adults with MCI than those with NC and physicians presuming that older adults with MCI desired less care or treatment in general than those with NC. We identified three primary themes: (1) Most participants had negative reactions to the data that physicians might undertreat patients with MCI for CVD; (2) Participants suggested that physicians may undertreat patients with MCI due to physician assumptions about treatment effectiveness, patient prognosis, value, and treatment adherence, and (3) Participants proposed that physicians may elicit less input from patients with MCI about treatments because of negative physician assumptions about patient decision-making capacity and physician time limitations. This study underscores the pressing need for person-centered communication and involvement of older adults with MCI and their care partners in the decision-making process to ensure that decisions are well-informed, reflecting patients' genuine preferences and values. Addressing these concerns has the potential to substantially enhance the quality of care and treatment outcomes for this vulnerable population, ultimately promoting their overall well-being.

What Are the Reliable Plasma Biomarkers for Mild Cognitive Impairment? A Clinical 4D Proteomics Study and Validation.

At present, Alzheimer's disease (AD) lacks effective treatment means, and early diagnosis and intervention are the keys to treatment. Therefore, for mild cognitive impairment (MCI) and AD patients, blood sample analysis using the 4D nonstandard (label-free) proteomic in-depth quantitative analysis, looking for specific protein marker expression differences, is important. These marker levels change as AD progresses, and the analysis of these biomarkers changes with this method, which has the potential to show the degree of disease progression and can be used for the diagnosis and preventive treatment of MCI and AD. Patients were recruited according to the inclusion and exclusion criteria and divided into three groups according to scale scores. Elderly patients diagnosed with AD were selected as the AD group (n = 9). Patients diagnosed with MCI were classified into the MCI group (n = 10). Cognitively healthy elderly patients were included in the normal cognition control group (n = 10). Patients' blood samples were used for 4D label-free proteomic in-depth quantitative analysis to identify potential blood biomarkers. The sample size of each group was expanded (n = 30), and the selected biomarkers were verified by enzyme-linked immunosorbent assay (ELISA) to verify the accuracy of the proteomic prediction. Six specific blood markers, namely, APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8, were detected by 4D label-free proteomic quantitative analysis. These markers showed a statistically significant upregulation trend in the MCI and AD groups compared with the normal cognition control group (P < 0.05). ELISA results showed that the levels of these six proteins in the MCI group were significantly higher than those in the normal cognition control group, and the levels of these six proteins in the AD group were significantly higher than those in the MCI group (P < 0.05). The plasma levels of APOE, MMP9, UBR5, PLA2G7, STAT5B, and S100A8 in cognitively healthy elderly patients and patients with MCI and AD were significantly different and, more importantly, showed a trend of increasing expression. These results indicate that these six human plasma markers have important diagnostic and therapeutic potential in the identification of cognitive impairment and have value for in-depth research and clinical application.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.

Association of cognitive reserve with transitions across cognitive states and death in older adults: A 15-year follow-up study.

We investigated the association of cognitive reserve (CR) with transitions across cognitive states and death. This population-based cohort study included 2631 participants (age ≥60 years) who were dementia-free at baseline and regularly examined up to 15 years. Data were analyzed using the Markov multistate models. Each 1-point increase in the composite CR score (range: -4.25 to 3.46) was significantly associated with lower risks of transition from normal cognition to cognitive impairment, no dementia (CIND) (multivariable-adjusted hazards ratio=0.78; 95% confidence interval=0.72-0.85) and death (0.85; 0.79-0.93), and from CIND to death (0.82; 0.73-0.91), but not from CIND to normal cognition or dementia. A greater composite CR score was associated with a lower risk of transition from CIND to death in people aged 60-72 but not in those aged ≥ 78 years. CR contributes to cognitive health by delaying cognitive deterioration in the prodromal phase of dementia. We use Markov multistate model to examine the association between cognitive reserve and transitions across cognitive states and death. A great cognitive reserve contributes to cognitive health by delaying cognitive deterioration in the prodromal phase of dementia. A great cognitive reserve is associated with a lower risk of transition from cognitive impairment, no dementia to death in people at the early stage of old age, but not in those at the late stage of old age.

White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis.

Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states,physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured usingan immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aβ42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aβ42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

Insight into the Relationship Between Motor and Cognitive Symptoms in Essential Tremor

Essential tremor (ET) is a heterogeneous disorder characterized by bilateral upper limbs action tremor and, possibly, neurological signs of uncertain significance, including voluntary movement abnormalities and cognitive disturbances, i.e., the so-called ‘soft’ signs configuring the ET-plus definition. While motor and cognitive disturbances often coexist in ET, their interrelationship remains largely unexplored. Here we aim to further investigate the relationship between motor symptoms, objectively assessed through kinematic analysis, and cognitive dysfunctions in ET. Seventy ET patients underwent clinical examination, as well as kinematic recordings of tremor and finger tapping and a thorough cognitive assessment. We then tested clinic-demographic and kinematic differences between patients with and without cognitive abnormalities, i.e., with mild cognitive impairment (MCI). Correlation analysis served to explore potential associations between kinematic and cognitive data. Forty-three ET patients (61.42%) had MCI. ET-MCI patients exhibited reduced movement velocity during finger tapping compared to those with normal cognition (p < 0.001). Lower movement velocity during finger tapping was associated with poorer cognitive performance. Namely, we observed a correlation between movement velocity and performance on the Babcock Story Immediate and Delayed Recall Test (r = 0.52 and r = 0.45, both p < 0.001), as well as the interference memory task at 10 and 30 s (r = 0.3, p = 0.008 and r = 0.2, p = 0.03). In this study, we have provided data for a better pathophysiological interpretation of motor and cognitive signs in ET, including the role played by the cerebellum or extra-cerebellar areas, which possibly underpin both signs.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.